Download Paulina Vaitkienė A STUDY OF TUMOR SUPPRESSOR GENE

LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
MEDICAL ACADEMY
Paulina Vaitkienė
A STUDY OF TUMOR SUPPRESSOR GENE
EXPRESSION AND PROMOTER
METHYLATION FOR THE IDENTIFICATION
OF PROGNOSTIC MARKERS IN
GLIOBLASTOMA
Doctoral Dissertation
Biomedical Sciences,
Biology (01B)
Kaunas, 2013
Dissertation has been prepared at the Medical Academy of Lithuanian
University of Health Sciences during the period of 2005–2013.
Scientific Supervisor
Prof. Dr. Dainius H. Pauža (Lithuanian University of Health Sciences,
Medical Academy, Biomedical Sciences, Biology – 01B)
Consultant
Prof. Dr. Habil. Arimantas Tamašauskas (Lithuanian University of Health
Sciences, Medical Academy, Biomedical Sciences, Medicine – 06B)
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LIETUVOS SVEIKATOS MOKSLŲ UNIVERSITETAS
MEDICINOS AKADEMIJA
Paulina Vaitkienė
GLIOBLASTOMŲ PROGNOZINIŲ
ŽYMENŲ PAIEŠKA TIRIANT NAVIKĄ
SLOPINANČIŲ GENŲ RAIŠKĄ BEI
PROMOTORIŲ METILINIMĄ
Daktaro disertacija
Biomedicinos mokslai,
biologija (01B)
Kaunas, 2013
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Disertacija rengta 2005–2013 metais Lietuvos sveikatos mokslų universiteto
Medicinos akademijoje.
Mokslinis vadovas
prof. dr. Dainius H. Pauža (Lietuvos sveikatos mokslų universitetas,
Medicinos akademija, biomedicinos mokslai, biologija – 01B)
Konsultantas
prof. habil. dr. Arimantas Tamašauskas (Lietuvos sveikatos mokslų
universitetas, Medicinos akademija, biomedicinos mokslai, medicina – 06B)
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CONTENTS
ABBREVIATIONS ....................................................................................... 7
INTRODUCTION ......................................................................................... 9
1.
2.
3.
AIM AND OBJECTIVES OF THE STUDY ................................... 10
ORIGINALITY OF THE STUDY ................................................... 11
REVIEW OF LITERATURE ........................................................... 13
3.1. Glioblastoma epidemiology and morphology ............................... 13
3.2. The concept of DNA methylation.................................................. 17
3.3. DNA methylation in gliomas ......................................................... 21
3.4. DNA methylation in medical research and practice ...................... 23
3.5. Genes selected for more detailed study ......................................... 25
4. MATERIALS AND METHODS ...................................................... 39
4.1. Patient tumor samples and characteristics ..................................... 39
4.2. RNA extraction .............................................................................. 39
4.3. DNA extraction .............................................................................. 41
4.5. cDNA synthesis ............................................................................. 44
4.6. DNA bisulphite modification ........................................................ 46
4.7. Sodium bisulphite sequencing ....................................................... 49
4.8. Methylation-specific PCR ............................................................. 53
4.9. Statistical analysis .......................................................................... 57
5. RESULTS ............................................................................................. 58
5.1. COX7A1, KRT81, AREG, NPTX2, and SPINT1 expression .......... 58
5.2. Bisulfite sequencing of genes promoters ....................................... 60
5.2.1. NTPX2 bisulfite sequencing .................................................... 60
5.2.2. AREG bisulfite sequencing ..................................................... 64
5.2.3. SPINT1 bisulphite sequencing ................................................ 65
5.2.4. COX7A1 bisulfite sequencing ................................................. 67
5.2.5. KRT81 bisulphite sequencing.................................................. 69
5.4. Characteristics of the study population tested for methylation of 11
genes …………………………………………………………………72
5.5. Gene methylation and its association with clinical variables and
survival ................................................................................................. 74
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5.5.1. AREG methylation .................................................................. 74
5.5.2. CASP8 methylation ................................................................. 76
5.5.3. CD81 methylation ................................................................... 78
5.5.4. DcR1 methylation.................................................................... 80
5.5.5. DR4 methylation ..................................................................... 82
5.5.6. GATA4 methylation ................................................................. 84
5.5.7. GATA6 methylation ................................................................. 86
5.5.8. hMLH1 methylation ................................................................ 88
5.5.9. NTPX2 methylation ................................................................. 89
5.5.10. TES methylation .................................................................... 91
5.5.11. TFPI2 methylation ................................................................ 93
5.6. Gene comethylation and its associations with survival ................. 96
DISCUSSION ............................................................................................ 104
CONCLUSIONS ........................................................................................ 113
REFERENCES ........................................................................................... 114
LIST OF PUBLICATIONS ....................................................................... 129
ACKNOWLEDGMENTS.......................................................................... 134
APPENDIX ................................................................................................ 135
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ABBREVIATIONS
AREG
CASP8
CD81
CNS
COX7A1
CpG
ECM
EGFR
GBM
GATA4
GATA6
G-CIMP
H3K4
HIF-1
hMLH1
IDH
KRT81
MBD
MeCP2
amphiregulin
caspase 8
CD81 molecule, TAPA-1
central nervous system
cytochrome c oxidase subunit VIIa polypeptide 1
"p" phosphodiester bond between the cytosine and
guanine nucleotides.
cancer stem cells
tumor necrosis factor receptor superfamily,
member 10c
deoxyribonucleic acid
DNA methyltransferases
tumor necrosis factor receptor superfamily,
member 10a
extracellular matrix
epidermal growth factor receptor
glioblastoma
GATA binding protein 4
GATA binding protein 6
glioma- CpG island methylator phenotype
lysine 4 on histone H3
hypoxic inducible factor 1
MutL homolog 1
isocitrate dehydrogenase enzyme
keratin 81
DNA methyl-binding domain
methyl CpG-binding proteins
MGMT
MMP-2
MMR
MSP
NPTX2
O6-methylguanine DNA methyltransferase
matrix metalloproteinase 2
DNA mismatch repair system
methylation-specific polymerase chain reaction
neuronal pentraxin II
CSC
DcR1
DNA
DNMT
DR4
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PCR
PDGF
RT-PCR
SPINT1
TES
TFPI-2
TRAIL
VEGF
VEGFR
WHO
5hmC
5mC
polymerase chain reaction
platelet-derived growth factor
reverse-transcription polymerase chain reaction
serine peptidase inhibitor
testin
tissue factor pathway inhibitor 2
tumor necrosis factor-related apoptosis-inducing
ligand
vascular endothelial growth factor
vascular endothelial growth factor receptor
World Health Organization
5-hydroxymethylcytosine
5-methylcytosine
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INTRODUCTION
Glioblastoma (GBM) is the most common and aggressive primary brain
tumor in adults. Patients with glioblastoma have a median survival time of
only 12-15 months (1, 2), and prognosis remains extremely poor despite
multimodal treatment by surgery, radiotherapy and, chemotherapy.
Generally, prognostically favorable clinical factors, such as young age and a
good Karnofsky performance score at diagnosis are associated with a better
prognosis. However, various clinical characteristics fail to explain a
variation in disease progression and response to treatment in histologically
diagnosed glioblastomas. There is increasing interest in identifying the
molecular markers that better capture the status of a tumor in order to
improve the existing predictions (3). The identification of methylated genes
in cancer may provide an insight in the molecular mechanisms of tumor
development and might reveal new tools to define the markers of prognostic
significance (4). For example, the epigenetic silencing of the tumor
suppressor gene O6-methylguanine DNA methyltransferase (MGMT), which
encodes a DNA repair enzyme, sensitizes cancer cells to alkylating agents
and is associated with a significantly improved outcome in GBM patients
treated with radiotherapy and temozolomide (5).
Recently, several microarray-based GBM studies have identified the gene
CpG rich promoter-associated sequences that are frequently methylated.
When hypermethylation occurs within the promoter region of a gene, it
could inhibit its expression, contributing to the inactivation of tumor
suppressor genes in cancer and provide the cell with a growth advantage (6).
The list of genes reported as being silenced in gliomas by aberrant promoter
methylation is growing and includes targets involved in glioma development
and progression. Up to now, a lot of genes were identified to be
differentially methylated in a subset of gliomas, enabling the discrimination
of tumors with different biological and clinical properties according to the
methylation pattern (2, 7, 8). However, studies are usually limited because
of a small sample of glioblastomas and scarce clinical data.
In order to identify the molecular markers relevant to the development,
diagnosis, and prognosis of glioblastoma, the analysis of 14 genes (AREG,
CASP8, CD81, COX7A1, DcR1, DR4, GATA4, GATA6, hMLH1, KRT81,
NPTX2, SPINT1, TES, and TFPI-2) was performed. The products of these
genes inhibit cellular migration and metastasis, regulate cell proliferation,
and protect the genome from mutagenesis. The main objective of our study
was to further characterize the role of the latter genes in the pathogenesis of
glioblastoma and to evaluate its clinical importance.
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1. AIM AND OBJECTIVES OF THE STUDY
The aim of this study was to determine aberrant promoter methylation of
tumor suppressor genes in tumor tissue DNA from patients with
glioblastoma multiforme and to evaluate the associations between the
methylation profile of genes, patients’ clinical characteristics, and
prognostic value.
The objectives of the study were as follows:
1. To investigate the expression of 5 genes (AREG, COX7A1, KRT81,
NPTX2, and SPINT1) in 22 different glioblastoma samples and to
compare with the expression of these genes in control brain tissue
samples;
2. To determine if the differences in the expression of AREG, COX7A1,
KRT81, NPTX2, and SPINT1 genes are associated with the promoter
methylation of these genes;
3. To determine the methylation frequency of 11 genes (AREG,
CASP8, CD81, DcR1, DR4, GATA4, GATA6, hMLH1, NPTX2, TES,
and TFPI2), involved in cell motility, growth, adhesion, apoptosis,
and repair processes, in 100 glioblastoma tissue samples;
4. To evaluate the associations between AREG, CASP8, CD81, DcR1,
DR4, GATA4, GATA6, hMLH1, NPTX2, TES, and TFPI2 gene
promoter methylation and clinical characteristics of patients with
glioblastoma.
5. To determine the relationship between AREG, CASP8, CD81, DcR1,
DR4, GATA4, GATA6, hMLH1, NPTX2, TES, and TFPI2 gene
promoter methylation and patients’ survival after surgery;
6. To identify epigenetic markers and their combinations that could be
significant in the prognosis of patients with glioblastoma.
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2. ORIGINALITY OF THE STUDY
Glioblastoma is one of the most aggressive tumors with a poor prognosis.
The epigenetic events that regulate gene expression have clearly emerged as
a fundamental mechanism in developmental biology and in the pathogenesis
of cancer. For example, multiple genes that affect numerous cellular
pathways are silenced by hypermethylation in cancer, and studies of these
genes have increased our understanding of how cancer develops and
progresses.
In 2007, Mueller et al. succeeded in unveiling novel candidate genes,
which are epigenetically regulated in glioblastomas, by using the
pharmacologic manipulation of glioma cells with the demethylating agent
5´-aza-dC combined with genome wide expression profiling (7). To confirm
the importance of these genes in gliomagenesis, in this study, a more
detailed analysis of COX7A1, SPINT1, AREG, NPTX2, and KRT81 gene
expression and their promoter methylation in glioblastoma samples was
performed for the first time. Genes were evaluated by using the following
methods: reverse-transcription polymerase chain reaction (RT-PCR),
bisulfite sequencing, and methylation-specific PCR (MSP). Potential
epigenetic markers in glioblastoma – AREG and NPTX2 – the expression of
which was found to be associated with promoter methylation have been
identified.
Moreover, in this study, the methylation frequency of 11 genes (AREG,
CASP8, CD81, DcR1, DR4, GATA4, GATA6, hMLH1, NPTX2, TES, and
TFPI2), involved in cell motility, growth, adhesion, apoptosis, and repair
processes, was determined in 100 glioblastoma tissue samples. The data on
the frequency of methylation of these genes in glioblastoma are scarce to
evaluate the usability and suitability of new epigenetic markers in molecular
diagnosis of these tumors and prognosis of the disease.
To our knowledge, the methylation frequencies of the AREG, DcR1,
GATA4, NPTX2, and TFPI2 gene promoters in glioblastoma have not been
reported in literature; therefore, we believe that our study was the first to
determine the methylation frequencies and significantly contributed to the
existing knowledge on epigenetic markers in glioblastoma and the process
of gliomagenesis.
Our study differs from other published studies by a sample size, analysis
of clinical data, and identification of new methylation markers and their
combinations that could be used in the diagnosis and prognosis of
glioblastoma.
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To our knowledge, this study is the first to report the significant
associations between the methylation of the AREG, CASP8, GATA6, and
TFPI2 gene promoters and patients’ survival after surgery. After a detailed
investigation of these genes and confirmation of the data in larger scale
studies, they could be used for the molecular typing of glioblastomas and
more accurate diagnosis to tailor the treatment strategies and prognosticate
the course of the disease.
Moreover, this study is one of the first studies where not only the impact
of the methylation of single gene promoters on survival has been studied,
but also associations between the comethylation of several genes and
patients’ clinical characteristics have been determined. With the help of
comprehensive statistical analysis, a unique set of 6 genes (AREG, CASP8,
DR4, GATA4, GATA6, and TFPI2) has been identified; the methylation
studies of the promoters of this gene combination would help reliably
predict survival as compared with the analysis of individual genes.
The importance of this combination of genes in the diagnosis and
prognosis of disease course in glioblastoma needs to be evaluated and
validated in more detailed studies with a larger cohort of patients with
glioblastoma.
In this study, the fundamental investigations were linked to clinical data.
The study conclusions are undoubtedly useful for clinical practice, and the
obtained results provide additional information for the future planning of
new clinical trials.
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3. REVIEW OF LITERATURE
3.1. Glioblastoma epidemiology and morphology
Central nervous system (CNS) tumors in Europe have an incidence of
approximately 6-7 per 100 000 persons per year and account for 2% of all
cancer-related deaths (9). The diagnosis and grading of primary brain
tumors follows the World Health Organization (WHO) classification (10).
The WHO classification indicates malignant potential, response to
treatment, and survival. Gliomas are tumors that are thought to arise from
glial progenitor and glial cells. These tumors account for 32% of all primary
brain tumors and, and 80% of all malignant primary brain tumors diagnosed
in the United States (11). The overall annual incidence of gliomas varies
from 5 to 6 cases per 100 000 worldwide (12). Glial tumors are categorized
into 4 grades. Grade I and II low-grade astrocytomas are slow-growing, less
aggressive tumors, while grade III and IV high-grade gliomas are malignant
tumors, characterized by a high proliferation rate (grade III) and the
presence of necrotic tissue and/or angiogenic activity (grade IV) (13).
Glioblastoma is also known as grade IV astrocytoma or glioblastoma
multiforme. The term multiforme refers to the vast intratumoral
heterogeneity seen in the disease. The histopathology of glioblastoma shows
brisk mitotic activity, microvascular proliferations, thrombosis, and necrosis
(14). Glioblastoma multiforme accounts for up to 60% of all malignant
primary brain tumors in adults, occurring in 2-3 cases per 100 000 in Europe
and North America (15). It is one of the most devastating and lethal forms
of human cancer. GBM presents some of the greatest challenges in the
management of cancer patients worldwide, despite notable recent
achievements in oncology. Even with aggressive surgical resections using
state-of-the-art preoperative and intraoperative neuroimaging, along with
recent advances in radiotherapy and chemotherapy, the prognosis for GBM
patients remains dismal: survival time after diagnosis is about 1 year (16).
Only 3%–5% of patients survive longer than 5 years (17). Nevertheless,
residual tumor causes relapse in almost all cases. GBM most frequently
recurs after a median survival time of 32 to 36 weeks (18). Although GBM
can manifest itself at any age, it preferentially occurs in adults, with a peak
age of incidence between 45 and 70 years (19). This type of cancer is more
common in men than women. It most frequently involves the brain
hemispheres, but it can also affect basal ganglia and the brain stem (19). The
etiology of malignant glioma is unknown. Therapeutic ionizing irradiation
has been confirmed as a sole unequivocal risk factor for the development of
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glioblastoma. No association between exposure to electromagnetic fields
and development of brain tumors has been established so far. Further, no
definite association between viral infection or diet (e.g., intake of N-nitroso
compounds) and malignant glioma has been proven as well (14).
Invasion
An additional important feature of malignant glioma include its great
tendency to infiltrate into normal brain tissue, rendering the condition
incurable by surgery alone (19). Migrating glioma cells are relatively
resistant to cytotoxic therapy (chemotherapy, irradiation) because the
processes of metabolism are reduced as compared to nonmigrating tumor
cells. The invasion of glioma cells requires interaction with the extracellular
matrix (ECM) and with surrounding cells of the healthy brain tissue. The
brain ECM is mainly composed of soft components such as
glycosaminoglycans, proteoglycans, tenascin-C, and thrombospondin (14).
Rigid components like collagen, fibronectin, or laminin are limited to
perivascular and vascular areas in the brain. Migrating glioma cells may
degrade the soft components of ECM by proteases, namely the matrix
metalloproteinases, serine proteases, and cysteine proteases (14). The outer
GBM border usually observed in T1-weighted gadolinium magnetic
resonance imaging does not delineate the true dimension of the tumor.
Moreover, tumor cells diffusely invade the normal brain and may be
detected beyond that border (19).
Hypoxia and angiogenesis
Vascular proliferation and tissue necrosis are characteristic features of
malignant gliomas, particularly of glioblastoma. These features are most
likely the consequence of rapidly increasing tumor mass that is inadequately
oxygenized by the preexisting vasculature (14). Hypoxia-related genes are
significantly associated with necrosis in human glioblastoma specimens,
which suggests that there is a possible link between glioblastoma necrosis
and hypoxia. Hypoxic cells are known to overexpress hypoxic inducible
factor 1 (HIF-1), vascular endothelial growth factor (VEGF), and matrix
metalloproteinase 2 (MMP-2) (20). HIF-1 is translocated to the nucleus and
induces the transcription of target genes involved in angiogenesis,
migration, proliferation, and cell survival. The downstream effectors of
HIF-1 include VEGF and vascular endothelial growth factor receptor
(VEGFR). VEGF is a major angiogenic growth factor. It binds to and
activates the tyrosine kinase receptors of the VEGFR family. Activated
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tyrosine kinase activates downstream pathways that induce proliferation and
migration of vascular cells (14). Considering the morphology of
glioblastoma necrosis, these factors could cause tumor cell migration to
form pseudopalisades and microvascular hyperplasia in perinecrotic areas,
which are associated with poor prognosis in cancer (20). Tumor cells upregulate hypoxia-associated factors that induce angiogenesis. In malignant
gliomas, newly formed vessels do not show a normal phenotype, but appear
as bizarre vascular proliferates. It is assumed that these bizarre vascular
proliferates are not suitable to sufficiently oxygenize the tumor tissue,
resulting in a vicious circle of hypoxia and aberrant angiogenesis (14).
Glioblastoma cancer stem cell hypothesis
Traditionally, it was thought that the adult human brain does not contain
precursor cells, and it was assumed that brain tumors derive from mature
parenchymal cells (14). An increasing body of evidence has strongly
supported the concept that a subpopulation of cancer cells in each tumor has
a greater potential of cancer initiation and repopulation. These cells are
known as cancer stem cells (CSCs) or tumor-initiating or propagating cells
because they share some critical characteristics with normal stem cells (21).
CSCs are defined as tumor cells with stem cell properties, i.e., asymmetric
cell division, infinite growth, and multipotency, that later differentiate into
rapidly proliferating progenitor-like and more differentiated cells, defining
the histopathology of the tumor (22). The CSC hypothesis also suggests that
resistance to chemo- and radiotherapy is driven by CSCs and thus
challenges the present diagnostic and therapeutic strategies (22). It has been
suggested that cancer stem cells are the main proliferating cell population in
malignant gliomas responsible for tumor growth and progression (14). GBM
stem cells may be resulted from genetic and epigenetic changes in neural
stem/progenitor cells or the differentiated cells such as astrocytes after a
series of mutations or epigenetic reprogram. Although the origin of GBM
stem cells is not clearly defined, they share similar properties with normal
neural stem cells that endow these cells with key traits in carcinogenesis
(21).
Pathogenesis of glioblastomas
Gliomas, like all human tumors, arise by a step-wise accumulation of
genetic events that inactivate tumor suppressor genes and activate
oncogenes (23). Glioblastoma may develop from diffuse low-grade or
anaplastic astrocytomas (secondary glioblastoma), but more frequently, they
15
manifest de novo, without a less malignant precursor lesion (primary
glioblastoma) (14). Primary and secondary GBMs are histologically
indistinguishable, but at the molecular level, each type demonstrates distinct
patterns of genetic alterations and these have long been thought to represent
different routes of molecular pathogenesis to a common histopathologic end
point (24). Two genetic alterations have been detected to occur at a
significant rate in primary GBM: amplification or activating mutation of
epidermal growth factor receptor (EGFR) and loss of chromosome 10 (23).
The activation of growth factor receptors – EGFR, alpha-type plateletderived growth factor receptor, or MET – by mutation and amplification
inhibits the functions of tumor suppressors such as p53 and Rb (24). The
tumor suppressor gene PTEN (phosphatase and tensin homolog) is mutated
in the 10q23 region in approximately one-third of primary GBMs (19).
Mutations in this gene have been described only in malignant gliomas and
are rarely associated with p53 mutations (19). Other frequent mutations in
primary GBM affect the CDK cell-cycle-regulator genes CDK4/6 and
CDKN2A/CDKN2B (19). In summary, primary glioblastoma typically
arises with a complement of mutations that, one way or another, serves to
activate progrowth pathways and inactivate or suppress tumor suppressors
p53 and Rb. Because primary GBMs arise de novo, there is little
information on the sequence of events during pathogenesis (24).
Other GBMs arise by the step-wise progression through a series of lower
grade lesions (low-grade astrocytoma, anaplastic astrocytoma) and are
called secondary GBMs (23). A number of alteration have been noted in
lower grade lesions, including the loss of the tumor suppressors p53, p15,
p16, and Rb and overexpression of the platelet-derived growth factor
(PDGF) (23). Somatic mutations of the isocitrate dehydrogenase enzymes
(IDH1 and IDH2) appears to play a critical and early role of these gliomas
(24). The recent finding of mutations in IDH genes in 70%-80% of grade II
and III astrocytomas, as well as some glioblastomas, has provided a
unifying mechanism for early gliomagenesis. As IDH enzymes are typically
associated with the Krebs cycle, they play a multitude of other roles in
normal cellular biology, such as contributing to cellular defenses against
oxidative damage and, potentially, regulating epigenetic modifiers (25). The
incidence of IDH mutation in primary GBM is low (~5%), reinforcing the
notation of a distinct pathogenesis associated with tumors arising in this
clinical scenario (24).
Despite the genetic differences, no differences in sensitivity to
conventional chemotherapy between primary and secondary glioblastoma
multiforme have been reported. The molecular and genetic aberrations in
16
There are not many molecular factors that contribute to long-term
survival. Among the molecular markers, TP53 mutation, infrequent
amplification of the EGFR gene, combined LOH 1p/19q, loss of
chromosome arm 19q, and low rate of tumor proliferation have been
associated with long-term survival in GBM (26, 27). MGMT promoter
methylation is seen in ~50% of glioblastomas and has been linked to
prolonged progression-free and overall survival (1). PTEN protein
expression was a favorable factor for long-term survival (27). In addition,
mutations in the IDH1 and IDH2 genes have recently been reported in a
subset of glioblastomas from younger patients and linked to a more
favorable survival outcome (29). So far, the molecular and cellular
mechanisms of long-term survival in glioblastoma patients are not known.
3.2. The concept of DNA methylation
Glioblastomas arise by a step-wise accumulation of the events that
inactivate tumor suppressor genes and activate oncogenes. These can occur
by both epigenetic regulatory mechanisms and structural genomic
alterations such as overexpression and amplification activating oncogenes,
and transcriptional silencing and genomic deletion inactivating tumor
suppressor genes (23).
Epigenetics has emerged as an important field in biomedical research in
the last decade, describing the novel mechanisms of normal biological
processes as well as disease pathogenesis. Epigenetics can be defined as the
study of heritable changes of a phenotype such as the gene expression
patterns of a specific cell type that are not caused by changes in the
nucleotide sequence of genetic code itself (30).
Epigenetic regulatory mechanisms are as follows:
1. Posttranslational histone modification;
2. ATP-dependent chromatin remodeling complexes;
3. Polycomb/trithorax protein complexes;
4. Small and other noncoding RNA (siRNA and miRNA);
5. DNA methylation.
These mechanisms are discrete, but interact closely in order to regulate
gene expression.
Many of the changes, particularly those resulting from DNA methylation
and histone deacetylation, affect gene expression and genome stability
through the inappropriate regulation of a local chromatin structure.
Furthermore, recent data suggest that epigenetic changes are involved in the
earliest phases of tumorigenesis, and that they may predispose stem
(progenitor) cells to subsequent genetic and epigenetic changes that are
17
involved in tumor promotion. Given the observed frequency of DNA
methylation changes in tumorigenesis and the inherent stability of these
molecular abnormalities, these events may provide ideal biomarkers for
molecular diagnostics and early detection of cancer (19).
DNA methylation is currently the most widely studied form of epigenetic
programming. This postreplication modification is almost exclusively found
on the 5 position of the pyrimidine ring of cytosines in the context of the
dinucleotide sequence CpG (31) (Figure 3.2.1).
Figure 3.2.1. Mechanism of CpG methylation
(A) CpG islands consist of nucleotide bases where cytosine (C) residues are followed by
guanine (G) in a genomic DNA sequence. (B) Methylation of CpG islands is catalyzed by
DNA methyltransferases (DNMTs), which transfer the methyl group from S-adenosylmethionine (SAM-CH3) to cytosine. The methylation reaction yields S-adenosyl
homocysteine (SAH) and 5-methylcytosine (5mC) (32).
In the term “CpG sites,” “p” represents the phosphodiester bond between
the cytosine and guanine nucleotides. The methylation of CpG rich clusters,
called CpG islands, is important because they are found in the promoter
region and first exons of many genes. CpG islands are regions of ~500 bp to
1 kbp where CpG nucleotides are about 5 times more abundant compared
with the rest of the genome (33). Although CpG islands account for
approximately 1% of the total human genome, they are present in >50% of
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human gene promoters indicating their functional importance in
transcriptional control.
DNA methylation is mediated by 1 of 3 DNA methyltransferases
(DNMTs), which catalyze the addition of a methyl group from a universal
methyl donor, S-adenosyl-L-methionine, to the 5-carbon position of a
cytosine (31). DNMT1 is considered to be primarily responsible for the
maintenance of this epigenetic modification and copies the pre-existing
methylation pattern onto the daughter strand after DNA replication.
