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Biol. Chem., Vol. 390, pp. 125–135, February 2009 • Copyright by Walter de Gruyter • Berlin • New York. DOI 10.1515/BC.2009.013
Exploring the pathogenesis of renal cell carcinoma: pathway
and bioinformatics analysis of dysregulated genes and
proteins*
Alexander D. Romaschin1,2, Youssef Youssef1,
Tsz-fung F. Chow1, K.W. Michael Siu3, Leroi V.
DeSouza3, R. John Honey4, Robert Stewart4,
Kenneth T. Pace4 and George M. Yousef1,2,**
Department of Laboratory Medicine and the Keenan
Research Center in the Li Ka Shing Knowledge
Institute, St. Michael’s Hospital Toronto, Toronto,
M5B 1W8, Canada
2
Department of Laboratory Medicine and Pathobiology,
University of Toronto, Toronto, M5B 1W8, Canada
3
Department of Chemistry and Center for Research in
Mass Spectrometry, York University, Toronto, M3J 1P3,
Canada
4
Division of Urology, Department of Surgery, St.
Michael’s Hospital Toronto, University of Toronto,
Toronto, M5B 1W8, Canada
Keywords: bioinformatics; gene ontology (GO)
consortium; kidney cancer; mass spectrometry;
mammalian target of rapamycin (mTOR); pathway
analysis; proteomics; renal cell carcinoma; tumor
markers.
1
** Corresponding author
e-mail: [email protected]
Abstract
We recently identified a group of proteins which are dysregulated in renal cell carcinoma (RCC). In this study, we
performed bioinformatics and pathway analysis of these
proteins. Proteins were mapped to gene ontology biological processes. The upregulated proteins tend to cluster
in processes, such as cancer initiation and progression.
In addition, we identified a number of pathways that are
significantly enriched in RCC. Some of these are ‘common’ pathways which are dysregulated in many cancers,
but we also identified a number of pathways which were
not previously linked to RCC. In addition to their potential
prognostic values, many of these pathways have a
potential as therapeutic targets for RCC. To verify our
findings, we compared our proteins to a pool of datasets
from published reports. Although there were only a minimal number of common proteins, there was a significant
overlap between the identified pathways in the two
groups. Moreover, out of 16 individually discovered
genes identified by a literature search, 10 were found to
be related to our dysregulated pathways. We also verified
the upregulation of the mammalian target of rapamycin
signaling pathway in RCC by immunohistochemistry.
Finally, we highlight the potential clinical applications of
pathway analysis in kidney cancer.
*Supplementary material to this article can be accessed from
the journal’s online edition at http://www.reference-global.com/
toc/bchm/390/2.
Introduction
A popular approach in understanding the pathogenesis
of initiation and/or progression of cancer has been
through the employment of genome-wide expression
analysis at the mRNA level. More recently, proteomic
analysis is becoming an increasingly important analytical
tool which overcomes many of the limitations of RNA
analysis (Faca et al., 2007). Unlike genomic studies
where individual changes may have no functional significance, protein expression is closely aligned to cellular
activity.
Classically, individual analyses of tumor markers,
especially the highly dysregulated ones, and their correlation with clinicopathological parameters helps in
assessment of their potential clinical utility (DeSouza et
al., 2005). More recently, however, focus of attention has
begun to switch into a more ‘global’ analysis of dysregulated genes and proteins in order to obtain a better
understanding of the potential ‘cross-talks’ between
them (Setlur et al., 2007). Global analyses usually yield
an enormous amount of information about molecules
which are involved in cancer pathogenesis and can be
used as tumor markers.
Several databases and analytical tools are now available to the research community including the Gene
Ontology (GO) consortium (for classification of genes
according to their subcellular compartmentalization, biological processes and molecular functions), pathway
analysis, and protein-protein interaction databases and
analytical algorithms.
Renal cell carcinoma (RCC) is one of the top 10 most
frequent malignancies in Western societies. It is known
to be one of the most therapy-resistant cancers. So far,
little is known about the molecular changes in RCC, and
there are no tumor markers which are of clinical use for
diagnostic, prognostic, and treatment purposes.
