Download Gene expression profiling reveals Ki

Gene expression profiling reveals Ki-67 associated proliferation signature in
human glioblastoma
This work was supported by grants from National Key Project of Science and Technology
Supporting Programs (No. 2007BAI05B08) and National Natural Science Foundation of China
(No. 30772238 and 30730035).
ABSTRACT
Background Everlasting cellular proliferation is the fundamental feature during gliomagenesis
and Ki-67 is one of the classical proliferation markers in human glioblastoma multiforme (GBM).
However, the driver genes or core pathways for cellular proliferation in GBM have not been
elucidated systematically.
Methods We evaluated by immunohistochemistry the prognostic value of Ki-67 expression in the
clinical outcome of 156 Chinese patients with GBM and a total of 64 GBM samples were selected
for further Agilent genome-wide microarray analysis. On the basis of the microarray data from
Tiantan (n=64) and TCGA (n=202) database, differentially expressed genes between the GBM
subgroups with high or low level of Ki-67 expression were identified using Significance Analysis
of Microarrays (SAM). Gene Ontology (GO) and KEGG Pathway analyses were then undertaken
for the Ki-67 associated genes to identify the most significant biological processes and signaling
pathways.
Results We confirmed that Ki-67 was an independent prognostic indicator in the largest Chinese
patient cohort of 156 GBM samples via immunohistochemical staining. Survival analysis of Ki-67
over-expression revealed a highly significant association with a worse clinical outcome (p=0.010
for progression-free survival; p=0.007 for overall survival). Comparative and integrated analysis
between Tiantan and TCGA database identified a 247-gene ‘proliferation signature’ (205
up-regulated and 42 down-regulated genes) that distinguished Ki-67 expression phenotypes. GO
and KEGG Pathway analyses further indicated that Ki-67 expression phenotype is associated with
distinct changes in gene expression associated with the regulation of cellular growth and
proliferation.
Conclusions Proliferation marker Ki-67 is an independent prognostic indicator in Chinese GBM
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patients. And Ki-67 associated proliferation signature identified through genome-wide microarray
analysis may provide potential targets for anti-proliferation therapy in GBM.
Keywords: glioblastoma; Ki-67; cellular proliferation; microarray; immunohistochemistry
INTRODUCTION
Glioblastoma multiforme (GBM) is the most common and lethal primary brain tumor in adults.1
Cellular proliferation is one of the most important biological processes in tumorigenesis because
of its role in growth and in the maintenance of tissue homeostasis.2,3 Different types of human
cancers have specific proliferation related gene signatures.4 And identification and blockage of
proliferation driver genes or core pathways has been considered as a key method for molecular
targeted therapy in human cancers.5-8 However, driver genes or core pathways for cellular
proliferation in GBM have not been investigated systematically.
In the present study, we evaluated the prognostic value of Ki-67 expression in the clinical outcome
of 156 Chinese patients with GBM by immunohistochemistry. Furthermore, to identify driver
genes and core signaling pathways for the cellular proliferation in GBM, we explored the genes
with significant differential expression between GBM samples with high or low level of Ki-67
expression both in Tiantan and TCGA microarray database, and for the first time identified a
247-gene ‘proliferation signature’ (205 up-regulated and 42 down-regulated genes) that
distinguished Ki-67 expression phenotypes.
METHODS
Patients and samples
One hundred and fifty-six patients with GBM from Neurosurgery Department of Beijing Tiantan
Hospital were enrolled in our study. All the patients underwent surgical resection between January
2006 and December 2009 and subsequently received radiation therapy and alkylating agent-based
chemotherapy. Patients were eligible for the study if the diagnosis of GBM was established
histologically according to the 2007 WHO classification. Tumor tissue samples were obtained by
surgical resection before the treatment with radiation and chemotherapy. This study was approved
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by the institutional review boards of the hospital and written informed consent was obtained from
all patients.