DNMT3A and DNMT3B are de novo methyltransferases and can also
complete the methylation process and correct the errors (31).
CpG islands are variable methylated as a mechanism to regulate gene
transcription. They are mostly nonmethylated associated with the
maintenance of an open chromatin structure and a potentially active state of
transcription (30). Methylation above normal levels is referred to as
hypermethylation. The hypermethylation of CpG islands in a gene promoter
blocks the ability of transcription factors to interact with the promoter and
inhibits gene expression (32) (Figure 3.2.2.).
Several mechanisms have been proposed to be associated with
transcriptional repression by DNA methylation. The first mechanism
involves direct interference with the binding of specific transcription factors
to their recognition sites in their respective promoters. Several transcription
factors, including AP-2, c-Myc/Myn, the cyclic AMP-dependent activator
CREB, E2F, and NFκB, recognize sequences that contain CpG residues, and
binding of each has been shown to be inhibited by methylation (35).
The second mode of repression involves a direct binding of specific
transcriptional repressors to methylated DNA (35). In addition to the
proteins that create and maintain the methylation pattern, there are proteins
that recognize methylated DNA and interact with it. DNA methyl-binding
domain (MBD1-4) proteins or methyl CpG-binding proteins (MeCP2)
recognize and bind to methylated DNA. They recruit transcriptional
corepressors such as histone-deacetylating complexes, polycomb proteins,
chromatin-remodeling complexes and attract chromodomain-binding
proteins (30).
Methylated DNA can also be bound by some zinc finger proteins such as
Kaiso and more recently discovered ZBTB4 and ZBTB38 proteins that are
also able to repress transcription in a methylation-dependent manner (30).
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Figure 3.2.2. Epigenetic patterns in normal and cancer cells
(A) DNA methylation. In normal cells, nearly all of the CpG dinucleotides are methylated
whereas CpG islands, mostly residing in 5′ regulatory regions of genes, are unmethylated.
In cancer cells, many CpG islands become hypermethylated, in conjunction with silencing
of their cognate genes, while global hypomethylation, mostly at repetitive elements, occurs.
(B) Chromatin and histone modification. Active genes are associated with acetylation of
histone tails, methylation of lysine 4 on histone H3 (H3K4), and nucleosome depletion at
their promoters. The promoters of silenced genes (drawn here in conjunction with DNA
hypermethylation) become associated with nucleosomes, lose acetylation and H3K4
methylation marks, and gain repressive methylation marks such as lysine 9 or 27 on histone
H3, which recruit repressive complexes. Methylated DNA binding proteins link methylated
DNA with the histone modification and nucleosome remodeling machineries (not shown)
(34).
DNA methylation can also affect histone modifications and chromatin
structure, which, in turn, can alter gene expression. DNA methylation and
histone modifications function in a close interplay with nucleosome
remodeling and positioning complexes that bind specifically modified
histones and methyl CpG-binding proteins, and move nucleosomes on DNA
by ATP-dependent mechanisms (34). Nonmethylated CpG island promoters
are usually hypersensitive to nucleases and are relatively depleted of
nucleosomes, whereas methylated promoters have nucleosomes on them and
are nuclease resistant (34).
20
DNA methylation is possibly a reversible step, and there are indications
that under some conditions, the methylated DNA pattern is a balance
between active demethylation and remethylation. Demethylation in the
absence of DNA replication normally occurs in the zygote and in the
development of germ cells, but it also is active in malignant cells. The
molecular mechanisms for demethylation remain unclear, though recent
studies suggest that DNMT3A and 3B may be involved (31).
In many cases, methylated and silenced genes can be reactivated using
DNA methylation inhibitors such as 5-azacytidine. However, it should be
noted that an unmethylated state of a CpG island does not necessarily
correlate with the transcriptional activity of the gene, but rather that the gene
can be potentially activated. On the other hand, the presence of methylation
alone does not necessarily induce silencing of nearby genes. Only when a
specific core region of the promoter that is often, but not necessarily,
spanning the transcription start site becomes hypermethylated, the
expression of the associated gene is modified (30).
3.3. DNA methylation in gliomas
In glioblastomas, the changes in methylation patterns can be seen, which
are characterized by a genome-wide loss of methylation combined with
locus-specific hypermethylation. Hypomethylation may promote tumor
growth by the activation of oncogenes, loss of imprinting, or promotion of
genomic instability.
CpG island promoter methylation occurs in genes with diverse functions
related to tumorigenesis and tumor progression, including cell-cycle
regulation, DNA repair, apoptosis, angiogenesis, invasion, and drug
resistance. CpG hypermethylation in gliomas may also occur in genes that
are not expressed in the brain, suggesting not all events of CpG island
methylation are functionally important for tumorigenesis (33).
Several epigenetic alterations have been described in glioblastoma.
Among them, the methylation of MGMT has been the most studied. MGMT
encodes a DNA repair protein that removes alkyl adducts at the O6 position
of a guanine and protects normal cells from carcinogens. Unfortunately, it
can also protect tumor cells from chemotherapeutic alkylating agents such
as temozolomide. von Deimling et al. in their article summarized that GBM
patients with a hypermethylated MGMT promoter demonstrated the survival
rates of 49% and 14% at 2 and 5 years, respectively, when treated with
concomitant and adjuvant temozolomide and radiotherapy. In contrast, the
estimated 2- and 5-year survival rates were only 24% and 5%, respectively,
21
in similar patients that were initially treated with radiotherapy alone. GBM
patients whose tumors lacked MGMT hypermethylation demonstrated the 2and 5-year survival rates of 15% and 8%, when they received combined
radiochemotherapy, and the rates decreased to 2% and 0% if radiotherapy
was administered alone (36).
There are data that different-grade gliomas show differences in the
methylation status of various genes (37-39). In this context, the methylation
of the DNA repair gene MGMT is more frequently observed in secondary
GBMs (about 75%) than primary GBMs (36%) or low-grade astrocytomas
(48%) (19). Secondary GBMs have a higher overall frequency of promoter
methylation compared to primary GBMs at least for the promoters of p14,
p16, RB1, MGMT, and TIMP-3. Low-grade gliomas and secondary GBMs
show PTEN promoter methylation (33). The progression of glioma over
time is associated with distinct epigenetic patterns. Malignant progression
and shorter survival in astrocytoma are associated with p14, but not MGMT
hypermethylation, suggesting that these are 2 distinct pathways in
astrocytoma with different clinical consequences (33). The association
between methylation levels and WHO grading may directly suggest that
hypermethylation reflects a potential of tumor aggressiveness. On the other
hand, there are data that methylation profiles are remarkably stable across
glioma evolution, even during anaplastic transformations, suggesting that
epigenetic alterations occur early during gliomagenesis (6).
There are some data that the analysis of the concomitant
hypermethylation of several genes allows better prediction of survival than
that of one gene (9).
Noushmer et al. reported that gliomas could be stratified by the glioma
CpG island methylator phenotype (G-CIMP) status into 2 distinct subgroups
with different molecular and clinical phenotypes. These molecular
classifications have implications for differential therapeutic strategies for
glioma patients (8).
Genes involved in invasion and metastasis can also be affected by
promoter hypermethylation in gliomas. The CpG promoter
hypermethylation of the protocadherin-gamma subfamily is frequently
observed in astrocytomas, glioblastomas, and glioma cell lines (33). The
significance of CpG island promoter hypermethylation in GBM
pathogenesis is emphasized by the observation of epigenetically mediated
inactivation of a wide variety of genes associated with tumor suppression
(RB1, VHL, EMP3), cell cycle regulation (p16, p15), DNA repair (MGMT,
hMLH1), tumor invasion, and apoptosis (19).
22
3.4. DNA methylation in medical research and practice
With a growing body of knowledge about the pathogenesis of
glioblastoma, there is hope for the development of novel therapeutic agents
that may effectively treat patients. DNA methylation is potentially
preventable or possibly reversible. For example, blocking DNA methylation
by inhibiting DNMTs results in the demethylation of CpG islands in
daughter cells, with subsequent restored expression of tumor suppressor
genes and abrogation of tumor growth (40).
Successful conventional chemotherapy depends on the activation of
proapoptotic genes that respond to cytotoxic agents leading to cell death.
DNA methylation of these proapoptotic genes can block cell death from
occurring leading to chemotherapeutic resistance, thus the reactivation of
epigenetically silenced apoptotic genes can increase the efficacy of
chemotherapy (41). Many compounds have been discovered to target
proteins that control DNA methylation. Some of them are already being
used clinically. The most investigated DNA methylation inhibitors are as
follows (41):
1.
5-Azacytidine (5-Aza-CR; Vidaza) is a nucleoside analog that
incorporates into RNA and DNA and is FDA approved to treat high-risk
myelodysplastic syndromes (MDS) patients; successful clinical results
have recently been reported.
2.
5-Aza-2 deoxycytidine (5-Aza-CdR; Decitabine) is the deoxy
derivative of 5-Aza-CR and is incorporated only into DNA.
3.
Zebularine, a cytidine analog that acts similarly to 5-Aza-CR,
but has lower toxicity, increased stability and specificity.
4.
S110, a decitabine derivative that has increased stability and
activity, which has shown promise in preclinical studies.
Two clinically used compounds with DNA demethylating activities –
azacitidine (5-azaCR, Vidaza; Celgene, Summit, NJ, USA) and decitabine
(5-aza-2′-deoxycytidine, Dacogen; SuperGen, Dublin, CA, USA) – are
structurally similar to cytosine nucleosides (42). These inhibitors are
incorporated into DNA and trap the DNMT leading to a covalent proteinDNA adduct, depletion of DNMTs, and subsequent demethylation of
genomic DNA during replication. Although proven to be potentially
effective as antitumor agents in laboratory experiments and in clinical trials,
both inhibitors are unstable in solution and can be toxic (43). DNA
methylation inhibitors act during S-phase of the cell cycle, thus they
preferentially affect rapidly growing cells. In addition to inhibiting DNA
methyltransferase activity, azanucleosides also act through unspecific
mechanisms, which likely contribute to their clinical effectiveness (41).
23
Administration of a combination of DNMT and HDAC could have a
synergistic effect on gene activation and could allow lower doses of each
drug to be used (33).
Zebularine is a proven nucleoside DNMT inhibitor effective at
reactivating the silenced genes in vitro and in vivo with a high specificity
for cancer cells relative to normal cells. It was demonstrated that Zebularine
selectively sensitizes the brain tumor cells that are deficient in DNAdependent protein kinase. The sensitization of cell killing by Zebularine is
mediated by a combination of DNA repair and cell cycle checkpoint defects
in DNA-dependent protein kinase-deficient glioblastoma cells (43).
The methylation-induced silencing of cancer-testis antigens, such as NYESO-1, can protect cancer cells from being recognized by T cells. Treating
cancer cells with demethylating agents can induce the expression of these
antigens, allowing a reaction by engineered lymphocytes suggesting that
epigenetic therapy can be combined with immunotherapy for better results
(41).
Currently available DNA methylation inhibitors lead to overall DNA
methylation inhibition. Global hypomethylation may lead to the activation
of oncogenes and/or increased genomic instability. Recently, DNA
methylation inhibitors that targeted specific genes or the groups of genes are
being developed. Another way is to identify and use demethylases.
DNA methylation is involved in key cellular processes important to
tissue-specific gene expression, cell differentiation, and development. DNA
methylation patterns are frequently perturbed in human diseases such as
imprinting disorders and cancer (44). It is largely unknown about the
mechanisms that control methylation levels and prevent from DNA
methylation at CpG islands. After fertilization and at certain points of the
embryonic development, a large number of the methylation markers are
erased (45). Importantly, despite an intense search, no direct DNA
demethylase has been identified yet (44). Until recently, the only known
epigenetic marker of DNA itself was 5mC, which is converted by DNA
methyltransferases preferentially at CpG dinucleotides (46). 5Hydroxymethylcytosine (5hmC), which is considered to be the sixth base of
the genome of higher organisms, has recently been identified (45). The
recent discovery that the 3 members of the TET protein family can convert
5mC into 5hmC has provided a potential mechanism leading to DNA
demethylation (44).
24
Figure 3.4.1. Demethylation model
The canonical DNA nucleosides dG, dA, dT, and dC. Cytosine can be modified to mC and
hmC in mammalian tissues. hmC could be further oxidized to the putative demethylation
intermediates fC and caC, but have never been found in vivo (45).
Despite a lot of promising epigenetic data, they have not been used in
routine diagnostic practice yet. The hypermethylation of gene promoters
may be considered the tip of iceberg of deregulation of complex epigenetic
mechanisms that contribute to GBM onset (19). Whole genome,
transcriptome, and methylome sequencing is currently at the horizon of
clinical research and could be performed even in small-sized tumor tissue
samples (47). Unraveling the epigenetic alterations opens up possibilities for
discovering new biomarkers for cancer detection and prognosis (19).
Restoring epigenetically altered pathways will probably lead to the
development of new therapeutic tools in GBM.
Blood plasma in cancer patients contains DNA derived from cells due to
necrotic or apoptotic cancer cells releasing genomic DNA. This potentially
provides a less invasive method for the detection of biomarkers. Aberrantly
methylated cancer genes found in plasma could be one such type of
biomarkers (33).
3.5. Genes selected for more detailed study
The cytosine methylation of CpG dinucleotides in gene promoters is a
common cause of DNA silencing and transcriptional repression that can
25
modulate the clinical characteristics of glioblastoma. The best known is
MGMT promoter methylation determining tumor response to DNA
alkylating agents and being an independent prognostic factor for patients’
survival (48). However, several widely described genes can be seen as only
a partial picture of the methylation changes, and there may be many more
genes that need to be clarified. Recent methylome studies of brain tumors
have disclosed a list of new epigenetically modified genes associated with
gliomagenesis and different glioma clusters (2, 6, 8). The latter studies
illustrate a glioblastoma profile being constructed via methylation of a
multiple set of genes, forming networks attributed to different pathogenesis
pathways. It has been suggested that genes, such as AREG, CASP8, CD81,
DcR1, DR4, GATA4, GATA6, hMLH1, NPTX2, TES, and TFPI2 could be
important in gliomagenesis.
Table 3.5.1. Functions and reported methylation frequency of the selected
genes in glioblastoma
Gene
Location
Functions of encoded proteins
Autocrine growth factor and
mitogen for astrocytes, Schwann
cells, and fibroblasts
Responsible for the TNFRSF6/FAS
2q33
mediated and TNFRSF1A induced
CASP8
apoptosis
Encoded protein is a cell surface
11p15.5
glycoprotein that is known to form
CD81
a complex with integrins
May protect cells against TRAIL
mediated apoptosis by competing
8p22
DcR1
with TRAIL-R1 and R2 for binding
to the ligand
Receptor for the cytotoxic ligand
8p21
DR4
TNFSF10/TRAIL
8p23.1
Transcriptional activator
GATA4
18q11.1
Transcriptional activator
GATA6
Modulates postreplicative DNA
3p21.3
hMLH1
mismatch repair system
7q21.3
Modulates long-term cell plasticity
NPTX2
May play a role in cell adhesion,
cell spreading and in the
7q31.2
TES
reorganization of the actin
cytoskeleton
May play a role in the regulation of
7q22
plasmin-mediated matrix
TFPI2
remodeling
ND, no data on DNA methylation in glioblastomas.
AREG
4q13
26
GBM methylation in %,
(literature source)
ND
10-30 (49, 50)
54 (2, 6)
ND
25-70 (2, 26, 49)
ND
30-48 (2, 50-52)
0-15 (53, 54)
ND
58-69 (2, 7)
ND
AREG
The AREG (amphiregulin, SDGF, schwannoma-derived growth factor)
gene is located on chromosome 4q13.3. AREG is a member of the
epidermal growth factor family. It is a bifunctional growth modulator: it
interacts with the EGF/TGF receptor to promote the growth of keratinocytes
and normal lung epithelial cells in the presence of extracellular matrix, but
inhibits the growth of certain aggressive carcinoma cell lines, some
transformed cell lines, and lung epithelial cells in the absence of
extracellular matrix (55, 56). Thus, AREG can either stimulate or inhibit the
growth of lung cancer cells, depending on the biological settings (56).
Various studies have highlighted the functional role of AREG in several
aspects of tumorigenesis, including self-sufficiency in generating growth
signals, limitless replicative potential, tissue invasion and metastasis,
angiogenesis, and resistance to apoptosis (57). On the other hand, there is an
growing body of data about AREG promoter methylation in various types of
tumors: gastric (58), high-grade serous carcinomas (59), and human bladder
tumor cell line (T24) (60). AREG is differentially methylated in low-grade
gliomas (WHO grade II) in comparison with controls (6). Microarray
analysis coupled with the pharmacologic demethylation of cultured tumor
cells is a useful means for investigating genome-wide epigenetic events and
has been successfully used for this purpose in glioblastoma; one of the novel
candidate genes identified by this approach is AREG (7).
CASP8
The CASP8 (caspase 8, apoptosis-related cysteine peptidase 8) gene is
located on chromosome 2q33.1. This gene encodes a member of the
cysteine-aspartic acid protease (caspase) family. The sequential activation of
caspase 8 plays a central role in the execution phase of cell apoptosis
(Figure 3.5.1). Caspases exist as inactive proenzymes composed of a
prodomain, a large protease subunit, and a small protease subunit. The
activation of caspases requires proteolytic processing at conserved internal
aspartic residues to generate a heterodimeric enzyme consisting of the large
and small subunits. This protein is involved in the programmed cell death
induced by Fas and various apoptotic stimuli. The N-terminal FADD-like
death effector domain of this protein suggests that it may interact with Fasinteracting protein FADD (61).
A significant epigenetic silencing of the proapoptotic CASP8 gene was
observed during the progression of primary to recurrent GBM, probably
conferring tumor cells a relevant further growth advantage. In brain tumors,
CASP8 is hypermethylated in 40% of neuroblastomas and
medulloblastomas. Moreover, a high concordance between gene
27
methylation, decreased mRNA levels, and protein expression was observed
as well (26).
Figure 3.5.1. Apoptotic TRAIL signaling
Binding of TRAIL to death receptors (DR4, DR5) leads to the recruitment of the adaptor
molecule, FADD. DcR1/2 work as inhibitors of DR4/5. Pro-caspase-8 binds to FADD
leading to DISC formation and resulting in its activation. Activated caspase-8 directly
activates executioner caspases (caspase-3, -6, and -7) (type I cells) or cleaves Bid (type II
cells). Translocation of the truncated Bid (tBid) to the mitochondria promotes the assembly
of Bax-Bak oligomers and mitochondria outer membrane permeability changes.
Cytochrome c is released into cytosol resulting in apoptosome assembly. Active caspase-9
then propagates a proteolytic cascade of effector caspases activation that leads to
morphological hallmarks of apoptosis. Further cleavage of pro-caspase-8 by effector
caspases generates a mitochondrial amplification loop that further enhances apoptosis.
When FLIP levels are elevated in cells, caspase-8 preferentially recruits FLIP to form a
caspase-8-FLIP heterodimer which does not trigger apoptosis (64).
A study by Qi et al. showed the heterogeneity of glioblastomas and
derived cancer stem cells in the genomic status of CASP8, expression of
CASP8, and thus responsiveness to TRAIL-induced apoptosis. Clinic trials
may consider genomic analysis of the cancer tissue to identify the genomic
28
loss of CASP8 and use it as a genomic marker to predict the resistance of
glioblastomas to TRAIL apoptosis pathway-targeted therapies (62).
The loss of CASP8 expression in cancer cells may result in resistance to
drug-induced apoptosis and may be important for a therapeutic strategy.
Recent studies have demonstrated that the demethylation of CpG islands of
the CASP8 gene in resistant cancer cells sensitizes them to
chemotherapeutic agents (63). Moreover, the inactivation of CASP8
expression by promoter hypermethylation may result in cancer progression.
COX7A1
The COX7A1 (cytochrome c oxidase subunit VIIa polypeptide 1) gene is
located on chromosome 19q13.1. The product of this gene is one of the
nuclear-coded polypeptide chains of cytochrome c oxidase, the terminal
oxidase in mitochondrial electron transport (70). COX catalyses the electron
transfer from reduced cytochrome c to oxygen, producing H2O. This
reaction is coupled with proton transfer from the matrix to the
intermembrane space, thereby contributing to the energy stored in the
electrochemical gradient. The catalytic core of COX is composed of
mitochondrial encoded proteins whereas nucleus-encoded proteins
contribute to the structural and regulatory subunits of the complex. COX7A1
promoter methylation has been correlated with gene expression (71).
COX7A1 demonstrated increased expression after 5-azadc treatment in
methylated breast tumor cell lines (72). In an independent cohort of breast
tumor/normal pairs, COX7A1 demonstrated increased methylation in 70.6%
(12/17) of tumors compared with a corresponding normal tissue, and a
further 5 showed the equal levels of low-level methylation in a tumor and a
corresponding normal tissue (72). This gene has been detected differentially
expressed in lung cancer (73).
CD81
The CD81 (CD81 molecule, TAPA-1) gene is located on chromosome
11p15.5. This gene is localized in the tumor-suppressor gene region, and
thus it is a candidate gene for malignancies (65). It encodes a cellular
membrane protein belonging to the tetraspanin family. Most of these
members are cell-surface proteins that are characterized by the presence of 4
hydrophobic domains. This encoded protein is a cell surface glycoprotein
that is known to form a complex with integrins. The protein complex
mediates the events of signal transduction that play a role in the regulation
of cell development, activation, growth, and motility. In addition, CD81
29
may be involved in signal transduction. There are data that CD81
tetraspanin may have an inhibitory effect on cell movement and metastases
(Figure 3.5.2) (66, 67). The gene was found to be silenced by methylation in
multiple myeloma cell lines (68). Recent glioblastoma methylome studies
have shown a CD81 methylation rate of 54% (2, 6).
Figure 3.5.2. Tetraspanin signaling
CD81 (and CD9) is associated with phosphatidylinositol 4-kinase (PI4K), which locally
produces
phosphoinositides
(such
as
phosphatidylinositol-4,5-bisphosphate
(PtdIns(4,5)P2)). This causes the recruitment and activation of SHC (SRC-homology-2domain-containing transforming protein). Subsequent Ras-mediated activation of
extracellular signal-regulated kinase (ERK) or p38 or Jun N-terminal kinase (JNK)
pathways leads to proliferation or apoptosis, respectively. Caspase-3 activation might also
contribute to CD9-dependent regulation of apoptosis. Signaling through CD151 might
negatively regulate Ras-ERK/MAPK and Akt/protein kinase B (PKB) signaling in a celladhesion-dependent manner. CD151 (together with associated laminin-binding integrins)
also preferentially activates Rac (and Cdc42) over Rho, leading to regulation of the actin
cytoskeleton, cell spreading and motility. Conversely, CD82 might suppress tumor cell
invasion by downregulating p130Cas and thereby decreasing Rac activation. The Cterminal tails of CD151 (and CD81and CD9) contain PDZ-domain-binding motifs, and the
tail of CD151 is essential for its signaling and adhesion-strengthening functions. Possibly,
PDZ-domain-containing proteins could connect these tetraspanins to signaling and
cytoskeletal regulatory events that affect adhesion strengthening, cell spreading and cell
motility (69) .
30
DcR1
Despite 8p being a relatively small chromosome arm, it is one of the
most frequently altered genomic regions in human cancer, and is also rich in
candidate oncogenes and tumor suppressor genes associated with the
development of certain types of cancers (74). Deletions in this region are
observed in glioblastoma (75). One of these genes located on chromosome
8p region is DcR1 (tumor necrosis factor receptor superfamily, member 10c,
decoy without an intracellular domain, TNFRSF10C). The protein encoded
by the DcR1 gene is a member of the TNF-receptor superfamily. DcR1
lacks the intracellular death domain, can bind TRAIL, but is not capable of
transmitting the death signal because of its defective death domains (76).
This receptor is not capable of inducing apoptosis and is thought to function
as an antagonistic receptor that protects cells from TRAIL-induced
apoptosis. The expression of this gene was detected in many normal tissues
but not in most cancer cell lines, which may explain the specific sensitivity
of cancer cells to the apoptosis-inducing activity of TRAIL (77). DcR1
promoter methylation was observed in 40.4% of primary breast tumors (78).
Also, high methylation levels at the DcR1 promoter region in peripheral
blood was positively associated with perineural tumor spread and shorter
patients’ survival (79). DcR1 was frequently hypermethylated in
hepatocellular carcinoma tissue samples, and its methylation was found to
be closely associated with the inactivation of gene expression (80). As an
alternative to a deletion, DcR1 promoter methylation may be associated with
clinical data of glioblastoma patients. The methylation of the DcR1 gene
promoter was determined in 21% of low-grade gliomas (6) suggesting its
role in gliomagenesis.
TRAIL acts through 2 pairs of opposing receptors, 2 proapoptotic death
receptors, DR4 and DR5, and 2o antiapoptotic decoy receptors, DcR1 and
DcR2. All TRAIL receptors have been mapped to the same chromosomal
locus, 8p21-22, suggesting evolution from a common ancestral gene (76).
DR4
The gene DR4 (TNFRSF10A, tumor necrosis factor receptor superfamily,
member 10a) is located on chromosome 8p21. DR4 is a receptor for the
cytotoxic ligand TNFSF10/TRAIL. The adapter molecule FADD recruits
Casp8 to the activated receptor. The resulting death-inducing signaling
complex (DISC) performs Casp8 proteolytic activation, which initiates the
subsequent cascade of caspases (aspartate-specific cysteine proteases)
mediating apoptosis (81). The methylation of the DR4 gene promoter was
found to be extremely common, in both early gastric carcinomas and
31
advanced gastric carcinomas (98.4% and 83.3%, respectively) (76).
rhTRAIL and 2 newly designed monoclonal antibodies targeting DR4
(mapatumumab and HGS-ETR1) are new promising cancer drugs because
these agents have been shown to preferentially induce apoptosis in tumor
cells. However, the main obstacle in TRAIL-based therapies remains the
resistance of cancer cells to TRAIL and its analogues. Therefore, identifying
the resistance mechanisms and the predictors of response in these tumors
remains a key issue in modern cancer diagnostics and therapeutic strategies.
Among other possibilities, such predictive factors may be epigenetically
regulated in a tumor-specific manner (49). DR4 promoter methylation is
frequent in human astrocytic gliomas, and the epigenetic silencing of DR4
mediates resistance to TRAIL/DR4-based glioma therapies (49). The
hypermethylation of DR4 (up to 66.7%) was observed in neuroblastomas
(82). In non-CNS malignancies, higher methylation rates of DcR1 and DcR2
were reported (up to 100%), whereas DR4 and DR5 were rarely
hypermethylated (83). In contrast to the findings reported by van Noesel et
al. and Shivapurkar et al., Martinez et al. did not observe the CpG island
hypermethylation of the potentially antiapoptotic DcR1 in any GBM as well
as the lower rates of hypermethylation of DR4 and DR5 (25 and 12.5%,
respectively) (26). The development of targeted therapies restoring
functional extrinsic apoptosis, as recently shown in vivo with the synergistic
combination of the DNA demethylating agent decitabine and TRAIL, may
provide a useful tool to overcome the resistance of GBM to contemporary
treatment modalities.