In our efforts to elucidate the pathogenesis of RCC
initiation and progression, we recently identified a group
of proteins which are dysregulated in kidney cancer tissues compared to their normal counterparts (Siu et al.,
2008). In this paper, we further perform bioinformatics analysis including pathway and functional analysis. We validated one of the dysregulated pathways, the
mammalian target of rapamycin (mTOR) signaling pathway, by immunohistochemistry. We also verified our
bioinformatics results in silico through comparative analy-
2009/202
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126 A.D. Romaschin et al.
sis with previously published dysregulated mRNAs and
proteins in kidney cancer. Finally, we highlight the potential clinical applications of pathway analysis in RCC.
Results
Gene ontology analysis
Dysregulated proteins in RCC were mapped to GO biological processes using multiple search engines, as
described in the materials section. Figure 1 shows a partial list of the more significant biological processes which
are dysregulated in kidney cancer compared to their normal counterparts. As expected, the upregulated proteins
tend to cluster in biological processes which are related
to cancer initiation and progression, e.g., cell cycle, cell
motility, anti-apoptosis, and proteolysis. Other interesting
processes include glycolysis (discussed below), RNA
splicing, protein folding, signal transduction, and protein
transport (Figure 1A). The downregulated proteins show
a significantly different biological process clustering
(Figure 1B). Some of these processes can be directly
related to the malignant process, e.g., apoptosis, regulation of processing through cell cycle, signal transduction, and electron transport, while others, e.g., lipid
metabolic processes, tricarboxylic acid (TCA) cycle, need
to be investigated in relation to kidney cancer (discussed
further below).
Molecular function clustering was also carried out
through the GO consortium. Enriched molecular functions among upregulated proteins include RNA binding,
magnesium and metal ion binding, ATP, calcium and zinc
ion binding, and oxidoreductase activity, while downregulated proteins show a significant clustering in actin
binding, calcium, and zinc ion binding (data not shown).
Pathway analysis
Table 1 shows a partial list of the significantly enriched
pathways in the clear cell type of RCC. Some of these
pathways are ‘common’ pathways which are expected to
be dysregulated in many cancers, such as the cell cycle,
apoptosis, MAP kinase pathway, and cell adhesion, etc.
We also identified a number of interesting pathways
which were not previously linked to RCC, including insulin signaling, peroxisome proliferator-activated receptor
(PPAR) signaling, hemostasis and blood coagulation,
pyruvate metabolism and TCA cycle, formation of eEF1B
complex, and regulation of actin cytoskeleton pathways.
Literature searches showed that many of these pathways
were previously linked to other malignancies. As discussed below, some of these pathways have a potential
as therapeutic targets for RCC. It is important to mention
that only few genes match to the kidney cancer pathway.
This pathway, however, encompasses pathogeneses of
all histological types of kidney cancers and is not restricted to the clear cell type of RCC which was analyzed in
our study. It is also important to emphasize that statistical
significance (either the impact factor or the p-value) is
not equivalent to the biological significance in all cases,
as the total number of molecules in a given pathway varies according to the database used, and some pathways
are loosely defined and incorporate many subdivisions.
Figure 1 Pie chart showing a partial list of the significantly
altered biological processes in renal cell carcinoma, based on
GO analysis.
The biological processes which are enriched with upregulated
proteins are significantly different from those with downregulated
proteins. See text for details.
Some irrelevant pathways were identified because of the
presence of overlap in some segments with other pathways, e.g., Escherichia coli infection is overlapping with
the cytoskeleton rearrangements and the apoptotic
pathways.
Activated and suppressed pathways in RCC
To elucidate the functional relevance of dysregulated
pathways, we analyzed them according to the number of
upregulated and downregulated components. Although
protein upregulation might not necessarily lead to the
activation of a pathway, we can still assume activation of
the pathway when the majority of its components are
upregulated and vice versa. Figure 2 shows a representative graph of the up- and downregulated pathways. The
upregulated pathways were those related to apoptosis,
cell cycle, regulation of cytoskeleton, and cell adhesion.
Many signaling pathways were also found to be upregulated, including PPAR signaling, insulin signaling, Wnt
signaling, TGF-b signaling, and MAP kinase pathways.
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Pathway analysis in kidney cancer 127
Table 1 Partial list of significantly enriched pathways among dysregulated proteins in RCC.