RNA extraction and gene expression profiling
All the tissue samples were immediately snap-frozen in liquid nitrogen after surgery. Frozen
sections were then stained with hematoxylin and eosin following standard protocols and were
examined using transmitted light microscopy. Samples were reviewed prior to RNA extraction and
confirmed to contain 80% or more tumor cells. Total RNA from frozen tumor tissues was
extracted by using the mirVana miRNA Isolation kit (Ambion, Austin, TX) according to the
manufacturer’s protocol. RNA quantity, quality and integrity were verified using the NanoDrop
ND-1000 spectrophotometer and the Agilent Bioanalyzer 2100. The microarray analysis was
performed on a total of 64 GBM samples using Agilent Gene Expression oligo microarrays
containing greater than 41,000 probe sets for approximately 27,958 human genes according to the
Agilent One-Color Microarray-Based Gene Expression Analysis Protocol (Cat# G4140-90040,
Agilent technologies, Santa Clara, CA). Average values of the replicate spots for each gene were
background subtracted, normalized, log2-transformed and subjected to further analysis.
Immunohistochemistry
Immunohistochemistry was performed as previously described.9 Briefly, surgical biopsies from
the 156 GBM patients were fixed in formalin, routinely processed and paraffin embedded. Five
micron-thick sections were prepared, and immunohistochemical staining with streptavidin-biotin
immunoperoxidase assay was performed using rabbit monoclonal antibody to Ki-67 (1:50 dilution,
Santa Cruz). Then, the percentage of positive tumor cells was counted under a bright-field
Olympus microscope (×200) and was used as labelling index (LI) which is equal to a rate of the
number of strong positive cells to that of counted cells. All the GBM samples were divided into
two subgroups with high (LI >25%) or low (LI ≤25%) level of Ki-67 expression. Controls without
primary antibody and positive control tissues were included in all experiments to ensure the
quality of staining. The clinical characteristics of the GBM patients in relation to Ki-67 protein
expression level are listed in Table 1.
Statistical and bioinformatics analysis
Differences in clinical characteristics were evaluated by χ2 test between the two patient subgroups.
Two clinical end-points were used to measure clinical outcome, progression-free survival (PFS)
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and overall survival (OS). PFS was defined as the time interval between the date of surgery and
the date of first recurrence. OS was defined as the time interval between the date of surgery and
the date of death. The survival function curve was calculated with the Kaplan-Meier method and
the difference was analyzed using the two-sided log-rank test. Statistical analysis was performed
in the SPSS 13.0 for Windows. All tests were two-tailed, and the significance level was set at
p<0.05.
The Cancer Genome Atlas (TCGA) network is the publicly available cancer database containing
the largest genome-wide microarray data from 202 GBM samples obtained from a multicenter
consortium. TCGA GBM genome-wide microarray data were downloaded from TCGA database
online.10 Both Tiantan and TCGA GBM samples were divided into two subgroups according to a
cut-off at the median value of Ki-67 expression level. Differentially expressed genes between the
two subgroups were identified using Significance Analysis of Microarrays (SAM) version 3.0
(Fold change>1.5; FDR<0.1%). Clustering analysis of the Ki-67 associated genes was performed
and visualized using Cluster 3.0 and Treeview software. Gene Ontology (GO) and KEGG
Pathway analyses were then undertaken for the Ki-67 associated genes using DAVID to identify
the most significant biological processes and signaling pathways (EASE score<0.01,
Bonferroni<0.01).11 The interactions and networks among the Ki-67 associated genes were
visualized with the Pajek software.
RESULTS
Ki-67 protein expression and clinical outcome in Chinese GBM patients
The expression of Ki-67 protein in 156 GBM samples was evaluated by immunohistochemistry.
Representative examples of Ki-67 immunohistochemical staining in GBM samples are showed in
Figure 1A. Ki-67 protein overexpression was found in 98/156 (62.8%) GBM samples. Survival
analysis revealed that patients with high level of Ki-67 expression had significantly shorter PFS
(p=0.010) and OS (p=0.007) than patients with low level of Ki-67 (Figure 1B). And there was no
significant difference in patient age, gender, preoperative KPS and extent of resection between the
two GBM subgroups with high or low level of Ki-67 expression (Table 1). These results indicated
that Ki-67 protein expression is an independent prognostic marker in Chinese GBM patients.