GATA4
The GATA4 (GATA binding protein 4) gene is located on chromosome
8p23.1. This gene encodes a member of the GATA family of zinc-finger
transcription factors. The members of this family recognize the GATA
motif, which is present in the promoters of many genes (84). GATA4
regulates the gene expression of various genes, which are involved in
cardiac development, cardiac hypertrophy, and heart failure. In addition to
the heart, GATA4 plays important roles in the reproductive system,
gastrointestinal system, respiratory system, and cancer. Therefore, the
positive and negative regulations of GATA4 are important components of
biologic functions (85). Recently, GATA4 expression in normal embryonic
and adult mouse and human astrocytes has been reported, in which it
functions as an inhibitor of proliferation and inducer of apoptosis (86).
GATA4 knock-out in mice lead to embryonic lethality due to cardiac defects
(87, 88), and GATA4 mutations cause Holt-Oram syndrome and congenital
32
heart defects. GATA4 is frequently silenced in lung, colon, prostate, ovarian,
and breast cancer (89-93), but its exact role in cancer biology and the
mechanisms by which it operates are poorly understood (94). The
expression of GATA4 was extinguished in the majority of cell lines from
colorectal and gastric cancers as well as in primary tumors. Silencing was
associated with the hypermethylation of the GATA4 promoter sequences.
Moreover, the candidate target genes of the GATA4 factor were
commensurately decreased in their expression and also displayed the
methylated promoters (95).
GATA4 loss occurred through promoter hypermethylation or novel
somatic mutations (94). The re-expression of GATA4 also conferred the
sensitivity of GBM cells to temozolomide, a DNA alkylating agent currently
used in the GBM therapy (94). GATA4 can be activated through stimulating
the activity of the already existing GATA4 protein molecule or by increasing
the GATA4 protein expression. GATA4 protein expression can be altered
through transcriptional or translational mechanisms (85).
Given the role of GATA4 in regulating astrocyte proliferation and the
observed loss of GATA4 in several human cancers, GATA4 was
demonstrated to be a novel tumor suppressor in GBM.
GATA6
The GATA6 (GATA binding protein 6) gene is located on chromosome
18q11.2. GATA6 is one of 6 members of the mammalian GATA family of
transcription factors, all containing 2 highly conserved zinc-finger DNA
binding domains that interact with a canonical DNA motif
(G/A)GATA(A/T) (96). It is thought that GATA6 may be important for
regulating terminal differentiation and/or proliferation. It is also linked to
embryonic cardiac development. The loss of GATA6 resulted in the
enhanced proliferation and transformation of astrocytes (52). Knockin
GATA6 expression in human malignant astrocytoma cells reduced their
tumorigenic growth with decreased VEGF expression. The disruption of
GATA6 led to transformation and demonstrated that GATA6 is a new and
relevant human GBM tumor suppressor gene (52). The great majority of
human glioblastomas (which can arise from low-grade astrocytomas), but
not low-grade astrocytomas, displayed the loss of GATA6 expression,
mutations in GATA6, and loss of heterozygosity. The re-expression of
GATA6 in human malignant astrocytoma cells inhibited their growth (52).
This establishes GATA6 as a bona fide tumor suppressor in this disease,
marking the progression from low-grade astrocytoma to malignant
glioblastoma (95).
33
hMLH1
The hMLH1 (MutL homolog 1, colon cancer, nonpolyposis type 2 E.
coli) gene is a human gene located on chromosome 3p21.3. hMLH1 plays a
role in DNA mismatch repair. hMLH1 heterodimerizes with PMS2 to form
MutL alpha, a component of the postreplicative DNA mismatch repair
system (MMR) (Figure 3.5.3) (97).
Figure 3.5.3 Mismatch repair
A mispaired base is recognized by the hMSH2/GTBP complex while an insertion/deletion
loop is recognized by the hMSH2/hMSH3 complex. MutL related proteins
(hMLH1/hPMS2 and hMLH1/hPMS1 complexes) then interact with the MutS related
proteins that are already bound to the mispaired bases. The hMSH2/GTBP complex may
also support the repair of insertion/deletion loops (97).
DNA repair is initiated by MutS alpha (MSH2-MSH6) or MutS beta
(MSH2-MSH6) binding to a dsDNA mismatch, and then MutL alpha is
recruited to the heteroduplex. The assembly of the MutL-MutS34
heteroduplex ternary complex in the presence of RFC and PCNA is
sufficient to activate the endonuclease activity of PMS2. It introduces
single-strand breaks near the mismatch and thus generates new entry points
for the exonuclease EXO1 to degrade the strand containing the mismatch.
DNA methylation would prevent cleavage and therefore assure that only the
newly mutated DNA strand is going to be corrected. MutL alpha (MLH1PMS2) interacts physically with the clamp loader subunits of DNA
polymerase III, suggesting that it may play a role to recruit DNA
polymerase III to the site of the MMR. Also implicated in DNA damage
signaling, a process, which induces cell cycle arrest and can lead to
apoptosis in case of major DNA damages. hMLH1 heterodimerizes with
MLH3 to form MutL gamma, which plays a role in meiosis (98). hMLH1
inactivation by promoter hypermethylation has been reported to be
associated with some human cancers (99-101). Herman et al. have
suggested that DNA methylation associated with the transcriptional
silencing of hMLH1 is the underlying cause of mismatch repair defects in
most sporadic colorectal cancers (102). Xinarianos et al. have shown that
58.6% and 57.8% of lung cancer tumor specimens had the reduced
expression levels of the hMLH1 and hMSH2 proteins, respectively (103).
KRT81
The KRT81 (keratin 81, also KRTHB1 or Hb-1) gene is located on
chromosome 12q13. The protein encoded by this gene is a member of the
keratin gene family. As type II hair keratin, it is a basic protein that
heterodimerizes with type I keratins to form hair and nails (104). Keratins
are proteins expressed in all types of epithelial cells (105), with different
expression patterns among different carcinomas (106), and they are
extensively used as diagnostic markers. To date, KRT81 has not been
validated as a prognostic marker, and the present study is the first to link a
KRT81 variant to tumor recurrence. Campayo et al. have found that KRT81
may be a new immunohistochemical marker of squamous cell carcinoma
(107). KRT81 showed a clear positive staining in squamous cell carcinomas
(95% positive), whereas 81% of adenocarcinomas were negative (107).
Their findings indicate that single nucleotide polymorphisms in KRT81 and
XPO5 could be proved to be useful biomarkers for individualizing the
therapy in patients with non–small cell lung cancer and that KRT81 may be
a novel immunohistochemical marker of squamous cell carcinoma,
providing a new diagnostic tool to be used in therapeutic decision-making
(107).
35
NPTX2
The NPTX2 (neuronal pentraxin II) gene is located on chromosome
7q22.1. NPTX2 gene encodes a member of the family of neuronal
pentraxins, synaptic proteins that are related to C-reactive protein. This
protein is involved in excitatory synapse formation. It also plays a role in
the clustering of AMPA-type glutamate receptors at established synapses,
resulting in nonapoptotic cell death of dopaminergic nerve cells. Upregulation of this gene in the tissues of patients with Parkinson disease
suggests that the protein may be involved in the pathology of Parkinson
disease (108). Its methylation status was recently shown to be correlated
with age (109, 110). The aberrant methylation of NPTX2 was also detected
in 98% of primary pancreatic cancers, and it was not methylated in any of
the normal ductal epithelia studied (111). In vitro data showed that NPTX2
suppressed tumor cell proliferation, migration, and invasion. In addition,
experiments showed that NPTX2 affected cell cycle and apoptosis
indicating that NPTX2 might be a promising candidate tumor suppressor
gene (112). NPTX2 methylation has been demonstrated in various types of
cancers. The methylation frequencies of NPTX2 were statistically higher in
the malignant cholangiocarcinoma and pancreatic carcinoma group
(54.55%) than in the benign group (27.78%) (113). NPTX2 appeared to be
8% methylated in primary Mantle cell lymphoma. Furthermore, the number
of Ki-67 positive cells correlated significantly with the methylation levels of
NPTX2 in primary Mantle cell lymphoma (114). Although the methylation
status of NPTX2 showed a tendency to be associated with shorter overall
survival, it did not reach statistical significance (114). Mueller et al. in 2006
have suggested that NTPX2 is probably silencing by methylation in
glioblastomas (7).
SPINT1
The SPINT1 (serine peptidase inhibitor, Kunitz type 1, HAI-1,
hepatocyte growth factor activator inhibitor type 1) gene is located on
chromosome 15q15.1. SPINT1 strongly inhibits HGFAC, trypsin, KLK4,
KLK5, matriptase/ST14, prostasin/PRSS8, and hepsin/TMPRSS1. SPINT1
seems to have tumor suppressor activity: transgenic overexpression of
matriptase/ST14 resulted in skin carcinogenesis. However, the development
of skin cancer (squamous cell carcinoma) was suppressed when SPINT1
was coexpressed (115).
36
TES
The TES (testis derived transcript, 3 LIM domains) gene, also known as
testing, is located on chromosome 7q31.2. This gene is similar to
mouse testin, a testosterone-responsive gene encoding a Sertoli cell
secretory protein containing 3 LIM domains. LIM domains are double zincfinger motifs that mediate protein-protein interactions between transcription
factors, cytoskeletal proteins, and signaling proteins. This protein is a
negative regulator of cell growth and may act as a tumor suppressor. This
scaffold protein may also play a role in cell adhesion, cell spreading, and the
reorganization of the actin cytoskeleton. Multiple protein isoforms are
encoded by transcript variants of this gene (116). The loss of TES
expression also occurs frequently in various cancers (117, 118). Missense
mutations are scarce, and homozygous deletions have not been observed,
consistent with CpG promoter hypermethylation being a mechanism of TES
gene inactivation (118). Forced TES expression in HeLa or OVCAR5 cells
profoundly reduces cancer cell growth (118). TES knockout mice showed an
increased susceptibility to carcinogen-induced (nitrosomethylbenzylamine)
gastric cancer (119). In addition, the restoration of TES by adenoviral
transduction of non-TES expressing breast cancer and uterine sarcoma cell
lines inhibited their growth by the induction of apoptosis (120). Frequent
promoter hypermethylation in a subset of glioblastomas is therefore
consistent with the possibility of similar tumor-related inactivation in highgrade gliomas (7). TES methylation has been shown in primary tumors,
including glioblastomas (7) and ovarian cancer (118). Methylation at a
single site in the TES promoter has been reported for several cell lines
including lymphoid leukemia, breast cancer and pancreatic cancer cells
(117). TES methylation is closely associated with the loss of TES expression
in cell lines (117) and glioblastoma cells (7). A reciprocal relationship
between TES promoter methylation and expression has been demonstrated
(121).
TFPI-2
The TFPI-2 (tissue factor pathway inhibitor 2) gene is located on
chromosome 7q22. It may play a role in the regulation of plasmin-mediated
matrix remodeling. It inhibits trypsin, plasmin, factor VIIa/tissue factor, and
weakly factor Xa (122). TFPI2 has also been described as a MMP inhibitor.
MMP by effecting the proteolytic degradation or activation of cell surface
and extracellular matrix (ECM) proteins can modulate both cell-cell and
cell-ECM interactions, which influence cell differentiation, migration,
37
proliferation, and survival (123). The matrix metalloproteinase inhibitor
TIMP-2 has a high specificity for gelatinase A/MMP-2 (124). An imbalance
between MMP and TIMP-2 in favor of enzymatic activity is linked to the
degradation of the extracellular matrix associated with several physiologic
and pathologic events, including angiogenesis, invasion, and metastasis.
Since TIMPs are secreted molecules, they have the potential to be used for
the gene therapy of certain tumors (124). TFPI-2 was found to be
abundantly expressed in various normal human tissues, including seminal
vesicles, colon, stomach, brain, pancreas, esophagus, and liver (125-128).
Several studies suggested that the TFPI-2 gene is inactivated or absent
during tumor progression. The expression of TFPI-2 diminishes with an
increasing degree of malignancy, which may suggest a role for TFPI-2 in
the maintenance of tumor stability and inhibition of the growth of
neoplasms (126). Besides gene locus deletion and aberrant splicing, the
mechanism responsible for TFPI-2 down-regulation in tumor cells has been
majorly attributed to promoter hypermethylation (129). Transcriptional
silencing by the promoter hypermethylation of TFPI-2 was observed in
some human cancers (129). Gessler et al. have suggested that the antiinvasive properties of TFPI-2 are associated with inhibition of MMP-1 and
MMP-2, while the inhibition of MMP-9 seems to play a minor role in this
context (130). Their findings underscore the important role of TFPI-2 as a
tumor suppressor gene and indicate that TFPI-2 may be a useful diagnostic
marker for the aggressive phenotype of glial tumors (130).
38
4. MATERIALS AND METHODS
4.1. Patient tumor samples and characteristics
Part of the study was done with 22 glioblastoma samples obtained from
patients who underwent surgery in the Department of Neurosurgery,
Charité, Campus Virchow Klinikum (Berlin, Germany), after ethics
committee approval and patient consent. Glioblastoma samples were snap
frozen. All glioblastomas were histologically diagnosed according to the
WHO criteria. These samples were used for the analysis of COX7A1,
SPINT1, AREG, NPTX2, and KRT81 gene expression and methylation.
However, there were no data available on the clinical characteristics of these
patients.
Another part of the study included 100 WHO grade IV glioblastomas. All
glioblastomas were surgically resected and histologically diagnosed
according to the 2007 WHO criteria (131) in the Clinic of Neurosurgery,
Hospital of Lithuanian University of Health Sciences, Kaunas, Lithuania,
between 2003 and 2009. The date of database closure was November 2010.
The tissue samples were coded and separated from patients’ names and
personal information prior to their collection. Tumor samples were
collected, following written informed consent, in accordance with the
Lithuanian regulations and the Helsinki Declaration. Written informed
consent was obtained for every patient under the approval of the Ethics
Committee, Lithuanian University of Health Sciences. The following
clinical data were recorded for each patient: age at the time of the operation,
gender, time of the last follow-up, and patient status. Overall survival times
were recorded for all cases and were calculated from the time of operation
to death or last contact with live patients.
4.2. RNA extraction
Total RNA and DNA from glioblastoma tissue cells was purified
using a TRIZOL reagent (Invitrogen, Carlsband, CA, USA) following the
manufacturer’s recommendation. Genomic RNA and DNA were isolated
from glioblastoma tissue samples by crushing frozen samples with a mortar
and pestle.
39
Reagents:
TRIzol Reagent (Invitrogen)
Ethanol (Fisher)
Isopropanol (Fisher)
Chloroform (Fisher)
75% ethanol (in DEPC-treated water)
RNase-free water
Equipment:
10/100/1000 µL single-channel pipettes (Eppendorf)
Homogenizer (Kinematica Hand Held Polytron)
Spectrophotometer (Nanodrop)
RNAse-free 1.5 mL tubes (Ambion)
Centrifuge 5415 R (Eppendorf AG)
Protocol:
1. Add 0.75 mL of TRIzol LS Reagent per 50-100 mg of tissue sample
and homogenize. Incubate the homogenized sample for 5 minutes at
room temperature to permit complete dissociation of the
nucleoprotein complex.
2. Add 0.2 mL of chloroform per 0.75 mL of TRIzol LS Reagent used
for homogenization. Cap the tube securely. Shake tube vigorously by
hand for 15 seconds. Incubate for 2-15 minutes at room temperature.
3. Centrifuge the sample at 12 000g for 15 minutes at 4°C. Note: The
mixture separates into a lower red phenol-chloroform phase, an
interphase, and a colorless upper aqueous phase. RNA remains
exclusively in the aqueous phase. The upper aqueous phase is ~70%
of the initial volume of TRIzol LS Reagent used for homogenization.
4. Remove the aqueous phase of the sample by angling the tube at 45°
and pipetting the solution out. Avoid drawing any of the interphase
or organic layer into the pipette when removing the aqueous phase.
5. Place the aqueous phase into a new tube and proceed to the RNA
Isolation Procedure. (Save the interphase and organic phenolchloroform phase if isolation of DNA or protein is desired. See DNA
Isolation Procedure and Protein Isolation Procedure for details. The
organic phase can be stored at –70°C).
6. Add 0.5 mL of 100% isopropanol to the aqueous phase, for every
0.75 mL TRIzol LS Reagent used for homogenization. Incubate at
room temperature for 10 minutes.
40
7. Centrifuge at 12 000g for 10 minutes at 4°C. (The RNA is often
invisible prior to centrifugation, and forms a gel-like pellet on the
side and bottom of the tube).
8. Remove the supernatant from the tube, leaving only the RNA pellet.
9. Wash the RNA pellet, with 1 mL of 75% ethanol per 0.75 mL of
TRIzol LS Reagent used for the initial homogenization. Vortex the
sample to mix.
10. Centrifuge the sample at 7500g for 5 minutes at 4°C and discard the
supernatant. Air dry the RNA pellet for 5-10 minutes.
11. Resuspend the RNA pellet in RNase-free water by passing the
solution up and down several times through a pipette tip.
12. Store at –70°C.
Analyze the RNA on a spectrophotometer (Nanodrop).
A260/280 ratios should be between 1.8 and 2.1.
A260/230 ratios should be at least 1.9, more than 2.0 is preferable.
A260/280 ratios outside this range indicate DNA or protein contamination.
Low A260/280 ratios indicate carbohydrate, phenol, salt contamination.
4.3. DNA extraction
DNA extraction from glioblastoma tissue samples using TRIZOL
Reagents:
Ethanol (Fisher)
75% ethanol (in DEPC-treated water)
0 1 M sodium citrate in 10% ethanol
DNase-free water
Equipment:
10/100/1000 µL single-channel pipettes (Eppendorf)
Centrifuge 5415 R (Eppendorf AG)
Spectrophotometer (Nanodrop)
Protocol:
DNA is isolated from the interphase and phenol-chloroform layer saved
from the RNA phase separation step:
1. Remove any remaining aqueous phase overlying the interphase. This
is critical for the quality of the isolated DNA.
41
2. Add 0.3 mL of 100% ethanol per 0.75 mL of TRIzol LS Reagent
used for the initial homogenization. Cap the tube and invert the
sample several times to mix. Incubate samples for 2-3 minutes at
room temperature.
3. Centrifuge the tube at 2000g for 5 minutes at 4°C to pellet the DNA.
Remove the phenol-ethanol supernatant and save it in a new tube if
protein isolation is desired. The supernatant can be stored at –70°C
for several months.
4. Wash the DNA pellet with 1 mL of sodium citrate/ethanol solution
(0.1 M sodium citrate in 10% ethanol, pH 8.5) per 0.75 mL of
TRIzol® LS Reagent used for the initial homogenization. Incubate
for 30 minutes at room temperature. Mix occasionally by gentle
inversion.
5. Centrifuge at 2000g for 5 minutes at 4°C. Remove and discard
supernatant.
6. Repeat wash (steps 4-5), once.
7. Add 1.5-2 mL of 75% ethanol per 0.75 mL TRIzol LS Reagent used
for the initial homogenization. Incubate for 10-20 minutes at room
temperature. Mix the tube occasionally by gentle inversion.
8. Centrifuge at 2000g for 5 minutes at 4°C. Remove and discard
supernatant.
9. Air dry the DNA pellet for 5-10 minutes. Do not allow the pellet to
dry out.
10. Resuspend the DNA in 50 µL of water.
11. The DNA can be stored at –20°C.
Analyze the DNA on a spectrophotometer (Nanodrop).
A260/280 ratios should be between 1.6 and 1.8
DNA extraction from glioblastoma tissue samples using ZR Genomic
DNA™ Tissue MiniPrep
Tumor DNA was extracted from 25-40 mg of frozen tissue using ZR
Genomic DNA™ Tissue MiniPrep (Zymo Research, USA) according to
manufacturer’s protocol. DNA was isolated from glioblastoma tissue
samples by crushing frozen samples with a mortar and a pestle.
Reagents:
ZR Genomic DNA™ Tissue MiniPrep Kit (Zymo Research, USA)
Beta-mercaptoethanol
42
Equipment:
10/100/1000 µL single-channel pipettes (Eppendorf)
1.5 mL tubes (Eppendorf)
Centrifuge 5415 R (Eppendorf AG)
Thermomixer comfort (Eppendorf AG)
MS2 Minishaker (IKA, Wilmington, USA)
BioPhotometer (Eppendorf AG)
Reagent preparation:
• Add 260 μL Proteinase K Storage Buffer to each Proteinase K tube prior to
use. The final concentration of Proteinase K after the addition of Proteinase
K Storage Buffer is ~20 mg/mL.
•Add beta-mercaptoethanol to the Genomic Lysis Buffer to a final dilution
of 0.5% (v/v) i.e.
Protocol:
1. To a tissue sample (≤25 mg) in a microcentrifuge tube add a solution of:
 95 μL of H2O
 95 μL of 2× digestion buffer
 10 μL of proteinase K
2. Mix and then incubate the tube at 55ºC overnight.
3. Add 700-μL genomic lysis buffer mix thoroughly by vortexing.
Centrifuge at 10 000 g for one minute to remove insoluble debris.
4. Transfer the supernatant to a Zymo-Spin™ IIC Column in a collection
tube and centrifuge at 10 000g for one minute.
5. Add 200 μL of DNA pre-wash buffer and centrifuge at 10 000g for one
minute.
6. Add 400 μL of g-DNA wash buffer to the spin column and centrifuge at
10 000g for one minute.
7. Add 50 μL of water to the spin column. Incubate for 2-5 minutes at room
temperature and then centrifuge at top speed for 30 seconds to elute the
DNA.
The eluted DNA can be stored –20ºC for future use.
43
4.5. cDNA synthesis
First-Strand cDNA Synthesis Using SuperScript™ II RT Invitrogen
Equipment:
10/100 µL single-channel pipettes (Eppendorf)
Centrifuge 5415 R (Eppendorf AG)
Spectrophotometer (Nanodrop)
Thermomixer comfort (Eppendorf AG)
MS2 Minishaker (IKA, Wilmington, USA)
RNAse-free 0.5 mL tubes (Ambion)
Ice bath
Protocol:
• Add the following components to a nuclease-free microcentrifuge
tube:
RNA
5 µg
10 mM dNTP mix
1 µL
Oligo(dT)12-18 (0.5 µg/µL)
1 µL
DEPC- water
to 10 µL
•
Heat mixture to 65°C for 5 minutes and quick chill on ice. Collect
the contents of the tube by brief centrifugation and add:
2 µL
4 µL
2 µL
2 µL
10x RT buffer
25 mM MgCl2
0.1 M DTT
RNase inhibitor
•
•
•
•
•
Mix contents of the tube gently and incubate at 42°C for 2 minutes.
Add 1 μL of SuperScript™ II RT and mix by pipetting gently up and
down.
Incubate at 42°C for 50 minutes.
Inactivate the reaction by heating at 70°C for 15 minutes.
To remove RNA complementary to the cDNA, add 1 μL (2 units) of
RNase H, and incubate at 37°C for 20 minutes.
cDNA amplification
cDNA amplification reaction was performed in a total volume of 10 µL,
using AmpliTaq Gold PCR Master Mix (Applied Biosystems, USA) and 10
pmol of each primer (Metabion International AG, Germany).
44
Equipment:
MS2 Minishaker (IKA, Wilmington, USA)
Centrifuge 5415 R (Eppendorf AG, Germany)
Mastercycler epgradient S (Eppendorf AG, Germany)
10/100 µL single-channel pipettes (Eppendorf)
Protocol for one sample:
2x AmpliTaq Gold PCR Master Mix
Primer I
PrimerII
Water
cDNA
Total volume
5 µL
0.5 µL
0.5 µL
3 µL
1 µL
10 µL
cDNA amplification reaction conditions and cDNA PCR primers are shown
in Tables 4.5.1 and 4.5.2.
Table 4.5.1. cDNA amplification reaction conditions
Step
Initial DNA denaturation
DNA denaturation
Annealing
Extension
Final Extension
END
Temperature, ºC
Duration
95
95
See table below
72
72
4
5 min
30 s
40 s
30 s
10 min
–
The number of
cycles
–
33
–
–
Table 4.5.2. cDNA PCR primers
Primer name
NPTXII_cDNAf
NPTXII_cDNAr
KRTHB1_cDNAf
KRTHB1_cDNAr
COX7A1_cDNAf
COX7A1_cDNAr
AREG_cDNAf
AREG_cDNAr
SPINT1_cDNAf
SPINT1_cDNAr
GAPDH_cDNAf
GAPDH_cDNAr
Sequence
CAACGACAAGGTTGCGCAGC
ACGGTGTCCTGCTCTTGTCC
ATGCATCACCACCGTGTCGG
GCGCACCTTGTCGATGAAGG
AGGACAAGGCAGAATGCAGG
GGATGTCATTGTCCTCCTGG
TGTCCCAGAGACCGAGTTGC
GCATAATGGCCTGAGCCGAG
CATCAACTGCCTCTACGAGC
ATCCCAGACCCTCCAAAGCC
GGTTTTTCTAGACGGCAGGTCA
TGGCAAATTCCATGGCACCGTCA
45
Tm, ºC
63
63
63
63
63
63
63
63
58
58
58
58
4.6. DNA bisulphite modification
DNA Bisulphite modification using the CpGenome DNA Modification
Kit
Sodium bisulphite modification is based on the selective demetilation of
unmethylated cytosines to uracils where methylated cytosines remain
unchanged. This chemical reaction converts a difference in methylation into
a difference in sequence. Genomic DNA (1 µg) was modified with sodium
bisulphite using the CpGenome DNA Modification Kit (Chemicon
International) according to the manufacturer’s instructions.
Reagents:
CpGenorneTDmN A Modification Kit
β-Mercaptoethanol
Ethanol
EDTA
Tris Base
NaOH pellets
(Chemicon International)
(Sigma)
(Fishers)
(Metford)
(Fishers)
Equipment:
10/100 µL single-channel pipettes (Eppendorf)
Centrifuge 5415 R (Eppendorf AG)
Spectrophotometer (Nanodrop)
Thermomixer comfort (Eppendorf AG)
MS2 Minishaker (IKA, Wilmington, USA)
Microcentrifuge tubes, 1.5 mL (Eppendorf)
pH electrode
Protocol:
Reagent Preparation
1. 3 M NaOH stock (freshly prepared prior to each use): dissolve 1 g of
dry NaOH pellets in 8.3 mL of water.
2. 20 mM NaOH/90% ethanol (freshly prepared prior to each use). To
prepare 1 mL of this solution, combine 900 μL of 100% ethanol,
93.4 μL of H2O, and 6.6 μL of 3 M NaOH.