Pathway name
Cell cycle
Apoptosis
TCA cycle
Hemostasis and coagulation
Regulation of actin cytoskeleton
Cell-cell adhesion
Leukocyte transendothelial migration
PPAR signaling pathway
Insulin signaling pathway
Wnt signaling pathway
TGF-b signaling pathway
MAPK signaling pathway
VEGF signaling pathway
mTOR signaling pathway
Calcium signaling pathway
Rho GTPases signaling pathway
Regulation of gene expression
UTR-mediated translational regulation
DNA repair
mRNA processing
Translation
Metabolism of small molecules
Metabolism of amino acids
Metabolism of ketone body
Metabolism of lipoprotein
Metabolism of nucleotide
Metabolism of xenobiotics
Metabolism of lipid and lipoprotein
Renal cell carcinoma
Pathogenic Escherichia coli infection
Antigen processing and presentation
Axon guidance
ECM receptor interaction
Natural killer cell-mediated cytotoxicity
Electron transport chain
Formation of eEF1B complex
Total
molecules
Positive
matches
Search
enginea
Significance
factorb
Upregulatedc
Downregulatedc
114
84
49
21
127
69
208
195
116
70
135
149
84
256
70
47
175
132
310
109
85
136
124
70
72
5
13
81
68
118
69
49
82
128
87
131
76
3
9
6
6
10
14
13
31
23
14
13
13
8
5
11
4
2
4
8
41
17
5
18
23
29
20
4
6
16
11
16
3
15
10
9
6
6
6
2
1
1
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
2
2
5.261
3.439
3.80E-01
5.70E-05
8.60E-01
16.607
25.089
18.560
12.138
16.427
8.982
2.777
2.405
2.176
1.987
0.951
0.309
9.50E-01
7.30E-02
2.50E-02
9.20E-01
2.10E-01
1.10E-02
2.30E-11
2.10E-05
3.70E-04
6.70E-04
7.70E-03
5.20E-02
8.80E-02
1.168
25.929
9.144
4.528
3.292
1.780
7.50E-01
2.50E-02
4
4
2
0
4
4
9
12
6
7
3
6
4
5
2
1
2
3
21
9
1
8
13
18
4
1
5
7
7
9
1
11
3
5
3
3
0
1
3
2
3
9
8
5
7
6
6
5
2
1
1
3
2
1
0
1
11
3
2
6
5
7
13
3
0
8
0
6
2
3
3
3
1
3
5
1
Search engines used are: (1) Onto-Tools and (2) Reactome.
Significance factor is the ‘impact factor’ for the Onto-Tools search engine and the ‘p’-value for Reactome.
c
Only proteins with G1.5-fold change were defined as up- or downregulated.
a
b
Also, pathways related to regulation of gene expression,
translational regulation, metabolism of lipoproteins, and
xenobiotics were upregulated. Downregulated pathways
included the TCA cycle, electron transport chain, coagulation and hemostasis, and the immune system signaling pathways. Also, DNA repair, amino acid metabolism,
and ketone body metabolism showed underexpression
of many members. Certain pathways, such as the actin
cytoskeleton, cell adhesion, and antigen presentations
pathways, showed a mixed pattern of up- and downregulation of its components. Our findings are quite comparable to previously published functional analyses in
RCC (Boer et al., 2001; Gieseg et al., 2002). Differences
can be attributed to the fact that previous studies were
based on mRNA microarray analysis rather than proteins.
Also, we analyzed ‘pathway’ dysregulation which is more
accurate than functional taxonomy analysis which does
not take into account the dynamics or dependencies that
would be required to fully describe a pathway. Specimen
heterogeneity may be another important factor. While
many of the previous studies were based on analysis of
a mixture of histological types, we focused only on the
clear cell subtype of RCC.
Validation of pathway analysis by independent
datasets
To verify our findings, we compared our results to a pool
of datasets from 12 previously published lists of dysregulated genes and proteins in kidney cancer (Rae et
al., 2000; Boer et al., 2001; Takahashi et al., 2001; Higgins et al., 2003; Lenburg et al., 2003; Seliger et al.,
2003a,b; Liou et al., 2004; Shi et al., 2004; Perego et al.,
2005; Craven et al., 2006; Perroud et al., 2006). This pool
included both mRNAs and proteins, analyzed using a
variety of techniques including differential display,
microarray, and proteomic profiling on a spectrum of
samples ranging from cell lines to tissues, urine, and
blood.
There was a minimal overlap (10–20%) between our
signature protein list and previously published results
(data not shown), which is consistent with the average
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128 A.D. Romaschin et al.
Pathway analysis of individually isolated protein
markers
To further validate our findings, we performed pathway
analyses on previously identified ‘individual’ diagnostic
markers for RCC to examine if they belong to our significant pathways. Out of 16 genes and proteins identified
by literature searches, 10 were found to be related to our
list of dysregulated pathways in kidney cancer. These
markers are listed in Table 3, along with their clinical
significance.