Identification of Ki-67 associated proliferation signature in GBM
To evaluate the Ki-67 associated proliferation signature, all the GBM samples from Tiantan and
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TCGA database were divided into two subgroups according to a cut-off at the median value of
Ki-67 expression level. Subsequently, a list of genes with significant differential expression was
identified between the two subgroups with high or low Ki-67 expression, respectively. In Tiantan
database (n=64), there were 4,504 up-regulated and 6,791 down-regulated genes in patients with
high Ki-67 expression compared to those with low Ki-67 expression. In TCGA database (n=202),
there were 318 up-regulated and 199 down-regulated genes in patients with high Ki-67 expression.
By using comparative analysis, a total of 247 overlapping genes from these two databases were
identified as a Ki-67 associated proliferation signature consisting of 205 up-regulated and 42
down-regulated genes (Figure 2A). Figure 2B and 2C showed the expressional clustering of genes
in the Ki-67 associated proliferation signature in Tiantan and TCGA database, respectively, and
the expression of these proliferation related genes changed in accordance with Ki-67 expression
levels.
Gene Ontology and KEGG Pathway analyses of proliferation related genes
Gene Ontology (GO) analysis of the 247 proliferation related genes identified cell cycle, cell
proliferation, DNA metabolism, DNA replication and chromosome cycle, cell growth and/or
maintenance, DNA repair, nucleotide and nucleic acid metabolism as the most enriched biological
processes (Figure 2D, Table 2). KEGG Pathway analysis also indicated that the proliferation
related genes were significantly involved in cell cycle and cell growth and death. (Table 2).
Furthermore, functional network analysis demonstrated that the most significant networks
representing the web of interactions among these genes indeed mediate cellular growth and
proliferation as shown in Figure 3. Taken together, these data indicate that Ki-67 expression
phenotype is associated with distinct changes in gene expression associated with the regulation of
cellular growth and proliferation.
DISCUSSION
Glioblastoma multiforme is the most malignant primary neoplasm in the central nervous system.
Even receiving the similar treatments, the clinical outcome varies significantly, with some patients
living less than a year while others surviving for more than 3 years.12 Therefore, many attempts
have been made to identify clinically relevant subgroups of GBM with distinct prognosis.13-15 In
particular, a higher proliferation index of the tumor cells has been associated with shorter
survival.16-18 Therefore, anti-proliferation is considered as an efficient direction for GBM therapy.
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In the present study, we aimed to investigate the prognostic value of proliferation marker Ki-67 in
Chinese patients with GBM. Ki-67 is a nuclear protein expressed during the G1, S, G2 and M
phases of the cell cycle, and has been widely used as a stable marker of cell proliferation in
various types of human tumors, including malignant gliomas.19-21 The Ki-67 protein expression
was evaluated by immunohistochemistry in a large cohort of 156 Chinese GBM patients in our
study, and increasing Ki-67 expression was found to be associated with an adverse prognosis. All
these results demonstrated that cellular proliferation is one of the most significant biological
processes in GBM tumorigenesis. Therefore, anti-proliferation therapy should be a very potential
direction in the development of novel molecular targeted therapies in GBM.