3. Dissolve Reagent I (freshly prepared prior to each use). Warm bottle
to room temperature before opening. For each sample to be
modified, weigh 0.227 g of DNA Modification Reagent I and add to
0.571 mL of water. Mix thoroughly by vortexing. Adjust the pH to
46
5.0 with approximately 20 μL of 3M NaOH. Protect the Reagent I
solution from light.
4. Dissolve Reagent II. Warm bottle to room temperature before
opening. Add 1 μL of -mercaptoethanol to 20 mL of deionized
water. Add 750 μL of this solution to 1.35 g of DNA Modification II
for each sample to be modified. Mix well to ensure complete
dissolution.
DNA Modification Procedure:
1. In screw cap 1.5-2.0 mL microcentrifuge tubes: to 1 μg of DNA, add 2 μL
of DNA Modification Reagent IV to the sample DNA and bring the total
volume to100 μL with water. Then add 7.0 μL of 3M NaOH and mix.
2. Incubate DNA for 10 minutes at 50°C (heat block or water bath).
3. Add 550 μL of freshly prepared DNA Modification Reagent I and vortex.
4. Incubate at 50°C for 4-16 hours in a heat block or a water bath protected
from light.
5. Resuspend DNA Modification Reagent III by vortexing vigorously. Draw
suspension into and out of a 1 mL plastic pipette tip 10× to disperse any
remaining clumps.
6. Add 5 μL of well-suspended DNA Modification Reagent III to the DNA
solutions in the tubes.
7. Add 750 μL of DNA Modification Reagent II and mix briefly.
8. Incubate at room temperature for 5-10 minutes.
9. Spin for 10 seconds at 5000g to pellet the DNA Reagent III (a small
white pellet should be present). Discard supernatant.
10. Add 1.0 mL of 70% EtOH, vortex and spin for 10 seconds at 5000g, and
discard supernatant. Perform this step 3 times.
11. After the supernatant from the third wash has been removed, centrifuge
the tube at high speed for 2 minutes, and remove the remaining supernatant
with a plastic pipette tip.
12. Add 50 μL of the 20 mM NaOH/90% EtOH solution to the appropriate
samples.
13. Vortex briefly to resuspend the pellet, and incubate at room temperature
for 5 minutes.
14. Spin for 10 seconds at 5000g to move all contents to the tip of a tube.
Add 1.0 mL of 90% ethanol and vortex to wash the pellet. Spin again and
remove the supernatant. Repeat this step one more time.
15. After the supernatant from the second wash has been removed,
centrifuge the sample at high speed for 3 minutes.
47
16. Remove all of the remaining supernatant with a plastic pipette tip. Allow
the tube to dry for 10-20 minutes at room temperature (alcohol odor should
diminish).
17. Add 25 μL of TE Buffer.
18. Incubate the sample for 15 minutes at 50°C-60°C to elute DNA.
19. Centrifuge at high speed for 2-3 minutes and transfer the sample
(supernatant) to a new tube using a plastic pipette tip.
20. Proceed to MSP or sequencing, or store at –15°C to –25°C for up to 2
months, at –80°C for up to 6 months.
Bisulfite conversation of DNA using EZ DNA Methylation Kit
The methylation status of genes promoters was determined by bisulfite
treatment of DNA. An amount of 400 ng of DNA was used for bisulfite
modification. DNA modification was performed using EZ DNA
Methylation Kit (Zymo Research, USA), and all procedures were done
according to the manufacturer’s protocol.
Equipment:
10/100 µL single-channel pipettes (Eppendorf)
Centrifuge 5415 R (Eppendorf AG)
Spectrophotometer (Nanodrop)
Thermomixer comfort (Eppendorf AG)
MS2 Minishaker (IKA, Wilmington, USA)
Microcentrifuge tubes, 1.5 mL (Eppendorf)
Protocol:
Reagent Preparation
1. Preparation of CT Conversion Reagent. Add 750 μL of water and 210 μL
of M-Dilution Buffer to a tube of CT Conversion Reagent. Mix at room
temperature with frequent vortexing or shaking for 10 minutes.
2. Preparation of M-Wash Buffer. Add 24 mL of 100% ethanol to 6 mL of
M-Wash Buffer concentrate.
DNA Modification Procedure:
1. Add 5 μL of M-Dilution Buffer to the DNA sample and adjust the total
volume to 50 μL with water. Mix the sample by flicking or pipetting up and
down.
2. Incubate the sample at 37°C for 15 minutes.
48
3. After the above incubation, add 100 μL of the prepared CT Conversion
Reagent to each sample and mix.
4. Incubate the sample in the dark at 50°C for 12-16 hours.
5. Incubate the sample at 0°C-4°C for 10 minutes.
6. Add 400 μL of M-Binding Buffer to a Zymo-Spin™ IC Column and
place the column into a provided collection tube.
7. Load the sample (from Step 5) into the Zymo-Spin™ IC Column
containing the M-Binding Buffer. Close the cap and mix by inverting the
column several times.
8. Centrifuge at full speed (>10 000g) for 30 seconds. Discard the flowthrough.
9. Add 100 μL of M-Wash Buffer to the column. Centrifuge at full speed for
30 seconds.
10. Add 200 μL of M-Desulphonation Buffer to the column and let stand at
room temperature (20°C-30°C) for 15-20 minutes. After the incubation,
centrifuge at full speed for 30 seconds.
11. Add 200 μL of M-Wash Buffer to the column. Centrifuge at full speed
for 30 seconds. Add another 200 μL of M-Wash Buffer and centrifuge for
an additional 30 seconds.
12. Place the column into a 1.5 ml microcentrifuge tube. Add 40 μL of MElution Buffer directly to the column matrix. Centrifuge for 30 seconds at
full speed to elute the DNA.
DNA is ready for immediate analysis or can be stored at or below –20°C
for later use. For long-term storage, store at or below –70°C.
4.7. Sodium bisulphite sequencing
Sodium bisulphite sequencing is a genomic sequencing technique (132)
allowing the generation of methylation maps with single nucleotide
resolution based on bisulphite modification of DNA and sequencing of PCR
products. The method involves the subcloning of a PCR product into an
appropriate vector and sequencing of the inserts of several individual clones.
The resulting final sequence pattern shows that all original cytosines appear
as thymines, whereas methylated cytosine bases are displayed as cytosines,
resulting in the quantification of CGI methylation at every CpG position.
49
Preparing for sequencing using TOPO TA Cloning Kit
Protocol for one sample:
Component
2x AmpliTaq Gold PCR Master Mix
Primer I
Primer II
Water
DNA
Total volume
Volume
10 μL
1 μL
1 μL
6 μL
2 μL
20 μL
cDNA amplification reaction conditions and PCR primers are shown in
Tables 4.7.1 and 4.7.2.
Table 4.7.1. DNA amplification reaction conditions
Step
Initial DNA denaturation
DNA denaturation
Annealing
Extension
Final Extension
END
Temperature, ºC
95
95
See table below
72
72
4
Duration
5 min
30 s
60 s
2 min
10 min
–
No. of cycles
–
36
–
–
Table 4.7.2. PCR primers
Primer name
NTPX2_pr_bm_Fr_1f
NTPX2_pr_bm _Fr_1r
NTPX2_pr_bm_Fr_2f
NTPX2_pr_bm_Fr_2r
NTPX2_pr_bm_Fr_3f
NTPX2_pr_bm_Fr_3r
KRT81_pr_bm_Fr_1f
KRT81_pr_bm_Fr_1r
KRT81_pr_bm_Fr_2f
KRT81_pr_bm_Fr_2r
Cox_pr_bm_Fr_1f
Cox_pr_bm_Fr_1r
Cox_pr_bm_Fr_2f
Cox_pr_bm_Fr_2r
SPINT1_pr_bm_Fr_1f
SPINT1_pr_bm_Fr_1r
AREG_pr_bm__Fr_1f
AREG_pr_bm_Fr_1r
Sequence
TTTAGGTTTTGTTGAATAAGGTTAT
TAAAAAAAAAATAAAAACCCCT
TTTTTTTTGAGGTAGAGTT
CCCTTTCTTAAACTAAAAAAC
GTTTTTTAGTTTAAGAAAGGG
AACTATCCTAAACCCCAAC
GGTGTTTTGAGGAGTATATTGGAGT
TAATAATACATAAAAAACTAAACCC
GGGTTTAGTTTTTTATGTATTATTAT
TATAATCCAAAACACCCACCTTATC
TTTGTAAAAATGTATTTTTTGGTAT
ACCTACATTCTACCTTATCCTCTTCC
GTGAGGTTTTTATGGGTTGGGT
AAAAAAATTTCCTAAATTAAAAAAAA
TTTTAGGTTTGGGGTTGGGAAAGTA
CCTAATTTCTAATAAAACTAAATCAA
AGGTTTAAGTTTTATTTTTTTT
AAACTCTCATTAATCCTTC
50
Tm, ºC
56
56
47
47
51
51
55
55
55
55
51
51
53
53
60
60
49
49
Remove 10 μL from the reaction and analyze by agarose gel electrophoresis.
Prepare the obtained PCR products to clone with TOPO TA Cloning Kit
(Invitrogen) according to the manufacturer’s recommendations.
TOPO TA Cloning Reaction
•
Protocol for TOPO Cloning reactions:
Reagent
Volume
Fresh PCR Product
2 μL
Salt solution
1 μL
Water
2 μL
TOPO vector
1 μL
•
•
Mix gently and incubate for 5 minutes at room temperature.
Place the tubes on ice.
One shot chemical transformation
Supplied Materials:
• S.O.C. medium (included with the kit)
• LB plates containing 50 μg/mL of ampicillin
• 40 mg/mL X-gal in dimethylformamide (DMF)
1. Add 2 μL of the TOPO® Cloning reaction into a vial of One Shot®
Chemically Competent E. coli and mix gently.
2. Incubate on ice for 30 minutes.
3. Heat-shock the cells for 30 seconds at 42°C without shaking.
4. Immediately transfer the tubes on ice.
5. Add 250 μL of room temperature S.O.C. medium.
6. Cap the tube tightly and shake the tube horizontally (200 rpm) at 37°C for
1 hour.
7. Spread 10-50 μL from each transformation on a prewarmed selective
plate.
8. Incubate plates at 37°C overnight.
9. Pick ~10 white or light blue colonies for analysis. Do not pick dark blue
colonies.
LB-medium agar-plates (1 L):
1. Dissolve 10 g of tryptone, 5 g of yeast extracts, 15 g of agar, and 10 g og
NaCl in 950 mL of deionized water.
51
2. Adjust the pH of the medium to 7.0 using 1 N NaOH and bring volume
up to 1 liter.
3. Autoclave on liquid cycle for 20 minutes at 15 psi. Allow solution to cool
to 55°C and add antibiotic (50 µg/mL of Amp or Kan) and pour into Petri
dishes.
4. Let harden, then invert, and store at 4°C in the dark.
Primers of bacterial plasmids for PCR reaction and cDNA amplification
reaction conditions are shown in Tables 4.7.3 and 4.7.4.
Table 4.7.3. Primers of bacterial plasmids for PCR reaction
Primer
M13 Forward
M13 Reverse
Sequence
5'-GTAAAACGACGGCCAG-3'
5'-CAGGAAACAGCTATGAC-3'
Component
2x polymerase mix
Primer I
Primer II
Water
Take a little amount of bacterial colonies
Total
pMoles Supplied
407 pM
385 pM
Volume
10 μL
1 μL
1 μL
8 μL
20 μL
Table 4.7.4. DNA amplification reaction conditions
Step
Initial denaturation
DNR denaturation
Annealing
Extension
Final extension
END
Temperature, ºC
95
95
55
72
72
4
Duration
5 min
30 s
1 min
1 min
10 min
–
Cycles
–
25
–
–
Remove 10 μL from the reaction and analyze by agarose gel electrophoresis.
PCR Product Cleanup
ExoSAP-IT reagent (Affymetrix, USA) was used for PCR Product Cleanup.
It treats PCR products ranging in size from less than 100 bp to more than 20
kpb with absolutely no sample loss by removing unused primers and
nucleotides.
52
Protocol:
1. Remove the ExoSAP-IT reagent from a –20°C freezer and keep on ice
throughout this procedure.
2. Mix 10 μL of a post-PCR reaction product with 4 μL of ExoSAP-IT
reagent for a combined 14-μL reaction volume.
3. Incubate at 37°C for 15 minutes to degrade remaining primers and
nucleotides.
4. Incubate at 80°C for 15 minutes to inactivate ExoSAP-IT reagent.
The PCR product is now ready for use in DNA sequencing, Treated PCR
products may be stored at –20°C until required.
Preparation for sequencing using SEQ
USA):
Component
Clean PCR product
ABI 5x sequencing buffer
Primer M13 (5 pmoL/μL)
BDT premix
Mili-Q water
Total
Sequencing Reaction Kit (Montage,
Volume
0.5 μL
1.5 μL
0.5 μL
1 μL
6.5 μL
10 μL
DNA amplification reaction conditions are shown in Table 4.7.5.
Table 4.7.5. DNA amplification reaction conditions
Step
Initial DNA denaturation
DNA denaturation
Annealing
Extension
Final extension
Temperature, ºC
93
96
55
60
10
Duration
3s
10 s
15 s
4 min
–
Cycles
–
24
–
4.8. Methylation-specific PCR
MSP is a qualitative PCR-based technique, which is highly sensitive and
has the potential to detect the small subpopulations of a methylated
sequence (133). It is used to detect a sequence corresponding to a particular
methylation state (either methylated or unmethylated) in extracted bisulphite
modified genomic DNA. Primers were specifically designed to utilize the
53
sequence differences between methylated and unmethylated DNA resulting
from sodium bisulphate treatment.
For all MSP assays, a series of positive and negative controls plus H20
blank were run simultaneously with each reaction.
Reagents:
Maxima® Hot Start PCR Master
Mix (2X)
Primers
Bisulfite Converted Universal
Methylated Human DNA Standard
(Thermo Scientific, Lithuania)
(Metabion international, Germany)
(ZymoResearch, USA)
For a negative methylation control, normal human blood lymphocyte
DNA treated with bisulfite was used.
Promoter methylation was detected by MSP. Each MSP reaction
incorporated approximately 20 ng of bisulfite treated DNA as a template.
Sequence primers specific for methylated and unmethylated DNA were
obtained from the published data. For AREG and NPTX2, the primer design
for these assays was based on bisulfite sequencing results and incorporated
2 CpG dinucleotides that were regularly methylated in glioblastoma and
unmethylated in normal adult human brain. All primers are listed in Table
4.8.1.
Equipment:
MS2 Minishaker (IKA, Wilmington, USA)
Centrifuge 5415 R (Eppendorf AG, Germany)
Mastercycler epgradient S (Eppendorf AG, Germany)
10/100 μL single-channel pipettes (Eppendorf)
Protocol for one sample:
Component
2x polymerase mix
Primer_M_F or Primer_U_F
Primer_M_R or Primer_U_R
Water
Modified-DNA (20 ng/μL)
Volume
10 μL
1 μL
1 μL
6 μL
2 μL
Total 20 μL
54
Table 4.8.1. Primers for methylation-specific polymerase chain reaction
Primer
Tm, °C
M_F
CGGCGTATATTTTCGGTTTTTATTC
62
M_R
GTCTCGATCTCTAAAACAACTCGAT
62
U_F
GAGAGTGGTGTATATTTTTGGTTTTTATTT
62
U_R
ATCTCAATCTCTAAAACAACTCAAT
62
CASP8
M_F
TAGGGGATTCGGAGATTGCGA
58
M_R
CGTATATCTACATTCGAAACGA
58
U_F
TAGGGGATTTGGAGATTGTGA
58
U_R
CCATATATATCTACATTCAAAACAA
58
CD81
M_F
CGACGGCGGCGATTTTATCGC
58
M_R
GACCTACGAAACGCGAACCG
58
U_F
GTGATGGTGGTGATTTTATTGT
58
U_R
ACAACCTACAAAACACAAACCAA
58
DcR1
M_F
TTACGCGTACGAATTTAGTTAAC
60
M_R
TCAACGACCGACCGAAACG
60
U_F
GAATTTTTTTATGTGTATGAATTTAGTTAAT
60
U_R
CCATCAAACAACCAAAACA
60
DR4
M_F
TTCGAATTTCGGGAGCGTAGC
60
M_R
GTAATTCAATCCTCCCCGCGA
60
U_F
GTAGTGATTTTGAATTTTGGGAGTGTAGT
60
U_R
CTCATAATTCAATCCCCACAA
60
GATA4
M_F
GTATAGTTTCGTAGTTTGCGTTTAGC
62
M_R
AACTCGCGACTCGAATCCCCG
62
U_F
TTTGTATAGTTTTGTAGTTTGTGTTTAGT
62
U_R
CCCAACTCACAACTCAAATCCCCA
62
GATA6
M_F
CGGGGTAGATTTCGGATTCGC
60
M_R
CAACCGAACCTCGAACGAACG
60
U_F
GTGTGGGGTAGATTTTGGATTTGT
60
U_R
AAACAACCAAACCTCAAACAAACA
60
hMLH1
M_F
ACGTAGACGTTTTATTAGGGTCGC
60
M_R
CCTCATCGTAACTACCCGCG
60
U_F
TTTTGATGTAGATGTTTTATTAGGGTTGT
60
U_R
ACCACCTCATCATAACTACCCACA
60
NPTX2
M_F
CGTGATTTTGTTTTTAATTTCG
60
M_R
GAATACAACGATCCCCGA
60
U_F
TGTGATTTTGTTTTTAATTTTG
60
U_R
CCAAATACAACAATCCCCAA
60
TES
M_F
TATTGAGTTTGTTTAGTAGGGCGTC
62
M_R
AATAACAACCGAACAACTCCG
62
U_F
TGAGTTTGTTTAGTAGGGTGTTG
62
U_R
ATAACAACCAAACAACTCCAA
62
TFPI2
M_F
TTCGTTTCGTATAAAGCGGGTATTC
61
M_R
CCGTCAAAAAAAACAACAAAATCG
61
U_F
TTTGTTTTGTATAAAGTGGGTATTTGG
61
U_R
CATCAAAAAAAACAACAAAATCAAC
61
M, methylated; U, unmethylated; F, forward; R, reverse; Tm, melting temperature.
Gene
AREG
55
DNA amplification reaction conditions are shown in Table 4.8.2.
Table 4.8.2. DNA amplification reaction conditions
Step
Initial denaturation
DNR denaturation
Annealing
Extension
Final extension
END
Temperature, ºC
95
95
Look table
72
72
4
Duration
5 min
30 s
1 min
1 min
5 min
–
Cycles
–
36-40
–
–
The amplification products were loaded on a 2% agarose gel with ethidium
bromide, and after electrophoresis gels were documented under UV. In a
case of both methylated and unmethylated signal appearance in a gel,
methylation of the gene was considered.
Agarose gel electrophoresis
Reagents:
TopVision™ Agarose
Buffer 10xTBE UltraPure™
6X DNA Loading Dye
O’GeneRuler™ Low Range DNA
Ladder, ready-to-use
Distillated water
Ethidium bromide
(Thermo Scientific, Lithuania)
(Invitrogen, USA)
(Thermo Scientific, Lithuania)
(Thermo Scientific, Lithuania)
Equipment:
Microwave
Electronic balance ABJ 120-4M (KERN & Sohn GmbH)
Mini horizontal submarine unit HE-33 (Amersham Biosciences)
Electrophoresis Power Supply EPS 601 (Amersham Biosciences)
Bio-Rad Molecular imager Gel Doc (BIO-RAD laboratories)
Protocol:
Making the gel
1. Place a casting platform with a well former sideways in a gel stand where
you wish to pour the gel.
2. For a 2% gel, add 2 g of high purity, wide range agarose per 100 mL to be
made.
3. Add 100 mL of 1X TBE.
56
4. Mix and microwave for about 1-3 minutes. Stop and mix the solution
once or twice during the microwaving.
5. Add 0.5 g of ethidium bromide per mL of gel solution from stock
solution (10 mg/mL).
6. Pour hot gel into a gel cast on a flat surface avoiding bubbles.
7. When gel solidifies, turn gel and tray to a proper position and fill a gel
stand with 1X TBE so that it covers the gel completely.
Loading the gel
Mix the reaction volumes on a piece of parafilm as follows:
1. First, load a loading dye onto parafilm, add the appropriate volume
of the sample, and mix with one pipette.
2. Then, with the same pipette tip, load the sample with the loading
pipette into the appropriate well very carefully.
Loading buffer (2 μL) was added to each PCR product and 10 μL of this
mixture was pipetted into wells of a 2% agarose gel. A DNA ladder was run
at the same time to confirm that products were of the expected size.
Gels were run for 35-45 minutes at 150 V. DNA was visualized with UV
light using a Bio-Rad Molecular imager Gel Doc System. For each primer
sequences, samples which were positive displayed visible bands in their
corresponding lanes. The MSP assay including the samples of unknown
methylation status was only regarded as being successful if there was a
visible band in the positive control lanes and no visible bands in both the
negative control and H2O blank lane.
4.9. Statistical analysis
Statistical analysis was carried out with the software of IBM SPSS
Statistics 19 (IBM SPSS Inc., Chicago, IL). Quantitative data are presented
as mean and standard deviation (SD). To show the reliability of the
estimate, the confidence interval (CI) with 95% confidence level was
calculated. To test the statistical hypothesis, the significance level of 0.05
was selected. The Kaplan-Meier method was used to estimate survival
functions. For comparing the survival of 2 groups, the log-rank and
generalized Wilcoxon tests were used. The Cox proportional hazard
regression model was applied to determine the optimal system of
independent variables and prognosis-related hazard. The independent
sample t test was used to compare the means of 2 groups. The chi-square
test was used to test statistical hypothesis about independence of 2 variables.
57
5. RESULTS
5.1. COX7A1, KRT81, AREG, NPTX2, and SPINT1 expression
The analysis of gene expression and its regulation is one of the most
intriguing fields in cancer research. Promoter methylation affects the
expression of many genes in normal and cancer tissues and may play an
important role in gliomagenesis. The pharmacologic manipulation of glioma
cells with the demethylating agent 5´-aza-dC, combined with genome wide
expression profiling, succeeded in unveiling novel candidate genes, which
are epigenetically regulated in cancer in general (134) and in gliomas in
particular (7, 49).
COX7A1, KRT81, AREG, NPTX2, and SPINT1 were among promising
candidate genes identified in a previous genome wide study (7). These
genes showed robust expression in normal brain samples, their gene
promoter harbored a CpG-rich island, and 5`-aza-dC demethylation was
accompanied by a significant up-regulation of their expression in glioma
cells, suggesting an epigenetic gene regulation (7). One important indication
for a potential role of a gene in the development of cancer is its altered
expression compared with healthy tissues. To elucidate a potential
epigenetic gene regulation of COX7A1, SPINT1, AREG, NPTX2, and
KRT81, we investigated their expression in human brain tissues and
glioblastoma samples in relation to the methylation status of their gene
promoter.
RT-PCR is commonly applied to analyze gene expression. By RT-PCR,
gene expression can be investigated with high accuracy and excellent
reproducibility. RNA sequences, unlike DNA, cannot be multiplied in vitro
directly, but the RNA sequence can be converted into cDNA sequences with
the assistance of specific enzymes, i.e., reverse transcriptases. In our study,
a two-step RT-PCR was used. During the first stage of the experiment,
cDNA synthesis was carried out; during the second, cDNA amplification.
According to the amplified cDNA content in an electrophoresis gel, the
differences in gene expression between glioblastomas and normal brain
tissue can be seen.
GAPDH (glyceraldehyde 3-phosphate dehydrogenase) amplification was
used as an internal control for cDNA quality and quantity. Expression array
data for COX7A1, KRT81, AREG, NPTX2, and SPINT1 genes were verified
by RT-PCR using cDNA isolated from 22 snap-frozen glioblastoma
samples. In addition, we investigated the expression of these genes in cDNA
58
isolated from the normal human brain. Data showed that the housekeeping
gene GAPDH was equally expressed in all samples confirming the excellent
cDNA quality and the equal cDNA quantity in these samples. Of note,
COX7A1, KRT81, AREG, NPTX2, and SPINT1 showed a differential
expression pattern in the glioblastoma samples (Figure 5.1.1).
GAPDH
AREG
COX7A1
KRT81
NPTX2
SPINT1
GAPDH
AREG
COX7A1
KRT81
NPTX2
SPINT1
Figure 5.1.1. Expression of AREG, COX7A1, KRT81, NPTX2, and SPINT1
in glioblastoma samples (1-22) and in normal fetal (FB) and adult human
brain (AB) samples
The number of glioblastoma and its place in the electrophoresis gel are
shown in Table 5.1.1.
Gene expression was decreased in some glioblastoma samples. Normal
fetal and adult human brain showed robust expression of COX7A1, KRT81,
AREG, NPTX2, and SPINT1 suggesting that the down-regulation of these
59
genes in glioblastoma samples was tumor related. As the methylation of
these gene promoters might be one of the possible mechanisms of COX7A1,
KRT81, AREG, NPTX2, and SPINT1 down-regulation, expression analysis
was followed by the analysis of promoter methylation by bisulfite
sequencing.
Table 5.1.1. The number of glioblastoma and its place in the electrophoresis
gel
1. 22164
2. 26882
3. 23218
4. 23480
5. 23316
6. 26718
7. 24040
8. 25290
9. 24672
10. 23480
11. 24528
12. 31118
13. 24702
14. 28590
15. 21636
16. 27824
17. 25108
18. 22334
19. 24440
20. 26494
21. 22584
22. 21660
FB
AB
5.2. Bisulfite sequencing of genes promoters
DNA sequencing is the method used to determine the primary
structure of DNA or order of the nucleotide bases in a molecule of DNA.
The bisulfite sequencing method allows a precise analysis of methylation in
a certain region by converting all nonmethylated cytosines into thymines,
while methylated cytosines remain unchanged. In order to address the
possibility of gene regulation by promoter methylation, we decided to
perform the bisulfite sequencing of the AREG, COX7A1, KRT81, NPTX2,
and SPINT1 gene promoters in selected glioblastoma samples, normal adult
human brain specimens, and DNA derived from human leukocytes.
5.2.1. NTPX2 bisulfite sequencing
The region of the NPTX2 gene promoter with CpG islands is located on
chromosome 7q21.3. The sequence of the promoter region with primer
binding sites is shown in Annex. Figure 5.2.1.1 shows the CpG island of the
NPTX2 promoter and the fragments of bisulfite sequencing.
60
Figure 5.2.1.1. The CpG island of the NPTX2 promoter and the location of
bisulfite sequencing fragments with the number of CpG dinucleotides
Three of the NPTX2 fragments were sequenced at the transcription start
site. The first fragment is 571 bp in size that covered 53 CpG dinucleotides,
the second 184-bp fragment covered 29 CpG dinucleotides, and the third
278-bp fragment covered 40 CpG dinucleotides.