Clinical applications of pathway analysis
Figure 2 The frequency of up- and downregulated proteins in
significantly dysregulated pathways in RCC.
Dysregulated proteins were mapped to pathways and were classified as over- or underexpressed according to quantitative fold
changes from normal kidney counterparts.
overlap observed when analyzing protein datasets from
different experiments (Yu et al., 2007).
Focusing only on proteomic profiling studies, we compared our results with four previously published reports
of differential protein expression in kidney cancer (Sarto
et al., 1997; Shi et al., 2004; Perego et al., 2005; Craven
et al., 2006). Only 24 proteins from our list of dysregulated proteins were identified in the other reports; 15 of
them by one report, five by two reports and four by three
studies (data not shown).
We then hypothesized that these genes, despite the
minimal overlap, are representatives of common pathways and that technical variations lead to identification
of different members of the same pathway in each study.
We performed a comparative pathway analysis between
our proteins and the pool of datasets. A total of 3969
non-redundant genes and proteins were compiled from
published literature and compared to our list of 869 dysregulated proteins against a database of 3030 pathway
events. A total of 541 genes from the literature pool
matched to 708 events (pathways), compared to a total
of 253 of our proteins which matched 380 pathway
events. As shown in Table 2, there was a significant overlap between the identified pathways in the two groups.
These pathways include cell cycle, apoptosis, TCA cycle,
insulin signaling pathways, PPAR receptor signaling, regulation of actin cytoskeleton, and hemostasis, among
others (compare Tables 1 and 2). An interesting observation is that despite this significant overlap, the actual
number of common proteins identified in these pathways
was 10–20% on average, further proving our hypothesis
that these pathways are involved in RCC pathogenesis,
regardless of the genes identified. Technical differences
between studies favor the identification of certain proteins in each study.
In addition to their value in delineating cancer pathogenesis, we hypothesized that dysregulated pathways have
direct clinical applications. We performed a literature
search to examine the potential clinical applications of
dysregulated pathways in both kidney and other cancer
types. Table 4 shows selected clinical applications of the
significantly altered pathways in different types of cancers. These applications range from being markers of
aggressiveness and metastasis to therapeutic applications. The latter include immunotherapy, targeted molecular therapy in addition to chemotherapy. Promising
preliminary results were observed in a variety of cancers,
including breast, colon, and lung cancers, in addition to
kidney cancer.
Immunohistochemical validation of pathway analysis
The mTOR pathway was chosen for immunohistochemical (IHC) validation because of its potential clinical
value. Inhibitors of the mTOR have shown promising efficacy in early stage trials in patients with advanced RCC
(Radulovic and Bjelogrlic, 2007). Both phosphorylated
mTOR and S6 protein were found to be overexpressed
in RCC compared to the normal kidney (p-0.05), in
agreement with previously published studies (Lin et al.,
2006; see Figure 3).
Semi-quantitative RT-PCR validation of pathway
analysis
Three components in the coagulation pathway: F2,
KNG1, and SERPING1, which were found to be dysregulated, were validated with semi-quantitative RT-PCR.
The PCR result supported the original findings that F2
was underexpressed in RCC compared to normal kidney,
while KNG1 and SERPING1 were overexpressed in RCC
compared to normal kidney (p-0.05) (Figure 4).
Discussion
To our knowledge, this is the first study, at the protein
level, which analyzes pathway changes in RCC based on
quantitative protein expression from kidney cancer tissues. Global analysis has several advantages over the
traditional, single molecule approach. Proteins are known
to function in regulated networks or pathways, and bioinformatic approaches are thus useful in delineating the
dynamic interactions between them. Moreover, while in
individual protein analysis we focus on ‘highly’ dysregu-
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Pathway analysis in kidney cancer 129
Table 2 Comparative pathway analysis between our dysregulated proteins in RCC and a pool of 12 studies.