In consideration of the vital role of cellular proliferation in GBM pathogenesis, the first step in
developing anti-proliferation therapy is to identify the driver genes or core pathways as potential
therapeutic targets for cellular proliferation in GBM. Each type of human cancer often has specific
signaling pathways to rely on for cellular proliferation.22-24 However, the proliferation related
genes in GBM have not been systematically elucidated. Consequently, we performed
whole-genome gene expression profiling on a total of 64 GBM samples from Tiantan database,
and undertook a comparative and integrated analysis with the whole-genome microarray data of
202 GBM samples from TCGA database. Our aim was to pinpoint proliferation related genes for
which expression was impacted by changes in proliferation marker Ki-67. For the first time to our
knowledge, a 247-gene signature consisting of 205 up-regulated and 42 down-regulated genes that
distinguished Ki-67 expression phenotypes was identified as the Ki-67 associated proliferation
signature in GBM. Furthermore, functional GO and KEGG Pathway analyses also demonstrated
that Ki-67 expression phenotype is associated with distinct changes in gene expression associated
with the regulation of cellular growth and proliferation. Impressively, it should be pointed out that
E2F2 transcription factor was positively correlated with Ki-67 expression and located near to
Ki-67 in gene expressional clustering in our study. And report has shown that the retinoblastoma
tumour suppressor (RB) pathway plays a critical role in the control of cellular proliferation by
regulating E2F1-3 factors thought to act as transcriptional activators important for progression.25
Thus, RB/E2F signaling pathway may be the key driver for cellular proliferation in GBM, and
should be considered as potential targets while developing molecular targeted drugs.
Taken together, we confirm proliferation marker Ki-67 is an independent prognostic indicator in a
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large cohort of Chinese GBM patients. And the integrated microarray analysis revealed a Ki-67
associated proliferation signature that may provide a basis for functional validation and may
eventually lead to the identification of novel candidates for anti-proliferation therapy in GBM.
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Table 1. Clinical characteristics of 156 Chinese patients with GBM in relation to the Ki-67
protein expression
Ki-67 Protein Expression
(n=156); n (%)
Clinical Variable
Low (n=58)
High (n=98)
P value*
<48
33 (31.2)
51 (52.8)
0.619
>48
25 (26.8)
47 (45.2)
Male
39 (36.1)
58 (60.9)
Female
19 (21.9)
40 (37.1)
<80
23 (21.6)
35 (36.4)
>80
35 (36.4)
63 (61.6)
Total
21 (21.21)
36 (35.8)
Subtotal
37 (36.8)
62 (62.2)
Age (year)
Gender
0.393
Preoperative KPS
0.732
Extent of resection
* Two-sided χ2 test.
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1.000
Table 2. Gene Ontology and KEGG Pathway analysis of the proliferation related genes in
Ki-67 associated proliferation signature
Gene Number
EASE score
Bonferroni
cell cycle
60
1.58E-29
6.00E-27
cell proliferation
68
2.62E-26
9.97E-24
DNA metabolism
42
3.88E-19
1.48E-16
DNA replication and chromosome cycle
27
1.07E-18
4.07E-16
cell growth and/or maintenance
92
2.32E-09
8.83E-07
DNA repair
16
5.11E-08
1.95E-05
nucleotide and nucleic acid metabolism
68
1.55E-06
5.91E-04
Cell cycle
17
2.44E-11
1.05E-09
Cell Growth and Death
17
7.13E-09
3.07E-07
GO Biological Process
KEGG Pathway
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Figure Legends
Figure 1. Ki-67 protein expression examined by immunohistochemistry and its correlation
with clinical outcome in 156 Chinese GBM patients.
(A) Representative micrographs of Ki-67 immunohistochemical staining in GBM samples
(Original magnification ×200). (B) Kaplan-Meier analysis comparing progression-free and overall
survival of GBM patients with low (red line) or high (black line) level of Ki-67 expression.
Figure 2. Identification of Ki-67 associated proliferation signature in GBM.
(A) Proliferation associated gene signature was derived from the overlap of Ki-67 associated
genes in Tiantan and TCGA GBM databases. (B) Expressional clustering of genes in the Ki-67
associated proliferation signature in Tiantan database (n=64). (C) Expressional clustering of genes
in the Ki-67 associated proliferation signature in TCGA database (n=202). (D) The most
significant GO biological processes of the proliferation related genes in Ki-67 associated
proliferation signature.
Figure 3. A functional network involving many of the proliferation related genes in Ki-67
associated proliferation signature.
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Figure 1
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Figure 2
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Figure 3
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