Leukocyte, brain, glioblastoma and DNA was sequenced. The results of
bisulfite sequencing of the first fragment are shown in Figure 5.2.1.2.
The results of bisulfite sequencing of the second fragment are shown in
Figure 5.2.1.3.
Figure 5.2.1.3. Results of bisulfite sequencing of the second fragment of the
NPTX2 promoter in leukocyte and glioblastoma DNA
L1 and L2 indicate 2 different samples of leukocytes; 24528, 24702, and 21636,
glioblastoma samples. The numbers at the top of the figure indicate the numbers of CpG
dinucleotides within the fragment. Black, methylated CpG dinucleotide, white,
unmethylated.
61
Figure 5.2.1.2. Results of bisulfite sequencing of the first fragment of the
NPTX2 promoter in leukocyte, brain, and glioblastoma DNA
L1-3 indicates 3 different samples of leukocytes; NB1 and NB2, different brain samples;
23316, 24528, 24702, and 21636, glioblastoma samples. The numbers at the top of the
figure indicate the numbers of CpG dinucleotides within the fragment. Black, methylated
CpG dinucleotide, white, unmethylated.
62
The results of bisulfite sequencing of the third fragment are shown in
Figure 5.2.1.4.
Figure 5.2.1.4. Results of bisulfite sequencing of the third fragment of the
NPTX2 promoter in leukocyte, brain, and glioblastoma DNA
L1-3 indicates 3 different samples of leukocytes; NB1 and NB2, different brain samples;
27824, 24528, 25290, and 21636, glioblastoma samples. The numbers at the top of the
figure indicate the numbers of CpG dinucleotides within the fragment. Black, methylated
CpG dinucleotide, white, unmethylated.
The analysis of bisulfite-modified DNA of brain tissue and leukocytes
confirmed that the NPTX2 promoter in all 3 fragments had only single
63
scattered methylated CpG dinucleotides. For the analysis of bisulfite
sequencing in glioblastoma samples, we chose glioblastoma samples with a
different expression profile: samples that showed the expression of cDNA
and samples with low cDNA expression. Dense promoter methylation was
observed in glioblastoma samples with low cDNA expression. Of note, the
NPTX2 gene promoters were unmethylated in glioblastoma samples, which
showed high expression of cDNA. These results show an agreement
between the promoter methylation and the expression status for this gene.
5.2.2. AREG bisulfite sequencing
The AREG CpG island is located at exon 1. The sequence of the
promoter region with primer binding sites the location of the exon are
shown in Annex. Figure 5.2.2.1 shows the CpG island of the AREG
promoter and the fragment of bisulfite sequencing.
Figure 5.2.2.1. The CpG island of the AREG promoter and the location of
bisulfite sequencing fragment with the number of CpG dinucleotides
For AREG, the fragment sequenced at the transcription start site was
412 bp in size and covered 45 CpG dinucleotides. The results are shown in
Figure 5.2.2.2.
Figure 5.2.2.2. Results of bisulfite sequencing of the AREG CpG islands in
brain DNA
NB1 and NB2 indicate different brain samples. The numbers at the top of the figure
indicate the numbers of CpG dinucleotides within the fragment. Black, methylated CpG
dinucleotide, white, unmethylated.
64
The analysis of bisulfite-modified DNA of brain tissue confirmed that the
AREG gene promoter had only single scattered methylated CpG
dinucleotides (Figure 5.2.2.2). For the analysis of bisulfite sequencing in
glioblastoma samples, we chose glioblastoma samples with a different
expression profile: samples that showed the expression of cDNA and
samples with low cDNA expression (Figure 5.2.2.3). Dense promoter
methylation was observed in glioblastoma samples with low cDNA
expression. The AREG promoter was unmethylated in the glioblastoma
samples, which showed high expression of cDNA. These results show an
agreement between the promoter methylation and the expression status for
this gene.
Figure 5.2.2.3. Results of bisulfite sequencing of the AREG CpG islands in
glioblastoma DNA
25290, 23480, 22164, 28590, and 24060 indicate glioblastoma samples. The numbers at the
top of the figure indicate the numbers of CpG dinucleotides within the fragment. Black,
methylated CpG dinucleotide, white, unmethylated.
5.2.3. SPINT1 bisulphite sequencing
The region of SPINT1 gene promoter with CpG islands is located on
chromosome 15q15.1. The sequence of the promoter region with primer
65
binding sites is shown in Annex. Figure 5.2.3.1 shows the CpG island of the
SPINT1 promoter and the fragment of bisulfite sequencing
Figure 5.2.3.1. The CpG island of the SPINT1 promoter and the location of
bisulfite sequencing fragment with the number of CpG dinucleotides
One fragment of the SPINT1 gene promoter was sequenced at the
transcription start site. It is a 344-bp fragment that covers 40 CpG
dinucleotides. First, 2 brain DNA samples were sequenced. The results of
bisulfite sequencing are shown in Figure 5.2.3.2.
Figure 5.2.3.2. Results of bisulfite sequencing of the SPINT1 gene promoter
in brain DNA
NB1 and NB2 indicate different brain samples. The numbers at the top of the figure
indicate the numbers of CpG dinucleotides within the fragment. Black, methylated CpG
dinucleotide, white, unmethylated.
The analysis of bisulfite-modified DNA of brain tissue samples revealed
that the SPINT1 gene promoter had only single scattered methylated CpG
dinucleotides in healthy brain samples. The absence of methylation was
associated with the high SPINT1 expression in normal human brain samples.
The same analysis with different glioblastoma samples was performed. The
results are presented in Figure 5.2.3.3.
As can be seen from the results of bisulfite sequencing of glioblastoma
samples, the methylation of the SPINT1 gene promoter was not a frequent
event. The glioblastoma samples with the high SPINT1 expression did not
showed methylation. The glioblastoma samples with the low SPINT1
66
expression accompanied by promoter methylation were observed, but not
very frequently. Therefore, our results encourage a more detailed analysis of
the entire sequence of the SPINT1 gene promoter in a larger group of glioma
samples to clarify the true relationship between promoter methylation and
gene expression.
Figure 5.2.3.3. Results of bisulfite sequencing of the SPINT1 CpG islands in
glioblastoma DNA
23480, 28590, 26882, and 31118 indicate glioblastoma samples. The numbers at the top of
the figure indicate the numbers of CpG dinucleotides within the fragment. Black,
methylated CpG dinucleotide, white, unmethylated.
5.2.4. COX7A1 bisulfite sequencing
The region of the COX7A1 gene promoter with CpG islands is located on
chromosome 19q13.1. The sequence of the promoter region with primer
binding sites is shown in Annex. Figure 5.2.4.1 shows the CpG island of the
COX7A1 promoter and the fragments of bisulfite sequencing.
67
Figure 5.2.4.1. The CpG island of the COX7A1 promoter and the location
of bisulfite sequencing fragments with the number of CpG dinucleotides
Two fragments of the COX7A1 gene promoter were sequenced. The first
fragment was 309 bp in size that covered 27 CpG dinucleotides; the second
fragment was 349 bp in size and harbored 17 CpG dinucleotides. The
bisulfite sequencing results of these 2 fragments are shown in Figures
5.2.4.2 and 5.2.4.3.
Figure 5.2.4.2. Results of bisulfite sequencing of the first fragment of the
COX7A1 promoter in brain DNA
NB1 and NB2 indicate different brain samples. The numbers at the top of the figure
indicate the numbers of CpG dinucleotides within the fragment. Black, methylated CpG
dinucleotide, white, unmethylated.
Figure 5.2.4.3. Results of bisulfite sequencing of the second fragment of the
COX7A1 promoter in brain DNA
NB1 and NB2 indicate different brain samples. The numbers at the top of the figure
indicate the numbers of CpG dinucleotides within the fragment. Black, methylated CpG
dinucleotide, white, unmethylated.
68
Two fragments of the COX7A1 gene promoter exhibited almost complete
methylation of all analyzed sequence clones derived from normal human
brain samples. It was decided to investigate the methylation status among
different glioblastoma samples. The results of bisulfite sequencing are
shown in Figure 5.2.4.4.
Figure 5.2.4.4. Results of bisulfite sequencing of the first fragment of the
COX7A1 promoter in glioblastoma DNA
22164 and 26882 indicate glioblastoma samples. The numbers at the top of the figure
indicate the numbers of CpG dinucleotides within the fragment. Black, methylated CpG
dinucleotide, white, unmethylated.
The results showed that the CpG islands of the COX7A1 gene promoter
were abundantly methylated in both glioblastoma samples and normal
human brain tissue samples, independent of its gene expression level.
5.2.5. KRT81 bisulphite sequencing
The region of the KRT81 gene promoter with CpG islands is located on
chromosome 12q13. The sequence of the promoter region with primer
binding sites is shown in Annex. Figure 5.2.5.1 shows the CpG island of the
KRT81 promoter and the fragments of bisulfite sequencing.
Figure 5.2.5.1. The CpG island of the KRT81 promoter and the location of
bisulfite sequencing fragments with the number of CpG dinucleotides
69
Two fragments of the KRT81 gene promoter were sequenced at the
transcription start site. The first fragment was 324 bp in size and had 26
CpG dinucleotides. The second fragment was 175 bp in size and harbored
11 CpG dinucleotides. Human brain and leukocyte DNA samples were
sequenced. The bisulfite sequencing results of both fragments are shown in
Figures 5.2.5.2 and 5.2.5.3.
Figure 5.2.5.2. Results of bisulfite sequencing of the first fragment of the
KRT81 promoter in leukocyte and brain DNA
L1 and L2 indicate different samples of leukocytes; NB1 and NB2, different brain samples.
The numbers at the top of the figure indicate the numbers of CpG dinucleotides within the
fragment. Black, methylated CpG dinucleotide, white, unmethylated.
Figure 5.2.5.3. Results of bisulfite sequencing of the second fragment of the
KRT81 promoter in leukocyte and brain DNA
L1 and L2 indicate different samples of leukocytes; NB1 and NB2, different brain samples.
The numbers at the top of the figure indicate the numbers of CpG dinucleotides within the
fragment. Black, methylated CpG dinucleotide, white, unmethylated.
70
The results showed that the KRT81 gene promoter was frequently
methylated in leukocyte and normal human brain samples. Bisulfite
sequencing of this gene promoter in glioblastoma samples was not
performed as we did not expect that the data could be informative and
suitable for future research. These findings suggest that the expression of
KRT81 is not regulated by promoter methylation in glioblastomas.
Summing up the results of gene expression and sequencing, we can
conclude that NPTX2 and AREG were found to be the most promising
molecular markers. The data on these genes and their role in gliomagenesis
are scarce in the literature. Our data suggest a role of NPTX2 and AREG at
least in our subset of glioblastomas. The analysis of bisulfite sequencing
showed that glioblastoma samples with low NTPX2 and AREG mRNA
expression were highly methylated as compared with normal brain and
glioblastoma samples with normal mRNA expression. From these data, it
was assumed that mRNA expression was associated with promoter
methylation.
5.3. Methylation-specific polymerase chain reaction of AREG and
NPTX2
Methylation-specific polymerase chain reaction (MSP) is one of the most
commonly used methods for the evaluation of a large number of DNA
samples. The methylation frequency of the NPTX2 and AREG gene
promoter was evaluated in a series of 22 glioblastoma specimens (Table
5.3.1.).
The primer design for these assays was based on the results of bisulfite
sequencing and incorporated 2 CpG dinucleotides that were regularly
methylated in glioblastoma tumor tissues and unmethylated in the normal
adult human brain. The methylation status of the NPTX2 and AREG
promoters was evaluated in 22 glioblastomas. NTPX2 promoter methylation
was detected in 45.5% (10/22) of the glioblastoma samples, but not in the
normal brain specimens.
AREG promoter methylation was also detected in 45.5% (10/22) of the
glioblastoma samples, but not in the normal brain specimens. It was decided
to investigate the methylation of these genes in a larger cohort of patients
(n=100) and to find the association between genes methylation, patients’
clinical variables, and overall survival time.
71
Table 5.3.1. Summary of NPTX2 and AREG gene methylation and
expression in control brain and glioblastoma samples
No.
1
Sample
FB
AB
GBM
NPTX2
Methylation
0
0
1
AREG
Expression
+
+
+
Methylation
0
0
1
2 GBM
1
+
3 GBM
0
+
4 GBM
0
+
5 GBM
1
–
6 GBM
1
–
7 GBM
0
+
8 GBM
0
–
9 GBM
1
–
10 GBM
0
+
11 GBM
1
–
12 GBM
1
+
13 GBM
0
+
14 GBM
1
–
15 GBM
0
+
16 GBM
1
+
17 GBM
1
–
18 GBM
0
+
19 GBM
0
–
20 GBM
0
–
21 GBM
0
–
22 GBM
0
–
1, presence of gene promoter methylation; 0, absence
gene was expressed; –, gene was not expressed.
Expression
+
+
+
1
+
0
–
0
–
1
–
1
–
0
+
1
–
0
+
0
+
0
+
1
+
1
–
0
+
0
–
1
–
1
–
0
+
0
+
1
–
0
+
0
–
of gene promoter methylation; +,
5.4. Characteristics of the study population tested for methylation of 11
genes
The data of 100 patients with glioblastoma were analyzed in this study.
The median patients’ age at diagnosis was 61.0 years (SD, 1.2; range, 2688). The male-to-female ratio was 1:1.3. The median age of male (n=43)
and female (n=57) patients was 58.0 years (SD, 1.8; range, 34-84) and 61.0
years (SD, 1.6; range, 26-88), respectively.
The mean survival time of patients was 14.6 months (SD, 16.04; range,
0.20-88.4), while the median survival time was 8.9 months (95% CI, 6.9 to
10.8). More than half (64%) of glioblastoma patients survived less than 12
months after the operation, and the probability to survive longer than 12 and
72
24 months after operation was 36% (95% CI, 26.6%-46.2%) and 21% (95%
CI, 13.5%-30.3%), respectively. When the patients were divided into the
groups by age (<60 and ≥60 years), Kaplan-Meier survival analysis showed
a significant difference between groups with a median survival time of 14.1
months (95% CI, 0.0-29.2) and 4.6 months (95% CI, 4.2-5.0), respectively
(P=0.001). The probability to survive more than 12 and 24 months after the
operation in the patients aged less than 60 years versus patients aged more
60 years was 58.7% (95% CI, 43.2%-73.0%) and 41.3% (95% CI, 27.0%56.8%) versus 16.7% (95% CI, 7.9%-29.3%) and 3.7% (95% CI, 0.5%12.7%), respectively.
The clinical variables were evaluated for a prognostic value separately
for age and multifocality using Kaplan-Meier analysis (Figure 5.4.1). The
patients younger than 60 years demonstrated much better survival as
compared with patients older than 60 years (P<0.0001). The overall survival
of the patients with multifocal glioblastomas was worse as compared with
the patients with nonmultifocal glioblastomas (P=0.007).
Figure 5.4.1. Overall survival of patients with glioblastoma according to
age (>60 versus ≤60 years) (top) and tumor multifocality (0, one focus; 1,
two and more foci) (bottom)
73
5.5. Gene methylation and its association with clinical variables and
survival
5.5.1. AREG methylation
Laffaire et al. (6) observed the methylation of the AREG gene promoter
in 52% of low-grade gliomas (grade II oligodendrogliomas, grade II
astrocytomas). However, there are no data on gene methylation in
glioblastomas in the literature so far. To our knowledge, we were the first to
show a 58.6% methylation frequency of the AREG gene promoter in
glioblastomas (58/99). The representative electrophoresis gel demonstrating
the methylation of the AREG gene promoter in glioblastoma specimens is
shown in Figure 5.5.1.
AREG
Figure 5.5.1.1. Methylation status of the AREG CpG island promoter in
glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
The methylation frequencies of the AREG gene according to the patients’
characteristics, tumor multifocality, and survival are shown in Table 5.5.1.1.
The methylation profile of this gene promoter was significantly associated
with 2-year survival (P=0.045). Among 58 AREG-methylated cases, only 8
patients (38.1%) survived longer than 2 years. However, there was no
significant difference between the methylation profile and other variables
(age, gender, and tumor multifocality), although the association with age
was of borderline significance (P=0.065).
The methylation of the AREG gene promoter for a prognostic value of
overall survival was evaluated using Kaplan-Meier analysis (Figure 5.5.1.2).
74
Table 5.5.1.1. Associations between the methylation of AREG gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
58 (58.6)
AREG
Unmethylated
n (%)
41 (41.4)
P
N
99
22 (47.8)
36 (67.9)
24 (52.1)
17 (32.1)
0.065
46
53
23 (54.8)
35 (61.4)
19 (45.2)
22 (38.6)
0.541
42
57
53 (57.6)
5 (71.4)
39 (42.4)
2 (28.6)
0.696
92
7
50 (64.1)
8 (38.1)
28 (35.9)
13 (61.9)
0.045
78
21
Figure 5.5.1.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the AREG gene
75
The analysis showed a significant difference in the overall survival
between patients with the unmethylated and the methylated AREG gene
promoter (P=0.027). The median survival time in the patients with the
unmethylated gene was 12.1 months (95% CI, 9.4 to 14.8) versus 6.3
months (95% CI, 4.4 to 8.2) in the patients with the methylated gene.
5.5.2. CASP8 methylation
The methylation profile of the CASP8 gene promoter was evaluated in
100 glioblastoma specimens. It was aberrantly methylated in 55% (55/100)
of glioblastomas, but not in the control brain specimens. The representative
gel electrophoresis demonstrating the methylation of the CASP8 gene
promoter in glioblastoma specimens is shown in Figure 5.5.2.1.
CASP8
Figure 5.5.2.1. Methylation status of the CASP8 CpG island promoter in
glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
Table 5.5.2.1 shows the methylation frequencies of the CASP8 gene
according to the patients’ characteristics, tumor multifocality, and survival.
The methylation profile of this gene promoter was significantly associated
with patients’ age (P=0.044) (Table 5.5.2.1). Among 55 CASP8-methylated
cases, 20 patients (43.5%) were younger than 60 years and 35 patients
(64.8%) were older than 60 years. However, there was no significant
association between the methylation profile and other variables (gender,
tumor multifocality, and 2-year survival).
The methylation of the CASP8 gene promoter for a prognostic value of
overall survival was evaluated using Kaplan-Meier analysis (Figure
5.5.2.2.).
76
Table 5.5.2.1. Associations between the methylation of the CASP8 gene
promoter and patients’ age and sex, multifocality, and 2-year survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
55 (55.0)
CASP8
Unmethylated
n (%)
45 (45.0)
P
N
100
20 (43.5)
35 (64.8)
26 (56.5)
19 (35.2)
0.044
46
54
25 (58.1)
30 (52.6)
18 (41.9)
27 (47.4)
0.686
43
57
51 (54.8)
4 (57.2)
42 (45.2)
3 (42.8)
1
93
7
47 (59.5)
8 (38.1)
32 (40.5)
13 (61.9)
0.090
79
21
Figure 5.5.2.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the CASP8 gene
77
The methylation of this gene promoter was associated with shorter
survival (long-rank test, P=0.035). The median survival among patients with
the unmethylated CASP8 promoter was 9.9 months (95% CI, 6.4 to 13.4) as
compared with 7.7 months (95% CI, 4.8 to 10.6) among their counterparts
with the methylated CASP8 promoter.
5.5.3. CD81 methylation
Recent glioblastoma methylome studies have shown a CD81 methylation
rate of 54% [2, 3]. In this study, the CD81 gene was aberrantly methylated
in 48% (48/100) of glioblastomas, but not in the control brain specimens.
CD81
Figure 5.5.3.1. Methylation status of the CD81 CpG island promoter in
glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
The methylation frequencies of the CD81 gene according to the patients’
characteristics, tumor multifocality, and survival are shown in Table 5.5.3.1.
The methylation profile of this gene promoter was not significantly
associated with patients’ age and gender, tumor multifocality, and 2-year
survival.
Kaplan-Meier analysis showed that there was no significant differenece
in the overall survival comparing the patients carrying the methylated CD81
gene promoter with those who had the unmethylated CD81 gene promoter
(P=0.25) (Figure 5.5.3.2). The median survival time in the patients with the
unmethylated gene was 8.9 months (95% CI, 7.0-10.8) versus 8.4 months
(95% CI, 4.2-12.5) in patients with the methylated gene.
78
Table 5.5.3.1. Associations between the methylation of the CD81 gene
promoter and patients’ age and sex, multifocality, and 2-year survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
48 (48.0)
CD81
Unmethylated
n (%)
52 (52.0)
P
N
100
22 (47.8)
26 (48.1)
24 (52.2)
28 (51.8)
1
46
54
20 (46.5)
38 (49.1)
23 (53.5)
29 (50.9)
0.842
43
57
45 (48.4)
3 (42.9)
48 (51.6)
4 (57.1)
1
93
7
35 (44.3)
13 (61.9)
44 (55.7)
8 (38.1)
0.219
79
21
Figure 5.5.3.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the CD81 gene
79
5.5.4. DcR1 methylation
The methylation profile of the DcR1gene promoter was evaluated in 98
glioblastoma specimens. It was aberrantly methylated in 27.6% (27/98) of
glioblastomas, but not in the control brain specimens. The representative
electrophoresis gel demonstrating the methylation of the CASP8 gene
promoter in glioblastoma specimens is shown in Figure 5.5.4.1.
DcR1
Figure 5.5.4.1. Methylation status of the DcR1 CpG island promoter in 14
glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB14, glioblastoma
samples, NB, normal brain. The presence of visible PCR products in the lanes marked “U”
indicates the presence of unmethylated genes and “M” indicates the presence of methylated
genes.
The methylation frequencies of the DcR1 gene according to the patients’
characteristics, tumor multifocality, and survival are shown in Table 5.5.4.1.
However, the methylation profile of this gene promoter was not
significantly associated with patients’ age and gender, tumor multifocality,
and 2-year survival.
Kaplan-Meier analysis showed that there was no significant differenece
in the overall survival comparing the patients carrying the methylated DCR1
gene promoter with those who had the unmethylated CD81 gene promoter
(P=0.55) (Figure 5.5.4.2). The median survival time in the patients with the
unmethylated gene was 8.3 months (95% CI, 5.8-10.9) versus 9.7 months
(95% CI, 5.8-13.7) in patients with the methylated gene.
80
Table 5.5.4.1. Associations between the methylation of the DcR1 gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
27 (27.6)
DcR1
Unmethylated
n (%)
71 (72.4)
P
N
98
12 (27.3)
15 (27.8)
32 (72.7)
39 (72.2)
1
44
54
9 (21.4)
18 (32.1)
33 (78.6)
38 (67.9)
0.263
42
56
27 (29.7)
0 (0)
64 (70.3)
7 (100)
0.185
7
91
22 (27.8)
5 (26.3)
57 (72.2)
14 (73.7)
1
79
19
Figure 5.5.4.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the DcR1 gene
81
5.5.5. DR4 methylation
The methylation profile of the DR4 gene promoter was evaluated in 99
glioblastoma specimens. It was aberrantly methylated in 43.4% (43/99) of
glioblastomas, but not in the control brain specimens. The representative
electrophoresis gel demonstrating the methylation of the DR4 gene promoter
in glioblastoma specimens is shown in Figure 5.5.5.1.
DR4
Figure 5.5.5.1. Methylation status of the DR4 CpG island promoter in 15
glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
Table 5.5.5.1 shows the methylation frequencies of the DR4 gene
according to the patients’ characteristics, tumor multifocality, and survival.
The methylation profile of this gene promoter was not significantly
associated with patients’ age and gender, tumor multifocality, and 2-year
survival, although the association between the methylation of the DR4 gene
promoter and 2-year survival was of borderline significance (P=0.079)
(Table 5.5.5.1).
Kaplan-Meier analysis showed that there was no significant differenece
in the overall survival comparing the patients carrying the methylated DR4
gene promoter with those who had the unmethylated DR4 gene promoter
(P=0.165) (Figure 5.5.5.2). The median survival time in the patients with
the unmethylated gene was 9.1 months (95% CI, 6.9-11.3) versus 6.4
months (95% CI, 2.6-10.3) in patients with the methylated gene.
82
Table 5.5.5.1. Associations between the methylation of the DR4 gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
43 (43.4%)
DR4
Unmethylated
n (%)
56 (56.6%)
P
N
99
16 (35.6)
27 (50.0)
29 (64.4)
27 (50.0)
0.161
45
54
21 (48.8)
22 (39.3)
22 (51.2)
34 (60.7)
0.415
43
56
41 (44.6)
2 (28.6)
51 (55.4)
5 (71.4)
0.696
92
7
38 (48.1)
5 (25.0)
41 (51.9)
15 (75.0)
0.079
79
21
Figure 5.5.5.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the DR4 gene
83
5.5.6. GATA4 methylation
The methylation profile of the GATA4 gene promoter was evaluated in 98
glioblastoma specimens. It was aberrantly methylated in 22.5% (22/98) of
glioblastomas, but not in the control brain specimens. The representative
electrophoresis gel demonstrating the methylation of the GATA4 gene
promoter in glioblastoma specimens is shown in Figure 5.5.6.1.
GATA4
Figure 5.5.6.1. Methylation status of GATA4 CpG island promoter in
14 glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB14, glioblastoma
samples; NB, normal brain. The presence of visible PCR products in the lanes marked “U”
indicates the presence of unmethylated genes and “M” indicates the presence of methylated
genes.
The associations between the methylation frequencies of the AREG gene
and patients’ characteristics, tumor multifocality, and survival were
evaluated. The methylation profile of this gene promoter was significantly
associated with patients’ age (P=0.027) (Table 5.5.6.1). Among 22 GATA4methylated cases, 5 patients (23%) were younger than 60 years and 17
patients (77%) were older than 60 years. However, there was no significant
difference between the methylation profile and other variables (gender,
tumor multifocality, and 2-year survival).
The Kaplan-Meier analysis showed no significant association between
the methylation of the GATA4 gene promoter and overall survival in the
patients with glioblastoma (P=0.13) (Figure 5.5.6.2). The median survival
time in the patients with the unmethylated gene was 9.0 months (95% CI,
7.5 to 10.6) versus 4.5 months (95% CI, 0.0 to 9.1) in the patients with the
methylated gene.
84
Table 5.5.6.1. Associations between the methylation of the GATA4 gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
22 (22.5)
GATA4
Unmethylated
n (%)
76 (77.5)
P
N
98
5 (11.4)
17 (31.5)
39 (88.6)
37 (68.5)
0.027
44
54
11 (26.8)
11 (19.3)
30 (73.2)
46 (80.7)
0.464
41
54
20 (22.0)
2 (28.6)
71 (78.0)
5 (71.4)
0.652
91
7
20 (25.6)
2 (10.0)
58 (74.3)
18 (90.0)
0.227
78
20
Figure 5.5.6.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the GATA4 gene
85
5.5.7. GATA6 methylation
The methylation profile of the GATA6 gene promoter was evaluated in
100 glioblastoma specimens. It was aberrantly methylated in 68.0%
(68/100) of glioblastomas, but not in the control brain specimens. The
representative electrophoresis gel demonstrating the methylation of the
GATA6 gene promoter in glioblastoma specimens is shown in Figure
5.5.7.1.