Pathway
Total
pathway
molecules
Our
study
Comparison
poola
Common
proteins
Common
protein symbols
Search
engineb
Cell cycle
114
9
16
5
PCNA, SFN, YWHAB,
YWHAG, YWHAH
1
Apoptosis
84
6
9
2
AIFM1, CYCS
1
Regulation of actin
cytoskeleton
208
40
44
12
ACTB, ARPC3, F2, GSN, MSN,
MYH10, MYH14, MYLK, RAC1,
RAC2, RDX, ROCK2
1
Regulation of gene
expression
310
41
48
7
EEF1A1, EIF4A1, RPS8,
RPLP2, RPS28, RPS25, EEF2
2
Cell-cell adhesion
195
23
53
5
ACTB, COL1A1, RAC1, RAC2,
ROCK2
1
21
10
10
5
OGDH, ACO2, SUCLG1,
SUCLG2, PDHB
2
150
14
56
7
2
69
13
34
8
RAC1, ALB, F2, SERPINA1,
HRG, FGA, FGB
C3, CFB, F2, FGA, FGB,
KNG1, SERPINA1, SERPING1
Leukocyte
transendothelial
migration
116
14
31
5
ACTB, MSN, RAC1, RAC2,
ROCK2
1
Insulin signaling
pathway
135
13
32
9
CALM2, FBP1, PCK1, PCK2,
PFKP, PKLR, PKM2, PYGB,
PYGL
1
PPAR signaling
pathway
70
13
30
8
ACSL1, APOA1, FABP1, PABP5,
FABP7, PCK1, PCK2, UBC
1
Immune system
signaling pathway
325
7
78
3
IGHV4-31, F2, C3
2
Wnt signaling
pathway
149
8
24
3
RAC1, RAC2, ROCK2
1
MAPK signaling
pathway
256
11
38
3
MAPT, RAC1, RAC2
1
TGF-b signaling
pathway
84
5
12
2
DCN, ROCK2
1
VEGF signaling
pathway
70
4
7
2
RAC1, RAC2
1
Metabolism of
small molecules
70
29
29
17
FBP1, ENO2, CALM1, ALDOA,
KHK, TPI1, ALDOB, PYGL,
PFKP, PCK1, PGK1, PKLR,
HK1, PGMI, PGAM1, ENO1,
PKM2
2
Metabolism of
amino acids
72
20
39
13
OGDH, HIBADH, GATM, SHMT1,
GOT1, CKB, ACAT1, GLUD1,
QDPR, ALDH9A1, ALDH6A1,
IVD, HIBCH
2
120
16
36
8
OXCT1, HMGCL, APOE, ALB,
ACAT1, HADHA, SDCI, P4HB
2
Metabolism of
xenobiotics
68
11
23
8
GSTP1, AHCY, ALDH1A1,
NNMT, GSS, ACSM2B,
ALDH2, GSTA2
2
Renal cell
carcinoma
69
3
12
1
RAC1
1
TCA cycle
Hemostasis and
coagulation
Metabolism of lipids
and lipoprotein
1
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130 A.D. Romaschin et al.
Table 2 (Continued)
Pathway
Total
pathway
molecules
Our
study
Comparison
poola
Common
proteins
Common
protein symbols
Search
engineb
Antigen processing
and presentation
82
10
29
8
CALR, CANX, CTSB, HLA- DRB1,
HSP90AA1, HSPA5, LGMN, PDIA3
1
Pathogenic Escherichia coli
rinfection
49
15
15
5
ACTB, CDH1, ROCK2, TUBB,
TUBB2C
1
128
9
31
4
DPYSL2, RAC1, RAC2, ROCK2
1
Axon guidance
A pool of 12 published studies on RCC was used for analysis. See text for full references.
b
Search engines used are: (1) Onto-Tools and (2) Reactome.
a
lated proteins (significantly up- or downregulated) which
can be verified experimentally, pathway analysis allows
the understanding of the role of minimally altered proteins
which might be significant in the context of a biological
pathway (Setlur et al., 2007).
Many of the overrepresented pathways in our analysis
were also shown to be dysregulated in other malignancies. Cell cycle pathway, MAP kinase, and cell adhesion
pathways were implicated to play a role in the development of prostate cancer and to be signatures of metastatic potential (Setlur et al., 2007). Cross-talks between
many of these pathways have been previously documented in various malignancies, e.g., the interaction
between both the EGFR and PPAR signaling axis with
the PI3K/Akt pathway in bladder cancer (Kassouf et al.,
2006).
Pathway alterations can have significant clinical applications. In addition to therapeutic intervention, pathwayderived signatures can also be used to predict prognosis
in many cancers, e.g., breast cancer (Yu et al., 2007).
An important pathway that was identified is the pyruvate metabolism and the TCA cycle, where 10 out of a
Table 3 Individually discovered RCC biomarkers which belong to pathways identified in our study.