GATA6
Figure 5.5.7.1. Methylation status of the GATA6 CpG island promoter in
15 glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
The methylation frequencies of the GATA6 gene according to the
patients’ characteristics, tumor multifocality, and survival are shown in
Table 5.5.7.1. None of the variables analyzed was significantly associated
the methylation status of this gene promoter.
The Kaplan-Meier analysis showed a significant difference in the overall
survival between patients with the unmethylated and the methylated GATA6
gene promoter (P=0.025) (Figure 5.5.7.2). The median survival time in the
patients with the unmethylated gene was 10.9 months (95% CI, 6.4 to15.6)
versus 6.3 months (95% CI, 3.1 to 9.5) in the patients with the methylated
gene.
86
Table 5.5.7.1. Associations between the methylation of the GATA6 gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
68 (68.0)
GATA6
Unmethylated
n (%)
32 (32.0)
P
N
100
28 (60.9)
40 (74.1)
18 (39.1)
14 (25.9)
0.198
46
54
30 (69.8)
38 (66.7)
13 (30.2)
19 (33.3)
0.830
43
57
62 (66.7)
6 (85.7)
31 (33.3)
1 (14.3)
0.425
93
7
57 (72.2)
11 (52.4)
22 (27.8)
10 (47.6)
0.114
79
21
Figure 5.5.7.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the GATA6 gene
87
5.5.8. hMLH1 methylation
Immunohistochemical studies have recently reported a reduced
expression of hMLH1 in recurrent compared with primary glioblastomas
(53), while hMLH1 gene methylation in gliomas ranges from 0% to 15%.
Fukushima et al. (54) showed a higher methylation frequency of the hMLH1
gene in the patients with anaplastic astrocytomas (21.4%, 3/14) than those
with GBM (14.8%, 4/27). The methylation profile of the hMLH1 gene
promoter was evaluated in 98 glioblastoma specimens. Our study showed
only a 2% methylation frequency (2/98), and this is in line with the data of
the study by Felsberg et al. (53), who did not observe any hMLH1
methylation in glioblastomas. The representative electrophoresis gel
demonstrating the methylation of the hMLH1 gene promoter in glioblastoma
specimens is shown in Figure 5.5.8.1.
hMLH
Figure 5.5.8.1. Methylation status of the hMLH1 CpG island promoter in
15 glioblastoma by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
A low percentage of the methylated cases did not allow us to performed
statistical analysis and check for all possible associations. On the other
hand, both patients with the methylated hMLH1 gene were 61 years old with
much lower as overall median postoperative survival (3.7 and 7.8 months).
Fukushima et al. (54) showed an association between hMLH1 and MGMT
methylation in the same tumor, and in this study, both patients with the
88
methylated hMLH1 gene had the concomitantly methylated MGMT gene
(data not shown).
Kaplan-Meier analysis showed no significant difference in the overall
survival between patients with the unmethylated and the methylated GATA6
gene promoter (P=0.236) (Figure 5.5.8.2). The median survival time in the
patients with the unmethylated gene was 8.9 months (95% CI, 6.9 to 10.9)
versus 3.7 months (95% CI, not applicable) in the patients with the
methylated gene.
Figure 5.5.8.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the hMLH1 gene
5.5.9. NTPX2 methylation
The data on NPTX2 epigenetic silencing in glioblastoma are scarce. In
our study, the methylation profile of the DR4 gene promoter was evaluated
in 98 glioblastoma specimens. To our knowledge, we were the first to show
a 53.1% methylation frequency of the NPTX2 gene promoter (52/98) in
patients with glioblastoma. The representative electrophoresis gel
demonstrating the methylation of the DR4 gene promoter in glioblastoma
specimens is shown in Figure 5.5.9.1.
89
NTPX2
Figure 5.5.9.1. Methylation status of the NTPX2 CpG island promoter in
15 glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
The methylation profile of this gene promoter was significantly
associated with tumor multifocality (P=0.049) (Table 5.5.9.1).
Table 5.5.9.1. Associations between the methylation of the NTPX2 gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Methylated
n (%)
52 (53.1)
NTPX2
Unmethylated
n (%)
46 (46.9)
P
N
98
26 (56.5)
20 (43.5)
0.549
46
26 (50.0)
26 (50.0)
24 (58.5)
28 (49.1)
17 (41.5)
29 (50.9)
0.415
41
57
51 (56.0)
1 (14.3)
40 (44.0)
6 (85.7)
0.049
91
7
42 (54.5)
10 (47.6)
35 (45.5)
11 (52.4)
0.627
77
21
52
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Among 52 NTPX2-methylated cases, only 1 patient (14.3%) had a
multifocal glioblastoma as compared with 51 patients (56.0%) with without
multifocal glioblastomas. No significant associations between the
90
methylation status of the NTPX2 gene promoter and age, gender, and
survival were documented.
Kaplan-Meier analysis showed no significant difference in the overall
survival between patients with the unmethylated and the methylated NPTX2
gene promoter (P=0.554) (Figure 5.5.9.2). The median survival time in the
patients with the unmethylated gene was 8.3 months (95% CI, 5.4 to 11.2)
versus 9.7 months (95% CI, 5.8 to 13.6) in the patients with the methylated
gene.
Figure 5.5.9.2. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the methylation status of the NPTX2 gene
The methylation of the NPTX2 gene promoter was a frequent event in our
series of glioblastomas, but the importance of the gene in glioblastoma
remains to be elucidated.
5.5.10. TES methylation
Some studies showed that TES was more frequently methylated in
glioblastomas (58%-69%) than low-grade gliomas (28%-30%) (2, 6, 7). In
agreement to the previously published studies, we observed a 66.7%
methylation frequency of the TES gene promoter in 99 glioblastoma
specimens. The representative electrophoresis gel demonstrating the
91
methylation of the DR4 gene promoter in glioblastoma specimens is shown
in Figure 5.5.10.1.
TES
Figure 5.5.10.1. Methylation status of the TES CpG island promoter in
15 glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples. The presence of visible PCR products in the lanes marked “U” indicates the
presence of unmethylated genes and “M” indicates the presence of methylated genes.
The methylation frequencies of the TES gene according to the patients’
characteristics, tumor multifocality, and survival are shown in Table
5.5.10.1.
Table 5.5.10.1. Associations between the methylation of the TES gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
66 (66.7)
TES
Unmethylated
n (%)
33 (33.3)
P
N
99
29 (63.0)
37 (69.8)
17 (37.0)
16 (30.2)
0.526
46
53
27 (64.3)
39 (68.4)
15 (35.7)
18 (31.6)
0.673
42
57
61 (66.3)
5 (71.4)
31 (33.7)
2 (28.6)
1
92
7
53 (67.9)
13 (61.9)
25 (32.1)
8 (38.1)
0.611
78
21
92
None of the variables analyzed was significantly associated the
methylation status of this gene promoter (P>0.05).
Kaplan-Meier analysis showed no significant difference in the overall
survival between patients with the unmethylated and the methylated TES
gene promoter (P=0.947) (Figure 5.5.10.2). The median survival time in the
patients with the unmethylated gene was 8.9 months (95% CI, 5.8 to 12.1)
versus 8.4 months (95% CI, 5.9 to 10.8) in the patients with the methylated
gene.
Figure 5.5.10.2. Kaplan-Meier estimation of overall survival in patients
with glioblastoma according to the methylation status of the TES gene
TES methylation needs further analysis to clarify the meaning of such a
high level of epigenetic down-regulation in glioblastoma.
5.5.11. TFPI2 methylation
The methylation profile of the TFPI2 gene promoter was evaluated in 99
glioblastoma specimens. It was aberrantly methylated in 22.2% (22/99) of
glioblastomas, but not in the control brain specimens. The representative
electrophoresis gel demonstrating the methylation of the DR4 gene promoter
in glioblastoma specimens is shown in Figure 5.5.11.1.
93
TFPI2
Figure 5.5.11.1. Methylation status of the TFPI2 CpG island promoter in
14 glioblastoma specimens by the methylation-specific PCR assay
Molecular weight markers are shown on the left. mDNA indicates methylated DNA
control; uDNA, unmethylated DNA control; W, water control; GB1-GB15, glioblastoma
samples; NB, normal brain. The presence of visible PCR products in the lanes marked “U”
indicates the presence of unmethylated genes and “M” indicates the presence of methylated
genes.
The methylation frequencies of the TFPI2 gene according to the patients’
characteristics, tumor multifocality, and survival are shown in Table
5.5.11.1.
Table 5.5.11.1. Associations between the methylation of the TFPI2 gene
promoter and patients’ age and sex, tumor multifocality, and 2-year
survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
Methylated
n (%)
22 (22.2)
TFPI2
Unmethylated
n (%)
77 (77.8)
P
N
99
6 (13.3)
16 (29.6)
39 (86.7)
38 (70.4)
0.057
45
54
11 (26.2)
11 (19.3)
31 (73.8)
46 (80.7)
0.468
42
57
21 (22.8)
1 (14.3)
71 (77.2)
6 (85.7)
1
92
7
21 (26.9)
1 (4.8)
57 (73.1)
20 (95.2)
0.037
78
21
The methylation profile of this gene promoter was significantly
associated with survival (P=0.037) (Table 5.5.11.1). Among 22 TFPI294
methylated cases, only 1 patient (4.8%) survived longer than 2 years. No
significant associations between the methylation status of the TFPI2 gene
promoter and age, gender, and tumor multifocality were documented,
although the association with age was of borderline significance (P=0.057).
These results suggest that the CpG island methylation in the TFPI2 gene
may be an important prognostic factor for glioblastoma. To test this
hypothesis, we evaluated overall survival using Kaplan-Meier analysis. The
analysis showed a significant difference in the overall survival between
patients with the unmethylated and the methylated TFPI2 gene promoter
(P=0.047) (Figure 5.5.11.2). The median survival time in the patients with
the unmethylated gene was 9.6 months (95% CI, 7.3 to 11.9) versus 6.1
months (95% CI, 2.9 to 9.4) in the patients with the methylated gene.
Figure 5.5.11.2. Kaplan-Meier estimation of overall survival in patients
with glioblastoma according to the methylation status of the TFPI2 gene
95
5.6. Gene comethylation and its associations with survival
Further we aimed to evaluate the concomitant methylation of 11 genes
(AREG, CASP8, CD81, DcR1, DR4, GATA4, GATA6, hMLH1, NPTX2,
TES, and TFPI2). A total of 100 glioblastoma samples were ranked in an
ascending order by the number of the methylated genes in them. All results
are summarized in Figure 5.6.1. (The smallest number of the methylated
genes at the top; and the greatest, at the bottom).
The methylation analysis of 11 genes in 100 glioblastomas showed that
the most frequently methylated genes were GATA6 (68%) and TES (66.7%),
while the least frequently methylated gene was hMLH1 (2%).
The overwhelming majority (98%) of the glioblastomas were methylated
in at least 1 of the 11 genes tested. Only 2% of the glioblastomas had no
detectable gene methylation, and 1% had the concomitant methylation of the
10 genes tested. More than half (52%) of the glioblastoma samples had the
comethylation of 4-6 genes.
Moreover, the percentage distribution of glioblastoma samples by the
number of methylated genes in each specimen was evaluated. The results
are depicted in Figure 5.6.2. As can be seen from the figure, the methylation
of 5 and 6 genes was the most common in the glioblastoma samples (20% in
both cases).
Figure 5.6.2. The percentage distribution of glioblastoma specimens by the
number of methylated genes in each glioblastoma sample
96
Figure 5.6.1. Analysis of the methylation profile of 11 genes in 100
glioblastoma samples
The numbers on the right side indicate the number of methylated genes in samples. The
percentages on the bottom indicate the methylation frequency of a particular gene. Reddish
cells indicate the methylated gene; greenish, unmethylated gene; and without color, no data
about the methylation of a particular gene.
97
Next, we aimed to determine the pairs of comethylated genes, as it is
known that the functions of some genes investigated in this study are
interrelated in the cell. In our series of glioblastomas, the following genes
were comethylated: AREG and TES (chi-square test, P=0.021), AREG and
GATA6 (chi-square test, P=0.001), AREG and NPTX2 (chi-square test,
P=0.007), TES and GATA6 (chi-square test, P=0.049), NPTX2 and TES (chisquare test, P=0.006), TFPI2 and GATA4 (chi-square test, P=0.02), TFPI2
and DR4 (chi-square test, P=0.01), and DR4 and GATA6 (chi-square test,
P=0.034). Some pairs of the comethylated genes showed comethylation
associations of borderline significance: CASP8 and DcR1 (chi-square test,
P=0.079), CASP8 and DR4 (chi-square test, P=0.095), AREG and DR4 (chisquare test, P=0.059), TES and DR4 (chi-square test, P=0.075), CD81 and
GATA4 (chi-square test, P=0.076), and NTPX2 and GATA6 (chi-square test,
P=0.088).
Further, we aimed to determine the median survival time of the patients
with glioblastoma by the number of the comethylated genes. The data of a
total of 90 patients were included in this analysis as the data on the
methylation of 1 or more genes were missing in 10 patients. Table 5.6.1
shows that the patients can be divided into 2 groups according to the medial
survival time: patients having 0-4 comethylated genes (0-4 group) and those
with 5-11 comethylated genes (5-11 group).
Table 5.6.1. Median patients’ survival time according to the number of
comethylated genes
No. of comethylated
Median survival
SE
genes
time, months
0
8.9
NA
1
7.6
1.7
2
12.9
5.8
3
8.9
1.1
4
10.8
4.5
5
4.5
0.2
6
9.7
2.0
7
4.1
3.7
8
4.6
2.2
9
20.7
NA
10
7.7
NA
Overall
8.3
0.8
NA, not applicable as there was only the one patient.
98
95% confidence
interval
NA
4.3-10.9
1.6-24.3
6.6-11.1
1.9-19.7
4.1-4.8
5.7-13.6
0.0-11.3
0.2-9.0
NA
NA
6.8-9.8
There were 39 patients (43.3%) in the 0-4 group and 51 patients (56.7%)
in the 5-11 group. The comethylation frequencies in the 0-4 and 5-11 groups
according to the patients’ characteristics, tumor multifocality, and survival
are shown in Table 5.6.2. No significant associations between the
comethylation status and patients’ age and gender, tumor multifocality, and
survival were documented, although the associations between the
comethylation status and patients’ age as well as 2-year survival were of
borderline significance (P=0.090 and P=0.060, respectively)
Table 5.6.2. Associations between the number of the comethylated genes (04 versus 5-11 genes) and patients’ age and sex, tumor multifocality, and 2year survival
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
SUM11
0-4
5-11
comethylated
comethylated
genes
genes
n (%)
n (%)
39 (43.3)
51 (56.7)
P
N
90
21 (53.8)
18 (35.3)
18 (46.2)
33 (64.7)
0.090
39
51
16 (45.7)
23 (41.8)
19 (54.3)
32 (58.2)
0.828
35
55
35 (42.2)
4 (57.1)
48 (57.8)
3 (42.9)
0.461
83
7
28 (38.5)
11 (64.7)
45 (61.6)
6 (35.3)
0.060
73
17
Kaplan-Meier analysis showed that there was no significant difference in
the overall survival comparing the 0-4 group with the 5-11 group (P=0.094)
(Figure 5.6.3). The median survival time in the 0-4 group was 9.6 months
(95% CI, 5.9-13.3) versus 6.3 months (95% CI, 3.1-9.5) in the 5-11 group.
Moreover, Cox proportional hazards analysis reveled that SUM11 did not
have any prognostic value (HR, 1.42; 95% CI, 0.91-2.35; P=0.12).
Considering the data obtained, it was assumed that the methylation of not
all genes could have an impact on overall survival after surgery.
99
Figure 5.6.3. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the comethylation groups (0-4 and 5-11)
Further, it was aimed to identify the combination of genes that could
help prognosticate patients’ survival after surgery and could be used in
diagnostics in clinical practice. Therefore, to analyze further the impact of
the methylation status of the individual gene promoters, a univariate
analysis with the use of the Cox proportional hazards model was performed.
Table 5.6.3. Results of analysis with the Cox proportional hazards model
Gene
DcR1
TFPI2
GATA4
CASP8
CD81
DR4
GATA6
AREG
hMLH1
NTPX2
TES
HR (95% CI)
P value
0.86 (0.53-1.40)
1.67 (1.00-2.77)
1.47 (0.89-2.44)
1.59 (1.03-2.47)
0.78 (0.51-1.19)
1.35 (0.88-2.07)
1.69 (1.06-2.69)
1.63 (1.05-2.51)
2.29 (0.56-9.49)
1.14 (0.74-1.75)
0.99 (0.63-1.54)
0.55
0.05
0.13
0.04
0.25
0.17
0.03
0.03
0.25
0.56
0.95
100
The analysis reveled that the methylation of the following gene
promoters were significant in predicting the overall survival: TFPI2
(HR=1.67; 95% CI, 1.00-2.77; P=0.05), CASP8 (HR=1.59; 95% CI, 1.032.47; P=0.04), GATA6 (HR=1.69; 95% CI, 1.06-2.69; P=0.03) and AREG
(HR=1.63; 95% CI, 1.05-2.51; P=0.03) (Table 5.6.3).
These 4 genes and 2 additional genes (GATA4 and DR4; hazard ratios
P<0.2) were combined in a set of 6 genes (SUM6) and were analyzed more
detailed. Cox proportional hazards analysis showed that SUM6 cases were
more likely to have a shorter survival (HR, 2.2; 95% CI, 1.32-3.68;
P=0.003) indicating that this set of the genes could be a prognostic marker
to identify the outcome of patients with glioblastoma.
The overall survival in patients with glioblastoma according to the
comethylation of these 6 genes is depicted in Figure 5.6.4. Kaplan-Meier
analysis showed a significant difference in the overall survival comparing
the groups with the different number of the comethylated genes (P=0.013).
Figure 5.6.4. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the number of the comethylated genes (AREG,
CASP8, DR4, GATA4, GATA6, and TFPI2)
101
Further, the median survival time of the patients with glioblastoma by the
number of the comethylated genes in a set of 6 genes (SUM6) was
calculated. Table 5.6.4 shows that the patients can be divided into 2 groups
according to the medial survival time: patients having no methylated genes
or only 1 methylated gene (0-1 group) and those with 2-6 comethylated
genes (2-6 group).
Table 5.6.4. Median patients’ survival time according to the number of
comethylated genes in a set of 6 genes ((AREG, CASP8, DR4, GATA4,
GATA6, and TFPI2)
No. of comethylated
genes
0
1
2
3
4
5
6
Overall
Median survival
time, months
14.1
15.7
9.1
6.2
6.4
3.7
7.6
8.9
SE
4.3
3.4
1.1
3.9
2.1
3
5.8
0.9
95% Confidence
Interval
5.6-22.6
9.1- 22.3
7.0-11.1
0.4- 12.1
2.4-10.5
0-9.6
0-18.9
7.1-10.7
The comethylation frequencies in the 0-1 and 2-6 groups according to the
patients’ characteristics, tumor multifocality, and survival are shown in
Table 5.6.5.
Table 5.6.5. Associations between the number of the gene methylated (0-1
versus 2-6 genes) and patients’ age and sex, tumor multifocality, and 2-year
survival
SUM6
Variable
Overall
Age, years
<60
≥60
Gender
Male
Female
Multifocal
No
Yes
Survival, months
<24
≥24
0-1 genes
methylated
n (%)
25 (26.3)
2-6 genes
methylated
n (%)
70 (73.7)
16 (38.1)
9 (17.0)
26 (61.9)
44 (83.0)
0.030
42
53
8 (20.5)
17 (30.5)
31 (79.5)
39 (69.6)
0.350
39
56
24 (27.3)
1 (14.3)
64 (72.3)
6 (85.7)
0.670
88
7
14 (18.4)
11 (57.9)
62 (81.6)
8 (42.1)
0.001
76
19
102
P
N
95
There were 25 patients (26.3%) in the 0-1 group and 70 patients
(73.7%) in the 2-6 group. There were significant associations between the
comethylation status and patients’ age as well as survival (P=0.030 and
P=0.001, respectively).
Kaplan-Meier analysis showed that a significant difference in the overall
survival comparing the 0-1 group with the 2-3 group (P=0.002) (Figure
5.6.5). The median survival time in the 0-1 group was 15.7 months (95% CI,
10.5-20.9) versus 6.4 months (95% CI, 4.2-8.7) in the 2-6 group.
Figure 5.6.5. Kaplan-Meier estimation of overall survival in patients with
glioblastoma according to the comethylation groups (0-1 and 2-6)
103
DISCUSSION
Microarray analysis coupled with the pharmacologic demethylation of
cultured tumor cells is a useful tool for identifying the genome-wide
epigenetic events and has been successfully implemented for this purpose in
other cancer types in the previous studies (134, 135). However, genes
identified by this method have to be necessarily examined more detailed to
validate a potential epigenetic gene regulation and to elucidate their
importance in cancer development. Multiple independent techniques should
be implemented to validate these candidate genes. In our study, the
expression of AREG, COX7A1, KRT81, NPTX2, and SPINT1 were
investigated first in normal human brain specimens. Normal fetal and adult
human brain samples showed robust expression of AREG, COX7A1, KRT81,
NPTX2, and SPINT1 suggesting that the down-regulation of these genes in
glioblastoma samples was tumor related. The up-regulation of their
expression following 5´-aza-dC-mediated DNA demethylation suggested
promoter methylation as a mechanism for gene silencing (7). Surprisingly
partial bisulfite sequencing of the COX7A1 and KRT81 promoters revealed
abundant promoter methylation both in normal human brain and
glioblastoma samples, independent of their individual expression levels. Our
results do not support the classical hypothesis that promoter
hypermethylation leads to gene silencing. They might suggest that the
expression of these genes might follow other mechanisms of regulation than
promoter methylation in gliomas. However, these results might also be
explained by other hypotheses. Some reports have shown that histone
deacetylase inhibitors lead to gene reactivation from hypermethylated
promoters without any changes in DNA methylation at the promoter level or
that the inhibition of SIRT1 reactivates silenced cancer genes without the
loss of promoter DNA hypermethylation (136, 137). Another hypothesis
suggests that there are some transcription factors, i.e. SP1 or NRF1, that are
capable to bind to methylated DNA. COX7A1 and KRT81 transcription
might be regulated by one of these transcription factors, explaining their
expression despite methylated promoter sequences (138, 139). The third
hypothesis relates to the bisulfite sequencing method itself. Bisulfite
sequencing cannot discriminate 5mC from 5hmC. The recent discovery that
3 members of the TET protein family can convert 5mC into 5hmC has
provided a potential mechanism leading to DNA demethylation (8, 44, 46).
We cannot rule out the possibility that in our study not 5mC but rather
5hmC was detected. All these hypotheses have to be addressed in future
investigations in order to elucidate the functional contribution of these genes
to gliomagenesis.
104
The glioblastoma samples with the low SPINT1 expression accompanied
by promoter methylation were observed, but not very frequently. Therefore,
our results encourage a more detailed analysis of the entire sequence of the
SPINT1 gene promoter in a larger cohort of glioma samples to clarify the
true relationship between promoter methylation and gene expression.
Summing up the results of gene expression and sequencing, we can
conclude that NPTX2 and AREG were found to be the most promising
molecular markers. There is no more information about these genes and
their role in gliomagenesis.
The findings of this study suggest a role of NPTX2 and AREG at least in
our subset of glioblastomas. The analysis of bisulfite sequencing showed
that glioblastoma samples with low NTPX2 and AREG mRNA expression
were highly methylated as compared with normal brain and glioblastoma
samples with normal mRNA expression. From these data, it was assumed
that mRNA expression was associated with promoter methylation.
However, the prognostic value of these biomarkers could not be
ascertained, and larger-scale prospective studies are warranted.
Using a MSP reaction, primary glioblastomas were analyzed for the
methylation of 11 gene promoters and its association with patients’ survival.
AREG is a member of the EGF family and a ligand for EGFR (140).
Amphiregulin is known as a bifunctional growth-modulating glycoprotein
acting stimulatory to several human fibroblast cell lines, while being
inhibitory to some neuroblastoma and adenocarcinoma cell lines (141).
AREG can either stimulate or inhibit the growth of lung cancer cells, and
this is dependent on a biological setting (56). Various studies have
highlighted the functional role of AREG in several aspects of tumorigenesis,
including self-sufficiency in generating growth signals, limitless replicative
potential, tissue invasion and metastasis, angiogenesis, and resistance to
apoptosis (57). AREG is associated with the induction of genes involved in
invasion and migration, such as cytokines and matrix metalloproteases
(142). It is overexpressed in various types of human cancer: colon, breast,
pancreas, and lung cancer, and recently, the lack of AREG expression in
GBM cell lines has been shown (142-146). There is a growing body of data
on AREG promoter methylation in various tumors: gastric, high-grade
serous carcinomas, and human bladder tumor cell line T24 (58-60).
Microarray analysis coupled with the pharmacologic demethylation of
glioblastoma cells showed that AREG is one of the novel tumor suppressor
candidate genes (7).
According to Laffaire et al., AREG methylation was observed in 52% of
low-grade gliomas (grade II oligodendrogliomas, grade II astrocytomas) (6).
However, there are no data on gene methylation in glioblastomas in the
105
literature so far. To our knowledge, we were the first to show a 58.6%
methylation frequency of the AREG gene promoter in glioblastomas
(58/99), and gene methylation was significantly associated with older
patient age and shorter postoperative survival. Unmethylated AREG was
associated with significantly prolonged survival as compared with that of
patients with methylated gene. To our knowledge, we were the first to show
an association between AREG gene methylation and poor patients’ outcome
in glioblastoma. Interestingly, the comethylation of AREG with TES and
NPTX2 was a frequent event in glioblastomas; all the genes are functionally
related to regulation of the invasive phenotype of the cell.
The hMLH1 gene is one of the MMR genes. The inactivation of genes
involved in MMR is associated with high microsatellite instability in
various cancers, albeit in sporadic gliomas, typical microsatellite instability
is rare (102, 147). However, the main mechanism in sporadic cases of
colorectal, endometrial, and gastric cancers with microsatellite instability is
transcriptional hMLH1 gene inactivation through its promoter methylation
(148). Methylation at the CpG islands in the promoter sequence and the
resulting loss of expression of the hMLH1 gene were reported in colon (102,
149), gastric cancer (150), and brain gliomas (54, 151). The promoter
methylation of MMR genes plays a causal role in the loss of mismatch
repair functions that modulate cytotoxic pathways in response to DNA
damaging agents (54). Associations between MMR mutations and MGMT
methylation, and resistance to alkylating agent temozolomide that is the
current standard of care for glioblastoma patients have been suggested
(152). Immunohistochemical findings recently reported on reduced
expression of hMLH1 in recurrent compared to primary glioblastomas (53).
The methylation frequency of the hMLH1 gene in gliomas ranges from 0%
to 15% (53, 54). Fukushima et al. showed a higher methylation frequency of
hMLH1 in anaplastic astrocytomas (21.4%, 3/14) as compared with
glioblastoma multiforme (14.8%, 4/27) (54). In line with a study by
Felsberg et al., who did not observe any methylation of hMLH1 in
glioblastomas, our study showed only a 2.0% methylation level in
glioblastomas (2/98) (53). Both patients with the methylated hMLH1 gene
were 61 years old with much lower as overall median postoperative survival
(3.7 and 7.8 months). Fukushima et al. (54) showed an association between
hMLH1 and MGMT methylation in the same tumor, and in this study, both
patients with the methylated hMLH1 gene had the concomitantly methylated
MGMT gene (data not shown). However, there were no significant
relationships between hMLH1 methylation and patients’ survival in our
glioblastoma samples.
106
The NPTX2 gene encodes a member of the family of neuronal pentraxins,
synaptic proteins that are related to C-reactive protein, and has a wide
distribution in various tissues and is normally expressed in the CNS (153).
In vitro data with pancreatic cancer cell lines showed that NPTX2
suppressed tumor cell growth, invasion, and migration and affected
apoptosis (112). Carlson et al. showed increased NPTX2 expression as an
independent negative predictor of survival with the highest levels of edema
in glioblastoma (154). There is a lack of information on NPTX2 methylation
silencing in glioblastoma. For the first time, a NPTX2 methylation
frequency of 53% was determined in glioblastomas, but no significant
associations between the gene methylation status and patients’ age and
gender as well as survival were found.
Recently, the TES gene has been identified as highly methylated in
glioblastomas (2, 6, 7). It was confirmed by the tumor-related methylation
of TES in cultured glioblastomas and glioblastoma cell lines (7). When the
TES gene becomes methylated, the cells loose internetworks, and this allows
them to migrate and tumor infiltrates brain. Gunduz et al. showed an
association between TES down-regulation and worse patients’ outcome in
head and neck squamous cell carcinomas (155). The down-regulation of
TES was shown to be correlated with tumor differentiation and prognosis in
gastric cancer (156), and it was densely methylated in acute lymphoblastic
leukemia (121). It has been shown that TES appears to be more frequently
methylated in glioblastoma (58%-69%) as compared to low-grade glioma
(28%-30%) (2, 6, 7). In agreement to the previous studies, our study showed
a similar TES methylation frequency (66.7%) in glioblastoma. Moreover,
TES and a few other genes were found to be comethylated. However, there
were no significant associations between the methylation status of TES and
patients’ age and gender, tumor multifocality, and survival in this study. The
methylation of the TES gene promoter needs further analysis to clarify the
meaning of such a high level of epigenetic down-regulation in glioblastoma
pathogenesis.
The transcription factor GATA6 with a methylation frequency of 68%
was one of the most frequently methylated genes in our study. GATA6 is
one of 6 members of the mammalian GATA family of transcription factors
that direct cell proliferation, differentiation, and inhibits apoptosis (157).
Recently, it has been identified as a tumor suppressor gene in the CNS
tumors with a gene expression loss of 90% and promoter methylation of
30%-48% in glioblastoma (2, 50-52). It was shown that the loss of GATA6
results in enhanced astrocyte proliferation and transformation (52). Reduced
GATA6 expression was observed in colon and ovarian carcinomas (92, 158).
107
In our series, the methylation status of the GATA6 gene promoter was not
associated with patients’ age and sex or tumor multifocality. Kaplan-Meier
analysis showed a significant association between the methylation of
GATA6 and overall patients’ survival. In agreement with the finding of the
methylome study by Martinez et al. (2), where the importance of GATA6
methylation in patients’ survival has been shown for the first time, our
results suggest that the methylation of GATA6 is a frequent event and is
highly important for survival of patients with glioblastoma.
The CD81 gene promoter was methylated in 48% of the glioblastomas in
our study. This gene is a member of the membrane-embedded tetraspanin
superfamily, which was found to be silenced by methylation in multiple
myeloma cell lines (68). CD81 participates in several functions such as cell
adhesion and signal transduction (159). It was shown that the methylation
frequency of CD81 in glioblastomas was 54% (2, 6). Our study showed no
significant associations between the methylation status of this gene and
patients’ age and gender, tumor multifocality, and survival.
The analysis of the methylation status of proapoptotic genes in
glioblastomas revealed that CASP8 and DR4 were methylated in 55% and
48% of the samples, respectively. DR4 inactivation by promoter methylation
has been previously reported in osteosarcomas (160), melanomas (161),
medulloblastomas (162), gastric carcinomas (6) and glioblastomas (2, 26,
49, 50). The DR4 methylation frequency in glioblastomas varied from 25%
to 70% in different studies. We hypothesize that this variation could be
attributed to the heterogeneity of glioblastomas. Our study showed no
significant associations between the methylation status of this gene and
patients’ age and gender, tumor multifocality, and survival..
In contrast to the findings of Elias et al. (49) and Hervouet et al. (50),
who showed a 10%-30% methylation level of CASP8 in glioblastoma, more
than half of tumors were methylated (55%) in our sample. In addition, we
noted a slight tendency for a relationship between the methylation of 2
TRAIL associated proapoptotic genes CASP8 and DR4 in glioblastoma:
both genes were comethylated in 28.4% (21/74) of the tumors. The latter
finding generally is in line with the findings of Elias et al. (49), who found a
10% DR4 and CASP8 co-methylation level in GBM. It is known that DR4
and CASP8 are factors affecting nonmitochondrial apoptotic pathway, and
the DR4 loss of expression, which was shown to be mediated by promoter
methylation, attenuates apoptosis and is associated with glioma cell
resistance to the pro-apoptotic ligand therapy (known as TRAIL resistance)
(49). The importance of CASP8 in TRAIL resistance in gliomas has been
reported as well (63), while other studies showed CASP8-independent and
DR4-specific TRAIL sensitivity (49).
108
When looking for the associations between the methylation status and
clinical variables, it should be mentioned that CASP8 gene methylation was
associated with patients’ age. Glioblastoma patients with methylated CASP8
were significantly older than those with unmethylated CASP8. As age is
known to play a significant role in patients’ survival, the methylation status
of CASP8 could be of an additive value in tumor genesis and patients’
outcome. Our data showed that the methylation status of GATA6 and
CASP8, but not CD81 and DR4 were independent factors for survival of
patient with glioblastoma.
Similar to the study by Harvouet et al. (50), the associations between
CASP8 gene methylation and survival showed in our study gene
methylation being important factor in worse outcome of patients. The
patients with the unmethylated CASP8 gene, as a favorable genotype,
mostly had prolonged survival as compared with the patients having tumors
with the methylated CASP8 gene. Our data complement the findings of
Martinez et al. (26) on the significance of CASP8 methylation in
glioblastoma progression.
Cox regression analysis revealed CASP8 and GATA6 as the independent
predictors of survival in patients with glioblastoma. Patients with
methylated CASP8 and GATA6 were more likely to have poor survival
(HR=1.59; 95% CI, 1.03-2.47; P=0.04; and HR=1.69; 95% CI, 1.06-2.69;
P=0.03). Contrary to our results, Cecener et al. (51) and Hervouet et al. (50)
reported no associations between the methylation status of GATA6 and
CASP8 and survival in glioblastoma patients.
The loss of GATA4 expression due to promoter hypermethylation has
been reported in primary colorectal, gastric, esophageal, lung, and ovarian
cancer (163, 164). To analyze the potential of GATA4 as a methylation
marker in glioblastoma, we analyzed a large series of glioblastoma samples
and showed that in glioblastoma, GATA4 was not so abundantly methylated
(23.2%) when compared with colorectal cancer (70%) (163) or sporadic
gastric carcinomas (53.8%) (164). The loss of GATA4 protein expression
was observed in about 57.7% of GBM samples (94). In our study, GATA4
methylation was detected only in 23.2% of cases, and this may suggest that
the GATA4 expression is regulated by different mechanisms or mutations.
Probably for this reason, GATA4 promoter methylation was not found to be
significantly associated with survival in comparison with the results
obtained in a study by Agnihotri et al. (94).
The members of the tumor necrosis factor receptor or death receptor
family were found to be frequently methylated in glioblastoma:
TNFRSF10D promoter was methylated in 100% (6) and TNFRSF10A in
68% (2) of glioblastomas. The methylation of the DcR1 gene promoter was
109
determined in 21% of low-grade gliomas (WHO grade II) (6). The data on
DcR1 promoter methylation in glioblastoma are scarce. A study by Martinez
et al. involving 16 glioblastoma patients did not reported the methylation of
the DcR1 promoter (26).
We have assumed that this percentage should be much higher in
glioblastomas. Our study found that DcR1 was methylated in 27.6% of
glioblastoma samples, but not in the normal brain specimens, which implies
that the epigenetic silencing of the DcR1 gene promoter might be involved
in gliomagenesis. This percentage is close to a 37% methylation level in
lung cancer cell lines or 30.7% in ovarian tumors and differs from 78.0% in
prostate cancer (83, 165, 166). These findings suggest that the prognostic
value of DcR1promoter methylation could be tissue specific. Although the
DcR1 gene promoter was found to be methylated in our series of
glioblastomas, the methylation profile was not associated with patients’ sex
and age, tumor multifocality, or survival.
The ability of glioblastoma cells to infiltrate the surrounding brain
parenchyma critically limits the effectiveness of current treatments
(167).Tumor invasion is a complex, multistep process, and the mechanisms
resulting in the degradation of the ECM and tumor cell migration and
invasion have not been completely understood yet (129). The matrix
degradation can be promoted by the imbalance between proteolytic enzymes
(proteases) and their inhibitors (168, 169). Additional studies have shown
that the tissue factor pathway inhibitor 2 inhibits tumor-related angiogenesis
and some members present in the ECM therefore implicates tumor invasion
and progression (170). It was shown previously that TFPI-2, a broad range
proteinase inhibitor, is highly expressed in low-grade gliomas, but,
minimally expressed or undetectable in glioblastomas, and that the enforced
expression of this gene reduces the invasive properties of brain tumor cells
(171).
The aberrant methylation of TFPI-2 promoter CpG islands in human
cancers and cancer cell lines was widely documented to be responsible for
the diminished expression of mRNA encoding TFPI-2 and decreased or
inhibited synthesis of TFPI-2 protein during cancer progression (172).
The transcriptional silencing of TFPI-2 by hypermethylation in the
promoter region has recently been demonstrated in many kinds of human
cancers: TFPI-2 promoter was methylated in 88.6% (62/70) of
nasopharyngeal carcinoma primary tumors (129), 93% (51/55) of colorectal
tumor samples (173), 27.1% (36/133) of non–small cell lung cancer (174),
and 18% (7/38) of primary gastric carcinomas (173, 175). The aberrant
methylation of TFPI-2 was also detected in 73% (102/140) of pancreatic
110
cancer xenografts and primary pancreatic adenocarcinomas and was more
likely in older patients with pancreatic cancer (176).
However, to our knowledge, the methylation status of the TFPI-2 gene
has not been investigated in glioblastoma yet. For the first time, we
examined the methylation status of TFPI-2 in the tumor DNA of patients
with glioblastoma. In this study, the methylation of the TFPI-2 gene
promoter was detected in 22 of the 99 glioblastoma samples, but not in the
normal brain specimens, which implies that the epigenetic silencing of the
TFPI-2 pathway might be involved in gliomagenesis. Although the
hypermethylation of the TFPI-2 promoter was frequently found in
glioblastoma, it was not associated with patients’ sex and age or tumor
multifocality in our series. Regarding patients’ outcome, a statistically
significant association was found between the methylation of TFPI-2 and
overall survival of patients. In our set of glioblastoma patients, the
methylation of the TFPI-2 gene promoter was detected in 4.7% of long-term
survivors with glioblastoma and in 27% of short-term survivors. The overall
methylation frequency of this gene in our glioblastoma series was 22.2%, a
percentage that is similar as reported in primary gastric carcinomas and lung
cancers, but much lower than in nasopharyngeal carcinomas or colorectal
tumors (129, 173-175). These findings suggest that the prognostic value of
the promoter hypermethylation of TFPI-2 could be tissue-specific.
Therefore, such findings may suggest TFPI-2 methylation as a molecular
marker for glioblastoma. The restoration of TFPI-2 expression in tumor
tissues inhibits invasion, tumor growth, and metastasis, which creates a
novel possibility of cancer patient treatment (172).
DNA methylation has shown promise as a potential biomarker for early
detection, survival prognosis, or prediction of therapy response in a variety
of malignancies, including glioblastoma. However, in recent years, a
methylator phenotype based on concurrently methylated multiple tumor
suppressor genes, also called the CIMP, is being considered to have a
greater clinical value than a single gene methylation (177). Numerous
studies have suggested that CIMP status might be associated with
progression, recurrence, as well as long-term survival in different types of
cancer, such as non–small cell lung cancer (178), acute lymphoblastic
leukemia (179), neuroblastoma (180) or colon cancer (181). In
glioblastoma, Noushmehr et al. detected a panel of CIMP including an 8gene signature (ANKRD43, HFE, MAL, LGALS3, FAS-1, FAS-2, RHO-F,
and DOCK5) and found that patients with CIMP tumors were younger at the
time of diagnosis and experienced a significantly improved outcome (8).
These CIMP tumors are a subclass of the GBM proneural subtype defined
by Phillips et al. and Verhaak et al. (182, 183). They were shown to be
111
associated with secondary and recurrent GBMs, IDH1 somatic mutation,
younger age at diagnosis, and longer survival (5). Etcheverry et al.
identified 6 CpG sites associated with overall survival; they were located in
SOX10, FNDC3B, TBX3, DGKI, and FSD1 gene promoters (5).
However, studies with many more cases will be needed to carefully
elucidate the effects of CIMP status on survival and the most reliable panel
of CIMP markers remains to be determined. Future studies are needed
including prognosis as a landmark to identify these markers.
In this study, we identified and characterized a distinct molecular
subgroup in human glioblastomas. In our study, 6 genes (AREG, CASP8,
DR4, GATA4, GATA6, and TFPI2) associated with overall survival were
identified. The association between the concomitant methylation of 6 genes
and patients’ survival showed a more significant effect compared with the
analysis of single methylated genes. In our study, we have shown that not
only methylation of an individual gene, but also comethylation of multiple
genes predicts a poor prognosis in glioblastoma. Our data indicate that
glioblastomas could be divided into 2 distinct subgroups with different
molecular and clinical phenotypes. This molecular classification could have
implications for different therapeutic strategies individually tailored to each
patient with glioblastoma. This finding also raises the distinct possibility
that there could be more than one type of a methylator phenotype in
glioblastoma. Further studies are needed to confirm our data in a larger
series of glioblastomas.
112
CONCLUSIONS
1. A decreased expression of AREG, COX7A1, KRT81, NPTX2, and
SPINT1 genes in glioblastoma tissue sample in comparison with the
expression of these genes in control brain samples indicates a
potential role of these genes in gliomagenesis.
2. The expression of AREG and NTPX2 genes was found to be
associated with their methylation status, while differences in the
expression of COX7A1, KRT81, and SPINT1 genes were not related
to their methylation status.
3. The investigation of 11 new epigenetic markers (AREG, CASP8,
CD81, DcR1, DR4, GATA4, GATA6, hMLH1, NPTX2, and TFPI2
TES) in 100 glioblastoma samples showed tumor heterogeneity and
different frequency of the promoter methylation of the analyzed
genes: AREG, 59%; CASP8, 55%; CD81, 48%; DcR1, 28%; DR4,
43%; GATA4, 23%; GATA6, 68%; hMLH1, 2%; NPTX2, 53%; TES,
67%; and TFPI2, 22%.
4. CASP8 and GATA4 gene promoter methylation was found to be
significantly associated with older age of patients with glioblastoma.
The association between AREG and TFPI2 gene promoter
methylation and patients’ age was of borderline significance. No
association between the methylation status of CD81, DcR1, DR4,
GATA6, hMLH1, NPTX2, and TES genes and patients’ age was
found. The methylation status of none of these analyzed genes was
significantly associated with gender. The methylation status of only
one gene – NPTX2 – was significantly associated with tumor
multifocality.
5. The methylation status of the following 4 gene promoters was
significantly related to patients’ survival after surgery: AREG,
CASP8, GATA6, and TFPI2. Identification of the methylation status
of these genes could be one of the objective criteria in the prognosis
of disease course in patients with glioblastoma and could supplement
the list of already known epigenetic markers.
6. The methylation status of a combination of 6 genes (AREG, CASP8,
DR4, GATA4, GATA6, and TFPI2) was found to be a more accurate
independent prognostic factor for patients’ survival after surgery as
compared with the methylation status of individual genes. The
molecular classification of glioblastomas according to the
methylation profile of a combination of these 6 genes could help
clinicians tailor an appropriate treatment strategy.
113
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LIST OF PUBLICATIONS
1. Vaitkienė, Paulina; Skiriutė, Daina; Skauminas, Kęstutis;
Tamašauskas, Arimantas. GATA4 and DcR1 methylation in
glioblastomas // Diagnostic pathology [electronic resource].
[London] : BioMed Central. ISSN 1746-1596. 2013, vol. 8, no. 1, 1
<http://www.ncbi.nlm.nih.gov/pubmed/23320456>. [ISI Master
journal list; MEDLINE; CAS; Index Copernicus; Scopus]. [Citav.
rod.: 1,638 (2011)]
2. Vaitkienė, Paulina; Skiriutė, Daina; Skauminas, Kęstutis;
Tamašauskas, Arimantas. Associations between TFPI-2 methylation
and poor prognosis in glioblastomas // Medicina. Kaunas : Lietuvos
sveikatos mokslų universitetas. ISSN 1010-660X. 2012, t. 48, Nr. 7,
p. 345-349 : pav, lent. <http://medicina.kmu.lt/1207/1207-03e.pdf>.
[Science Citation Index Expanded (Web of Science); MEDLINE;
Index Copernicus; DOAJ]. [Citav. rod.: 0,423 (2011)]
3. Skiriutė, Daina; Vaitkienė, Paulina; Šaferis, Viktoras; Ašmonienė,
Virginija; Skauminas, Kęstutis; Deltuva, Vytenis Pranas;
Tamašauskas, Arimantas. MGMT, GATA6, CD81, DR4, and
CASP8 gene promoter methylation in glioblastoma // BMC cancer
[electronic resource]. London : BioMed Central Ltd. (Research
article). ISSN 1471-2407. 2012, vol. 12, 1 skelb. (218).
<http://www.ncbi.nlm.nih.gov/pubmed/22672670>.
[Science
Citation Index Expanded (Web of Science); MEDLINE; CAS;
SCOPUS; Google Scholar; EMBASE]. [Citav. rod.: 3,011 (2011)]
4. Vaitkienė, Paulina; Skiriutė, Daina; Mueller, Wolf. DNA
methylation and gene expression of COX7A1, SPINT1 and KRT81
in glioblastoma and brain tissue // Biologija = Biology. Vilnius :
Lietuvos mokslų akademijos leidykla. ISSN 1392-0146. 2012, t. 58,
Nr. 2, p. 71-78 : pav, lent. [ISI Master Journal List; CABI
Publishing; USDA Database; VINITI; EBSCO].
129
Other publications
1. Steponaitis, Giedrius; Skiriutė, Daina; Vaitkienė, Paulina;
Tamašauskas, Arimantas. Activity of MGMT gene in Glioblastoma
as a predictive marker for treatment with alkylating agents.
Biomedical engineering - 2012: Proceedings of international
conference: 25, 26 October 2012: 90 metų KTU/Anniversary of
KTU - 90 / Kaunas University of Technology. [Lithuanian society
for biomedical engineering; Org. committee: A. Lukoševičius, A.
Kriščiukaitis]. Kaunas: Technologija. ISSN 2029-3380. 2012, p. 5255
2. Vaitkienė Grigaitė, Paulina; Skiriutė, Daina; Ašmonienė, Virginija;
Tamašauskas, Arimantas. DNA methylation impact on clinical
outcome of glioblastoma patients. Biomedical engineering - 2011:
Proceedings of International Conference: 27, 28 October 2011 /
Kaunas University of Technology; [Org. committee: A.
Lukoševičius, A. Kriščiukaitis]. Kaunas: Technologija. ISSN 20293380. 2011, p. 201-204
3. Vaitkienė, Paulina; Skiriutė, Daina; Skauminas, Kęstutis; Mueller,
Wolf. Novel discovered targets of aberrant DNA methylation in
glioblastomas. Biomedical engineering: Proceedings of International
Conference: 2007 m. spalio 25, 26 d. / Kauno technologijos
universitetas. Kaunas: Technologija, 2007. ISBN 9789955253679. p.
155-157
4. Vaitkienė, Paulina; Skiriutė, Daina; Steponaitis, Giedrius;
Tamašauskas, Arimantas. Aberrant promoter DNA methylation in
glioblastoma: impact on patient outcome. 5th Baltic congress of
genetics: Kaunas, Lithuania 19-22 October, 2012. Kaunas, 2012. p.
64 (abstracts, poster)
5. Vaitkienė Grigaitė, Paulina; Skiriutė, Daina; Steponaitis, Giedrius;
Ašmonienė, Virginija; Tamašauskas, Arimantas. Concomitant
promoter methylation of multiple genes in glioblastoma. Lietuvos
biochemikų draugijos XII konferencija "Biochemijos studijoms
Lietuvoje - 50 metų": 2012 m. birželio mėn. 28-30 d, Tolieja
(Molėtų raj.) / Lietuvos biochemikų draugija; [Org. komitetas: R.
Daugelavičius (pirm.), ir kt.]. Kaunas: Vytauto Didžiojo universiteto
leidykla, 2012. (Abstract, poster) p. 71, no. PS49, 67.
6. Vaitkienė Grigaitė, Paulina; Skiriutė, Daina; Mueller, Wolf. DNA
methylation and gene expression of COX7A1, SPINT1 and KRT81
in glioblastoma and brain tissue. The Vital nature sign: 6th
International scientific conference: Abstract book, 1st-4th June, 2012
130
/ Vytautas Magnus University. Faculty of natural sciences. Kaunas,
2012. p. 34. And poster
7. Steponaitis, Giedrius; Skiriutė, Daina; Vaitkienė Grigaitė, Paulina;
Tamašauskas, Arimantas. NDRG2 Promoter methylation status and
gene expression level in benign and malignant brain tumors /
Giedrius Steponaitis, Daina Skiriutė, Paulina Vaitkienė, Arimantas
Tamašauskas // The Vital nature sign: 6th International scientific
conference: Abstract book, 1st-4th June, 2012 / Vytautas Magnus
University. Faculty of natural sciences. Kaunas, 2012. p. 25. And
poster
8. Vaitkienė, Paulina; Skiriutė, Daina; Ašmonienė, Virginija;
Steponaitis, Giedrius; Skauminas, Kęstutis; Kazlauskas, Arūnas;
Tamašauskas, Arimantas. Promoter methylation of AREG, MLH1,
HOXA11, NDRG2, NPTX2 and TES genes in glioblastoma. CSC
Symposium Epigenetic Regulation: From Mechanism to
Intervention: London 20–22 June 2012: These Abstracts / MRC Medical Research Council. Imperial College London; Scientific
Organisers: Naill Dillon, Jesus Gil, Ana Pombo, Tim Aitman.
Administration: Odeana Waldron, Tathiana Santana. London:
Imperial College London, 2012. (Posters.). no. 115.
9. Vaitkienė Grigaitė, Paulina; Mueller, Wolf; Skauminas, Kęstutis.
Gene regulation by methylation in glioblastomas. International
Translational Neuroscience Conference: Celebrating the 60th
Anniversary of Neurosurgery in Lithuania and establishing the
Institute of Neurosciences of Lithuanian University of Health
Sciences: Abstract book: 16-17 June 2011, Kaunas, Lithuania /
[Institute for Biomedical Research, Lithuanian University of Health
Sciences. Lithuanian Society of Skull Base Surgeons]. Kaunas:
Published by the PI "Sorre", 2011. (Poster presentations.). ISBN
978-609-95133-6-2. p. 88, no. 71.
10. Skiriutė, Daina; Steponaitis, Giedrius; Vaitkienė Grigaitė, Paulina;
Ašmonienė, Virginija; Tamašauskas, Arimantas. Investigation of
promoter methylation of MGMT and GATA6 genes in Lithuanian
glioblastoma patient’s tumor tissue. International Translational
Neuroscience Conference: Celebrating the 60th Anniversary of
Neurosurgery in Lithuania and establishing the Institute of
Neurosciences of Lithuanian University of Health Sciences: Abstract
book: 16-17 June 2011, Kaunas, Lithuania / [Institute for Biomedical
Research, Lithuanian University of Health Sciences. Lithuanian
Society of Skull Base Surgeons]. Kaunas: Published by the PI
131
"Sorre", 2011. (Poster presentations.). ISBN 978-609-95133-6-2. p.
42, no. 25.
11. Skiriutė, Daina; Vaitkienė Grigaitė, Paulina; Steponaitis, Giedrius;
Ašmonienė, Virginija; Tamašauskas, Arimantas. MGMT, GATA6,
CD81, DR4, CASP8 and NPTX2 gene promoter methylation study
in glioblastomas. 22nd Wilhelm Bernhard Workshop: Program and
Abstracts: August 25-29, 2011, Riga - Latvia / President: Nikolajs
Sjakste; Universty of Latvia. Latvian Institute of Organic Synthesis.
Riga, 2011. (Poster Session II.). p. 114, no. P44.
12. Ašmonienė, Virginija; Skiriutė, Daina; Vaitkienė Grigaitė, Paulina;
Gudinavičienė, Inga; Tamašauskas, Šarūnas; Skauminas, Kęstutis;
Deltuva, Vytenis Pranas; Tamašauskas, Arimantas. Primitive
neuroectodermal tumor (pnet) located in third and fourth ventricles
and frontal lobe: case report of a 51-years-old woman. European
journal of human genetics - ESHG: European Human Genetics
Conference 2009 : Vienna, Austria, May 23-26, 2009 : Abstracts.