Gene symbol
Clinical application
NNMT
Potential diagnostic
and prognostic biomarker
Independent prognostic factor
Overexpressed in RCC
Overexpressed in the majority of
renal cell carcinomas
Identified in
our study
Reference
Related pathway
Yes
Yao et al., 2005
Yes
Yes
Yes
Takayama et al., 2006
Buchner et al., 2007
Hanada et al., 2001
Plexin B1
FGF5
Downregulated in RCC
Overexpressed in RCC
No
No
Gomez Roman et al., 2008
Hanada et al., 2001
MDM2
Overexpressed in RCC
No
Seliger et al., 2003a
ABP1
Antigen for potential immunotherapy
in RCC
Promotes renal tumorigenesis
Prognostic factor in RCC
No
Seliger et al., 2003a
Metabolism of
xenobiotics
Nucleotide metabolism
PPAR signaling pathway
Glycolysis
Gluconeogenesis
Insulin signaling
Axon guidance
Regulation of actin
cytoskeleton
Melanoma, glioma, chronic
myeloid leukemia and
prostate cancer
Amino acid metabolism
Yes
Yes
Yan et al., 2007
George and Bukowski,
2007
Gluconeogenesis
Rho GTPase signaling
pathway
ECGF1
FABP7
PKM2
PGK1
LDHB
Table 4 Potential clinical utility of significantly altered pathways in RCC.
Pathway
Clinical application
Cancer type
Reference
Glycolysis
mTOR
VEGF signaling pathway
MAPK
Immunotherapy targets
Molecular targeted therapy
Targeted therapy
Therapeutic
Cell cycle
Focal adhesion
Focal adhesion
Wnt signaling
Prognosis and therapeutic decision
Hanada et al., 2001
Hanna et al., 2008
Sosman et al., 2007
Chen et al., 2001
van Spronsen et al., 2005
Vincenzi et al., 2006
Draghici et al., 2007
van Nimwegen and van de Water, 2007
He et al., 2005; Mikami et al., 2005
TGF-b
Marker of invasiveness and
metastatic capacity
Kidney cancer
Kidney cancer
Kidney cancer
Breast cancer
Kidney cancer
Lung cancer
Lung cancer
Breast cancer
Colorectal cancer
and sarcoma
Breast cancer
Anticancer therapy
Therapeutic
Todorovic-Rakovic, 2005
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Pathway analysis in kidney cancer 131
Figure 3 mTOR pathway and overexpressions of its components in RCC.
(A) The mTOR pathway. (B,C) Immunohistochemical staining of the phosphorylated S6 protein in normal kidney (B) and renal cell
carcinoma (C). (D,E) Immunohistochemical staining of the phosphorylated mTOR protein in normal kidney (D) and renal cell carcinoma
(E). Both proteins show overexpression in RCC compared to the normal kidney.
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132 A.D. Romaschin et al.
Figure 4 Semi-quantitative RT-PCR analysis of three dysregulated components (F2, KNG1, SERPING1) in the coagulation
pathway.
total of 21 events in the pathway were detected as dysregulated proteins in our list. In contrast to normal proliferating cells, tumor cells have to survive in environments with varying oxygen and nutrient supplies (Mazurek et al., 2005). The increase in lactate dehydrogenase
and the activation of the pyruvate kinase pathway indicate active anaerobic glycolysis which is a reflection of
the hypoxic conditions known to be an integral component of the pathogenesis of RCC (Turner et al., 2002;
Kaelin, 2003). Our results are consistent with a recent
report (Perroud et al., 2006) whereby pathway changes
in RCC were analyzed. In another recent study, Hwa et
al. (2006) pointed out dysregulation of hexokinase in RCC
patients.
Another interesting pathway is the hemostasis and
blood coagulation pathway. RCC is known to be accompanied with dysregulation of the coagulation mechanism
and a propensity for vascular thrombosis in the renal vein
and inferior vena cava in up to 46% of cases (Hoehn and
Hermanek, 1983). This has been confirmed by a number
of coagulation studies (Zacharski et al., 1986). Moreover,
recent studies have also shown that the essential factors
of hemostasis support cancer growth and proliferation
through providing a supportive scaffold for tumor angiogenesis (Rak et al., 2006). They also directly play a role
in cancer cell ability for proliferation, migration, and
induction of proteolysis (Buller et al., 2007). These findings inspired therapeutic applications, and a number of
studies has evaluated the effects of anticoagulants on
tumor growth and recurrence in various types of cancer
(Buller et al., 2007). In addition, some components of the
coagulation factors, e.g., tissue factor, is found in urine
and might serve as cancer biomarkers for urologic cancers (Lwaleed et al., 2007).