London: Nature Publishing Group. (Cancer genetics.). ISSN 10184813. 2009, vol. 17, suppl. 2, May, p. 181-182, no. P06.071 [Indėlis:
0,125]
13. Vaitkienė Grigaitė, Paulina; Mueller, Wolf; Laß, Ulrike; Skauminas,
Kęstutis; Deltuva, Vytenis Pranas; Skiriutė, Daina; Tamašauskas,
Arimantas; Deimling, Andreas. Frequent epigenetic silencing of
NPTXII and AREG in glioblastomas. Second Nordic-Baltic
postgraduate summer-school in cellular bioenergetics „Mitochondria
in neurodegenerative and metabolic diseases: Tartu, June 10-15,
2007. Tartu, 2007. p. 34, no. 2.
14. Vaitkienė Grigaitė, Paulina; Mueller, Wolf; Laß, Ulrike; Deimling
von, Andreas. Frequent epigenetic silencing of NPTXII and AREG
in glioblastomas. Acta Neuropathologica: Joint Meeting of the
German Society of Neuropathology and Neuroanatomy (DGNN) and
the Polish Association of Neuropathologists with International
Participation in Conjunction with the 52nd Annual Meeting of the
German Society of Neuropathology and Neuroanatomy (DGNN) :
Greifswald, Germany, September 05–08, 2007 / President: R. W.
Warzok. Berlin: Springer Verlag. (Abstracts.). ISSN 0001-6322.
2007, vol. 114, no. 3, p. 329, no. 72.
15. Vaitkienė Grigaitė, Paulina; Mueller, Wolf; Laß, Ulrike; Deimling
von, Andreas. Frequent epigenetic silencing of NPTXII and AREG
in glioblastomas. The Baltic Summer School 2007: Inflammation: a
key to common complex diseases: 2-13 September 2007, Lund,
Sweden / Medical Inflammation Research, Department of
132
Experimental Medical Science Medical Faculty, Lund University.
Lund: Medical Inflammation Research, Department of Experimental
Medical Science, Lund University, [2007]. (Posters.). no. P41.
16. Vaitkienė Grigaitė, Paulina; Mueller, Wolf; Laß, Ulrike; Ehrlich,
Matthew; Louis, David N.; Deimling, Andreas. Frequent epigenetic
silencing of NPTXII in glioblastomas. Brain Tumor 2006: Program
and Abstracts (Orals and Posters): December 7/8 2006, Berlin
Berlin: HELIOS Kliniken, 2007. (Posters.). p. 19-20, no. 22.
133
ACKNOWLEDGMENTS
Thank you so much for all that every single one of you did. This study
would not have been possible without the contribution of many competent
and kind persons. Therefore, I would like to acknowledge:
My academic supervisor Prof. Dr. Dainius H. Pauža for accepting me as a
doctoral student. His support and supervision throughout the years.
My academic co-supervisor Prof. Habil. Dr. Arimantas Tamašauskas for
providing me an attractive research project, large resources of patient
material, continuous scientific support, encouragement and faith in my
skills.
I would like to thank the members of Laboratory of Neurooncology and
Genetics of Neuroscience Institute (Lithuanian University of Health
Sciences, Kaunas, Lithuania) for creating a friendly and enjoyable research
environment.
Dr. D. Skiriūtė for her supervision, introduction into the genetics of
complex diseases. Her scientific supervision, support, encouragement
provided the basis of this thesis.
All clinicians team of the Kaunas University of Medicine Hospital,
Department of Neurosurgery, for their expert help.
Patients and their families, who agreed on the use of glioblastoma samples
for academic research, for their cooperation.
I also thank the technical staff at the Institute for Neuropathology, Charité
Humboldt University (Berlin, Germany) for their expert help, pleasant and
warm atmosphere.
And finally, but not least, thanks goes to my whole family, who have been
an important and indispensable source of spiritual support.
134
APPENDIX
NPTX2
NPTX2gene promoter region with CpG islands is located chr7:97890357-97892510
Its sequence is as follows:
TTACAAGGACCGGGCACTGTTGGACCACGTGGCTCCATCATGATGACTCCAGTA
GATGTCACCCCGCCCCTGAGCTCAGGTCTTGCTGAATAAGGTCACCGCCCAGGG
GGCAGTCGATGAACACGCGCGCGAGGGCTCTGCGAGTGGCCTCGTGACTTTGT
CCCTAACTCCGGGTGTCCCCTCCTTCCCATCAGCGTCCGGCGCCTGGTCCTGGT
CCCGGTCCCCGAGGCCCCCGGGATTCTTCCCGAGCGTTTTCCGAGTTGGCGCGG
GGGGTGGAGGCGGGGCCATGGAGCGCGTCCCGGGGACCGTTGCATCCGGAGGC
GGCCGTCGTGCGGCTCCTTCCCGCCTCGAGAGTGAGGTGGCCGGGCCTTGACG
AGAAGGCCCACGCCTGCCGCGGGGGTGGCTCGCGATGGCAGTCGGGGTTCGAG
TCCCGCCTGGGGGGCTGCTCCTGCTGGAGAAAACGCCTCCCTGAGGGCGGCGG
CAAACGCGCAGCGAGGCCCCGTGCCGCGCCAGAAGCCACCCTGAGAAAGGGG
CACCGGGACACCGAGGGGTTCCCACTTTCTCCTCAGCCTGTGACGCCCGCGTCC
TCGGGTGGGTTCGAGGGGCGCCTGGGCACGGCCAGCCGAGGCTCTCGAGAGCC
CCAGTGTCGTTTTCCACCTCAGGCCTCCTTTCCTGAGGCAGAGCCCGGGACCT
CGCGCTCTCGCCTCAGGCTCCGGCCCACGCTCCCGCCCGGCCGCCAGGCGCGCA
ACGGAAAGCGCCCCCGCCCCGCCCCGCTCCGCCCACTGCGTGACGCGCACCCG
GCCGAGCCAATCAGAGCTCGTGGCGCGCGCCCCACACGCCGGCCCCCTCCGCC
CCTCAGCTTAAGAAAGGGCGCGCGGACCCGGCAGGCCAGAGTGCCGAGCAGC
GCGGTGGGTGCGGCTGTGAGACGGCAGGAGACTTCTGCCCCGCGGTGCACGCG
ACCCTCGAGACGACAGCGCGGCTACTGCCAGCAGCGAAGGCGCCTCCCGCGGA
GCGCCCCGACGGCGCCCGCTCGCCCATGCCGAGCTGAGCGCGGCAGCGGCGGC
GGGATGCTGGCGCTGCTGGCCGCCAGCGTGGCGCTCGCCGTGGCCGCTGGGGC
CCAGGACAGCCCGGCGCCCGGTAGCCGCTTCGTGTGCACGGCACTGCCCCCAG
AGGCGGTGCACGCCGGCTGCCCGCTGCCCGCGATGCCCATGCAGGGCGGCGCG
CAGAGTCCCGAGGAGGAGCTGAGGGCCGCGGTGCTGCAGCTGCGCGAGACCGT
CGTGCAGCAGAAGGAGACGCTGGGCGCGCAGCGCGAGGCCATCCGCGAGCTC
ACGGGCAAGCTAGCGCGCTGCGAGGGGCTGGCGGGCGGCAAGGCGCGCGGCG
CGGGGGCCACGGGCAAGGACACTATGGGCGACCTGCCGCGGGACCCCGGCCAC
GTCGTGGAGCAGCTCAGCCGCTCGCTGCAGACCCTCAAGGACCGCCTGGAGAG
CCTCGAGGTAGCGGCCCGCGGGGAGCGCGGGGGACCTGGAATGGGGACGCTCC
CGAGTCGGGGGCGGAAGACTCGGGAGGATGGGGAAAGGGGGCCTGGCCCTGG
GGAGGGTGTGATCGTCCGTGGGGGTGAGCTGGACTTGAGGGTGAAAGGCGGGG
ATCTAGATCCTGCTCGGGAACTCCCCTGCGTGGTATCCCTTCCCACACCGCTGC
TCTTGCTGGAAGGAAACGTTTAAATTCCACCCCCGCGCGTCGGGACTGCCAGCG
GGATCCGCCGAGCACTTCCCGAGGTCCGGGCTAGCGAACCCAGACGGCCAAGC
CGCGGGCGCCAAATACCCGGGGACGCGGTAGCCTCTATCCTCTTGCAAATCTCC
AAATCTCCGCGAGCCGGGATGCGCTCCCGCAGGCCGTGCGGGTTGCTGCGAGG
CTGCCGCGGGAGGTCAGCTGCCCCGGGCATGTGGTGTTTCTTTCTTTAAATCAG
GTCAGGCTGAGCTAGAGGGGGCGGAAGTCTGGGGGCGGAGGTCGCAGGGAGG
CGAGCGCAGGGGGTCACCGGCGACCCCAGAGCGCGCATTAGAGGGGCAACCTC
135
TGGTTAACTTCAAAGACTCCCGTGTCTGCAAGTTGGACCATTTTAAGGTGCTCT
TCCGGATTGCTTCAGGGGAAGGGACCTC
Underlined marker: Bisulfite sequencing primers
AREG
Bold:
Underscore:
CpG-island, 454 bp
exon 1, 271 bp
TTTCAGAGTCAGATGAAGAATTCATATCCACCTGGCTTTGAACATTATCGGCTG
TGAGATGGTGTAGGTAAAATTTTAAGTGCATAATTTGGCAATAATAAATCATCA
ATAAATATTAATGTTGATGAGGCCCCTGGGCCACATAAAGAAATAGGGAGTGA
GGGGATTTGAAATTCTGGCCACTTCACAGAAATGGGTGGGAAGGGGCTCTTGA
TTGAGATAGAAGCCCATCCTACATGAAGCAATTCCTCATTGAGTTCTCTCGTCC
TTTATCCTTGTTGGAAACATCAGGCAAAGTCACTCTTGGTCTTAAAGTACTTTTA
CATCTAAATACGGAACTCTTCTATTTAATCCCTGTCTGTTGTAGATGTTAAGTAT
ACAAAGAGGTTGTCAGAGTTTGAAACATCTGGACTTCTGTCAGGTACTAGCTCC
GGAACTCCAGTCCTGCTCGCCCTCAAAAACGGCTTGCAGCTAGAGGTTTAAGTT
CCACTTCCTCTCAGCGAATCCTTACGCACGAGGGAGGCGGGGCGTGTGTCC
TCCGCGCGTGGTTTTCGGGTAGCACCTTCTGGGGCGCCGCCTGCCTCCAC
CCACGGCCGGGCCTTGACGTCATGGGCTGCGGCCCCCTCCCGGCTGAGCC
TATAAAGCGGCAGGTGCGCGCCGCCCTACAGACGTTCGCACACCTGGGTG
CCAGCGCCCCAGAGGTCCCGGGACAGCCCGAGGCGCCGCGCCCGCCGCC
CCGAGCTCCCCAAGCCTTCGAGAGCGGCGCACACTCCCGGTCTCCACTCG
CTCTTCCAACACCCGCTCGTTTTGGCGGCAGCTCGTGTCCCAGAGACCGA
GTTGCCCCAGAGACCGAGACGCCGCCGCTGCGAAGGACCAATGAGAGCCC
CGCTGCTACCGCCGGCGCCGGTGGTGCTGTCGCTCTTGATACTCGGCTCA
GGTGAGGATTCACCGGCGCTGAACTGCTGGGCTCTCCTCCCATGGCAGGTTTT
GCCTCTTGAGTTTGGCTCTTGCTTCTCTAAAAATCGCCCTTTAGTTCCCCCGACC
CTGCGCCGCAGGAGGTGATCACTGTAGCTCCCTCCCTAGGAGGAAAAGAGAAG
GGCAGATGTCTCAAACGTCCAGGACAAGGTGTCCCAGAGAATCGCTGTACTTTT
TCTTTAGTTCAGCTGAGAAGACGGGAGAGATCATTTGAACCTAACCCTTCGTTT
CACAGATTAAAAGATTAAAAAGACCAAGATCTAGAGAGGTGAAGTGATTTCCG
CAGATATTGGACCCAGCAGTGTTTGCTGCCTCCTGCCTAATTCTCCATCTCTGTC
TAGTGATTTCTTCTTTACCTTCCTAGAGGGACTTAGAGTATCCCGGCGAGAGGT
TAAAAGCACCACTCTGATGCTGTTTTATTGAGACCTAAGAAAAAGCAAGATAA
AGCAGTGGCTTAACTCTGGGCCCCAAATCTTACT
SPINT
Bold:
CpG-island, 1491 bp
Underscore: exon 1; 139 bp
Underscore: exon 2; 540 bp
Underlined marker: Bisulfite sequencing primers
GGATGTTGCAGTTCAGCAAAATCCAAGGGTTGGCCACAGGATCTGCTAGGCAA
AGCTGCTCTGGATGAGAAACTGGGAAGAATGTTCCCATTGGTTTGGGGGTTGTT
AAGCCATCAGGAAAAGGTTTGGAATCTGTATTTTAAGAGAGCATGCTATGTGA
136
TTCCTTCCTCCAGCCCTGCCTTCATCTGTCTTTCACCTGTTCAGACCCTTGCCGT
AGAGAATGGTCTCTTAAGAGTTTTTTACTCAGGAGGAGCAATGAGCTTGGCTTC
AAGGAGTCCACTGTTTCGTGAAGGGCATTAACTCAGCTACTTGGTGAACTGGA
ACCTCCAGGCTTCCAAGGATGACATTTCCCTGGAAGAAAGTAGCCAGCAGCGT
CCCCTTTGTGAAAGAGAGGAAGCCTGGGCTCCAGGGAAGGGGGCAACAGTCCA
ATGAGCCATAGCAGGGAATGGACCGAACTGTACATACAAGATGAGGAGGGGC
CTCGAACAACAGGGAAGCCCCGTGGAGCACGCAGGGGAGCCTGGACGCGG
GGAATCGCCTCCGTTTCTCACCCTGACAGCAGGGAGGGCTTCCCGGAGCT
GACAGTGAGGCAGACATCCCGGATCCCTGTTATCACAGGTGCAGCCACGC
AACTTTTGGGTTACCTTCCCGCCACGAAATGGAAATGATTTCCTGAGGGCC
GGAAAGATCATTTGCATCGGGCCTCAGGTCTGGGGCTGGGAAAGCAGGCC
CCGGCCGGAAAGGAAGACTGGAGGCCGCCGCCCCAGAGGCCGGACCCTC
GCGTCCCGGCCGCATCCCCTCCCGGCTGGGTATGCGCTTCGGGCGCGGGG
CTCCCTCGCGAGGCTGGACTGGGTGGAGCCCGAGAGCCCCTCCGGGGAG
CTGGCTGCCGGGGAGGCACCGGGGCTGCGGGGCCGCGGGGCGGGCCGGG
CCTGGAGCTCCGCGCTAGAGGCGGCCGCAGCGCACCGGGCGTGGGCGGG
GCCGGCAGCGCAAGGGTGAATGTCGGCCCCGCCCCGACGGCGCTGACTCA
GTTTCACCAGAAACCAGGGGGAGAAGGCGGCCGAGCCCCAGCTCTCCGAG
CACCGGGTCGGAAGCCGCGACCCGAGCCGCGCAGGAAGCTGGGACCGGA
ACCTCGGCGGACCCGGCCCCACCCAACTCACCTGCGCAGGTAACCCGGGC
CCCCGCGCGCAAGGCCGAGGCGCAGGCGGAGCGGGCTGGAGCATGTCCG
GCCCTTTGTTCTGCGCTCGTGCGTGTGTCCGGGTACTTGAGCTCCCTAGCG
GTCCGCCTGTCCGTCTGTCTGGCTGCCGGGTCCCTGGAGGGCGCGTGGGA
GGGCCGGGCCCGTGCGCCCTCTTCGATCCTGGGGTGCTCCGGTCCCTCCT
CCCCGCCACTTCCTCCCGGCCGGCCCGCCTCCTCCAAAGTCTCCCGGGCT
GATCAGGTGTGTCTCCTCCTCTGTCCCCTCCCTTCTTCTCAGGTCACCAGC
ACCCTCGGAACCCAGAGGCCCGCGCTCTGAAGGTGACCCCCCTGGGGAGG
AAGGCGATGGCCCCTGCGAGGACGATGGCCCGCGCCCGCCTCGCCCCGG
CCGGCATCCCTGCCGTCGCCTTGTGGCTTCTGTGCACGCTCGGCCTCCAG
GGCACCCAGGCCGGGCCACCGCCCGCGCCCCCTGGGCTGCCCGCGGGAG
CCGACTGCCTGAACAGCTTTACCGCCGGGGTGCCTGGCTTCGTGCTGGAC
ACCAACGCCTCGGTCAGCAACGGAGCTACCTTCCTGGAGTCCCCCACCGT
GCGCCGGGGCTGGGACTGCGTGCGCGCCTGCTGCACCACCCAGAACTGCA
ACTTGGCGCTAGTGGAGCTGCAGCCCGACCGCGGGGAGGACGCCATCGCC
GCCTGCTTCCTCATCAACTGCCTCTACGAGCAGAACTTCGTGTGCAAGTTC
GCGCCCAGGGAGGGCTTCATCAACTACCTCACGAGGGAAGTGTACCGCTC
CTACCGCCAGCTGCGGACCCAGGGCTTTGGAGGTGAGGAGGGTGCCAAGATG
GATGGGTTTGGAGAGACTCAAAAAAGGGACTGGTTATGGGGTCCTAGGGGAGA
CGAACATCAGAGAGGAGTTAAGGAGAGTTCACTTTTTTTTTTCTTTTTCTTTCTT
TTTTTTTGCGACGGAGTCTCGCTCTTGTCGCCCAGGCTGGAGTGCAATGGCGCA
ATCTCTGGTTGCTCACTGCAACCTCCGTCTTCCACGTTCAAGTGATTCTTCTGTC
TCAGCCTCCGGAGTAGCTGGAACTACAGGCACCCGCTACCAGACCCGGCTAAT
TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGGTACAGACGGGGTTTCACTATGTTGG
CCAGGCTGGTCTTGAACTTCTGAACTCAGGTGATCCACCCACCTTGGCCTCCCA
AAGTGCTGGGATTATTTCTACCTGAAGCCCTCCTTAGAAGTAGGCAGTACAGCC
GAATCAAGCCTGGTGATCAATGGGGTGA
137
COX7A1
Bold:
CpG-island,
Underlined marker: Bisulfite sequencing primers
ataccgttttactcccaaagactttagcttgtgtttcctaaaaacaaggtcatttaatgtctcaacaaatctctgagaaagatcctgcctta
gccctcacGTTACAGATACGGAAACAGGCTCGGAGGTGCAATCTCTGGCTGTG
TAGACAGAACAGGGAGTTGGAACACGGCCTTaTGCGTTTCCTCACTCTCAC
ATACCAGCGGGCAGGGAGAGATCCGGGGAGGAGTTGCACTTGCCCCCGG
CGGAAATCTCCAAAAATCTGCAAAAATGTATTCCCTGGTACCGCTTTGGGC
TCCGGTGGGACCAAGCGAAATTCCGAGACCGAAACGGATGCGCGCTGCGG
CCCAGGGTGCGGGTCTGGACCGCCTCCTCCGTGGTGGACGAATCCGAGGA
GCAGGACTCCCCGCAACCCAGCCCCAGCCCCAGCCCCAGCCCCGCCGCGT
CCCCAGCTGTCACGCTGCGCGCAGCGGGTGGGGCCTGGGGTTCCTGGACA
GAGGAGGACTACGCGTGTCCTTGGGCGGAGAAGGGAGGTGACTCCGGCG
GAAGAGGACAAGGCAGAATGCAGGCCCTTCGGgtgaggcccccatgggctgggccggagc
cccccacccagaaaaccccgccctaatacgcgactctccaagtgcccccgcatctagggccttggaacgtgagccaccttc
agggcctgggactccccgctaaacagcctgggtcccttcccccacaatccactcttcggagacaactggggacccccaccc
cggcccaaagccccgggtttgaacttgacatctcgggaacccttaacactggcctcacccgggccttctcaggcagcctgga
cgctcccggggacctccattccggtccccacgttcccgggacctggtttcctccctttaatccaggaaaccctcccccaacaa
aaatctcaggacttaggccccccagaacaaagtccttctgggtcctctcccttccaggaccctctaggcagcccggtcaccc
ccaggccagactccagggcgagtcccctcccaacccagctggcagttcgcgggtaacagaatccaagctattaccctcctc
gcctttgggacgctcgctagggatggggctgtccccacgtccaggcctcagagagcggggtcccacttcaggacacccctc
caggtgcctccaatacccgcccacctcccaccccgcagGTGTCCCAGGCGCTGATCCGCTCCTTC
AGCTCCACCGCCCGGAACCGCTTTCAGAACCGAGTGCGCGAGAAACAGAA
GCTCTTCCAGgtggggggtcggggggggtggggatccgacgcgccagccgggagcgcgccgagccggggcag
gcggggcgcgctctaaggagcagccagcacccctttctcatcagacacccccacatccagGAGGACAATGACA
TCCCGTTGTACCTGAAGGGCGGCATCGTTGACAACATCCTGTACCGAGTG
ACAATGACGCTGTGTCTGGGCGgtgagcgcagggcccgtctgggctgcgggggaggcggggctgga
cccagagtaagaggtggctggtttctgggcaggactgaccaggatctgggttgggggttggcgtttaggaagggggtcgag
tactgagctggagtcaggcctgcagggaggggagtaactagggtttgggctcttgtcccaggacgggggctgggttagtgg
tggggctgaggcccagggaggagtcggggagaaagctcccagcccggtctagatcaaagccagggggctttacaatagg
agtccaatctgggaagggagctggagtccgaactagagctggggtccaggctgggaaccagggagcctgaaggccttaa
aagcctgggatgtccagggaggggattaggtctcattctgtctccaacgcttcccctttctagGCACTGTCTACA
GCTTGTACTCCCTTGGCTGGGCCTCCTTCCCCAGGAATTAAGACCAAGAAG
CCTGGGGGGCCTGAGAGACTTGAACAAGTGTCAATAAACGCTGGCCTCTGt
Two fragments were sequenced:
The first fragment is located from –301 to +8 bp and covers 27 CG dinucleotides.
The second fragment is located from +14 iki +363 and covers 17 CG dinucleotides.
KRTB81
CpG-island
Underlined marker: Bisulfite sequencing primers
ATCTGAGATGAATAGTAAGTGGTGCTTTGGAATTATTGTTAATTTTCCTGAATA
AGAAAATAGTACTCTCCTTATTCTTAAGAAATTCCTGCTGATGTATTTAAGGGT
GACATGCCATGCTTTCTGCCACTCAAATAATTGGGCAACTGTGAAAGCAAATAT
138
GGTATAATGTTAACTGTTGAATCGGGGTAAACAGGTGGAGCATCAATGCATGT
GTGTTGTATTATTCCAATTTTCCTCAATATTTGAAAGCTTTCATCATAAAAAAGA
TAAAAGACAAATATTTAATAATCTGCTGACTGTTCCCATGGTAGGAAAAGTGA
GATGGTTTTCAGCTGCCAACTGAAAAATCTGGAGATGGGGAAATCCAGATTTG
TGTTTCAGGAGCACTTTAGCCACAGCTGTGATGGAAATGAGAGATGGTATTTAT
TAATGCCTCAGCCTTTCCTGAAACCCCTCTCCCCACATCACACTTAACACACAG
GTCTCCTCCTCCCAGTTTCTCCCAGCACCTGGCCTCCCTTCTCTTCCCCTCGCCA
GGCTCTACTCCTCCTTCTCGTCCTCTCCTCTGCCTTCCCCTTCCCTCCCCAATGCC
TGCAGATGAAGGAATGCCCTGCTGGCAAGACACTTTGAAGATGAAACATGCTG
ACTCCCCCAGAGCCCAAGACTGACATCTTTTACAAAGAAGAGGGTGCAGGCCA
CTCCCCCATTAAAGCACATTCTGAGAGGCGTTAGACCCGGCTAACCACCCAAG
CCCATAAAGCGCAAATTGCCCCAACATCATCTTCACAGCCAAGCCCCTTCAGAA
TCTGCGCATAAATAGGGCTGCGGTGCCCTGAGGAGCACATTGGAGTTTCCATCA
GGACTCCAGGTCCCCTATCCTGTCCTCTGCAACCCAAACGTCCAGGAGGATCAT
GACCTGCGGATCAGGATTTGGTGGGCGCGCCTTCAGCTGCATCTCGGCCTGC
GGGCCGCGGCCCGGCCGCTGCTGCATCACCGCCGCCCCCTACCGTGGCAT
CTCCTGCTACCGCGGCCTCACCGGGGGCTTCGGCAGCCACAGCGTGTGCG
GAGGCTTTCGGGCCGGCTCCTGCGGACGCAGCTTCGGCTACCGCTCCGGG
GGCGTGTGCGGGCCCAGTCCCCCATGCATCACCACCGTGTCGGTCAACGA
GAGCCTCCTCACGCCCCTCAACCTGGAGATCGACCCCAACGCGCAGTGCG
TGAAGCAGGAGGAGAAGGAGCAGATCAAGTCCCTCAACAGCAGGTTCGCGGC
CTTCATCGACAAGGTGGGTGTCCTGGATCACACCCTTCCTGAACCCCCACCACC
TACACAGCCAGGGCCGGGCACTAAGGATGGAGTCAGAGGCACAAAGACCTCTG
CCTGCCTGAGATCCCAGTCTGATGGGCGAGGCACACAGACACACAGACAGACA
GACACATGCACAGACACACACACACAGGCACTCAAGATGCAGACTCAGCTGAC
AGTTCAGATAGAGGGAGCTTGTGAAGGAAAAGTGTGGCCAGGCTGCTCTGGGG
CACAAGCTAGACATCAGGGAGTTGGGGGAGGGGGGCATGTGACCACTGCTGCT
GAACTCAGAGGGGTTTGGGCAGCCCCAGGGTGAGAGAGTAGGGTGCACTGACT
TCCAGATGACACAGGGGAAGGGCTGGGACAGCTGAGCTCCCTTCCAGATGGCC
TGTGACACCCTCTTCCTGGCACCATCCCCAGAAACCTCATCTGTGATCTGAACC
TGAGTACTCTGCCTGTGTGGGTTGCTGATCACCTAGTTTCATAGTGAGGGACTG
AGCCTCCACTTCTGTTCATCTGCTTGGGTTGGGGGGGGGGGGTCACTCAACTCC
CCAGGGAGCAGGCACTGACACCACCCAGCCCTTGCTCCACTCCTGAGTGGGAA
ATCTAGACACAGATCATGACCAGGAAATCCAGAAATTGTCTGTCTCCCACTGCC
CACCTCTACTCAATCTCAGATAAGCTCCAGATTTGGGGGTTCCCATTAAAGCAC
CTCCCCTGCGAGCCCCCAGAGCTGGCTTTGGGGCACCAGACACCATCTGCCCCA
CCTTCCCACACCGAATGGCAGGTGCGCTTCCTGGAGCAGCAGAACAAACTGCT
GGAGACAAAGCTGCAGTTCTACCAGAACCGCGAGTGTTGCCAGAGCAACCTGG
AGCCCCTGTTTGAGGGCTACATCGAGACTCTGCGGCGGGA
The first fragment from – 85 to +239. It covers 26 CG dinucleotides. The second fragment
from +213 to +388. It covers 11 CG dinucleotides.
139