As shown in Table 1, the insulin signaling pathway is
significantly presented in RCC (impact factor 8.982), with
13 pathway molecules identified in our list. The link
between insulin signaling and kidney cancer is not surprising. Epidemiological studies have shown that insulinresistance states, characterized by hyperinsulinemia, are
associated with an increased risk of a number of malignancies, including kidney cancer (Belfiore, 2007), and
that insulin receptor is overexpressed in cancer. It has
also been recently shown that insulin can control the
mTOR signaling pathway (which is known to be dysregulated in RCC) by phosphorylation, mediated through PI3kinase signaling (Wang et al., 2005). Interestingly, our
upregulated proteins in this pathway included ribosomal
protein S6 kinase, a downstream target in the mTOR
pathway which mediates rapid phosphorylation of ribosomal protein S6 on multiple serine residues in response
to insulin or several classes of mitogens. This might have
important implications for both the prevention and treatment of kidney cancer. They underline the concept that
hyperinsulinemia, associated with insulin resistance and
obesity, should be treated to avoid an increased risk of
cancer. They also represent potential molecular targets
for cancer therapy. Recent studies have shown that the
level of insulin signaling is key in the regulation of cancer
stem cells which function through at least two tumor suppressor genes (Narbonne and Roy, 2008).
Our IHC results showing upregulation of members of
the mTOR pathway in kidney cancer is not unprecedented. In fact, this pathway has recently attracted much
attention for targeted therapy in kidney cancer, and the
first successful phase III clinical trial involving mTOR
inhibitors has been recently published (Hudes et al.,
2007).
The pathway involved in eEF1B complex formation (2
of 3 elements found) is presumed to have an essential
role in the control of gene expression (Le et al., 2006).
Regulation of actin cytoskeleton was also identified with
potential involvement in RCC pathogenesis. This is not
surprising, as loss of von Hippel-Lindau (VHL) tumor suppressor gene function occurs in familial and most sporadic clear cell RCC. VHL loss has been shown to allow
robust RCC cell motility, invasiveness, and morphogenesis (Koochekpour et al., 1999). A recent study showed
that VHL gene product represses oncogenic b-catenin
signaling in renal carcinoma cells (Peruzzi et al., 2006).
The PPAR signaling pathway also has a potential role
in the course of RCC. PPARs are transcription factors
which strongly influence molecular events in normal and
cancer cells (Michalik et al., 2004). PPARs are nuclear
receptors for linoleic and arachidonic acid metabolites. It
has been shown that PPAR-g promotes colonic tumorigenesis, and that non-steroidal anti-inflammatory drugs
(NSAIDs) suppress PPAR-d activity in colon cancer cells.
Moreover, breast cancer cell lines that express PPAR-g
can be prompted to undergo growth arrest and differentiation when treated with synthetic PPAR-g ligands.
ANGPTL4, a member of this pathway, was present in
very large amounts in RCC tumor cells, but not in benign
kidney. Both as a protein encoded by a target gene of
PPAR-a and PPAR-g, which have been shown to be
associated with the regulation of lipid metabolism and/or
glucose homeostasis and as a hypoxia-inducible secreted protein, ANGPTL4 has potential for use as a new
diagnostic tool and a therapeutic target, modulating
angiogenesis in tumors and ischemic tissues (Le et al.,
2003).
Changes in the lipid and lipoprotein metabolism can
also be of clinical significance. The metabolic syndrome
Article in press - uncorrected proof
Pathway analysis in kidney cancer 133
is composed of cardiovascular risk factors, including
increased body mass index/waist circumference, blood
pressure, plasma glucose, and triglycerides, as well as
decreased high-density lipoprotein cholesterol. Interestingly, most of the components of the metabolic syndrome have individually been linked in some way to the
development of cancer, including kidney cancer (Cowey
and Hardy, 2006).
Our study provides a better resolution of the apparent
differences and lack of consistency among studies,
which might be related to many factors including the
material used for analysis (tissue vs. serum vs. cell lines,
etc.), the molecules investigated (genes or proteins) or
the methodology used (microarray vs. proteomics, etc.).
Regardless of these differences, we showed that these
results can be very informative, being linked to specific
pathways which are worth investigating.
The recent success of molecular-targeted therapeutics
in treating cancers where oncogenic pathways are well
defined offers promise for overcoming the resistance to
conventional chemotherapy displayed by RCC. Our
results provide a rationale for the assessment of agents
that target these pathways for efficacy against RCC.
Materials and methods
Quantitative protein expression in RCC
We analyzed differentially expressed proteins in RCC compared
to normal counterparts from the same patient using quantitative
mass spectrometry analysis and simultaneous labeling. Overand underexpression were calculated as fold changes in protein
concentrations. A summary of the procedure and complete list
of the dysregulated proteins can be found in our previously published results (Siu et al., 2008), and in the online supplementary
material.
Bioinformatic analysis
Official gene symbols, GenBank accession numbers and
SwissProt IDs were compiled and converted through web tools
of the Cancer Genome Anatomy Project (Krizman et al., 1999),
The Database for Annotation, Visualization and Integrated Discovery (DAVID) (Dennis et al., 2003), and the ExPASy Proteomics
Server. Dysregulated proteins were mapped to the Gene Ontology (GO) (Harris et al., 2004). We used a cut-off value of 1.5fold change for up- or downregulation. GO analysis was carried
out using the Onto-Tools and DAVID algorithms. It should be
noted that a GO biological process is not equivalent to a pathway, as GO does not try to represent the dynamics or dependencies which would be required to fully describe a pathway.
For in silico validation, we utilized previously published genes
and proteins which were shown to be differentially expressed in
kidney cancer. Lists of studies included are indicated in the
results section for each analysis. Literature searches were carried out through PubMed, BioMedCentral, and Google search
engines.
Pathway analysis
Pathway analysis was performed through the KEGG pathway
database (Kanehisa et al., 2008), DAVID Bioinformatics
Resources (Dennis et al., 2003), the Reactome Knowledgebase
of Human Biological Pathways and Processes (Vastrik et al.,
2007), the Onto-Tools integrated databases (Draghici et al.,
2003), and the BioCarta Pathways (http://www.biocarta.com/
index.asp). Official gene symbols and SwissProt accession
numbers were used to map proteins to various pathways. Significantly presented pathways in the ‘Reactome’ databases were
calculated by unadjusted probability (hypergeometric distribution) of seeing a given number or more genes in an event by
chance, and significance was expressed by a p-value.
Onto-Tool analysis calculates a perturbation factor for each
input gene, which reflects the relative importance of each differentially regulated gene. Significance is presented as an
impact factor of the entire pathway and takes into consideration
the proportion of differentially regulated genes in the pathway
and gene perturbation factors of all genes in the pathway.
Immunohistochemical validation
Paraffin blocks were sectioned 4 mm thick, mounted on slides,
and dried overnight. Sections were deparaffinized in xylene and
rehydrated through decreasing graded alcohols. Slides were
immunostained using the Benchmark XT (Ventana, Tucson, AZ,
USA) with monoclonal antibodies for phospho-mTOR (Ser2448)
(Cell Signaling Tech, Danvers, MA, USA), and phosphor-S6 ribosomal protein (Ser235/236) (Cell Signaling Tech). Immune complexes were visualized by incubation with diaminobenzidine, and
sections were counterstained with hematoxylin. All slides were
reviewed and scored independently by two pathologists. Staining was scored for both percent positivity (in a scale from 1 to
4: 5–25%, 26–50%, 51–75%, and 76–100%, respectively) and
intensity of staining (in a scale from 1 to 3: weak, moderate, and
strong, respectively). An expression factor was assigned to each
slide which is a multiplication of the positivity and intensity
factors.
Tissue collection and RNA extraction
Tissues from 10 ccRCC tumors and their adjacent normal kidney
tissues were extracted and flash frozen in liquid nitrogen immediately afterwards. In total, 20 mg of tissue from each sample
were used in total RNA extraction using the miRNeasy Mini Kit
and its recommended protocol (Qiagen Canada, Mississauga,
ON, Canada).
Semi-quantitative RT-PCR
Total RNA RT reaction was performed on the RNA extractions
using the OmniScript RT kit and its recommended protocol (Qiagen Canada). Primers designed to amplify the cDNA sequences
of the targeted genes were obtained from Operon Biotechnologies (Huntsville, AL, USA); F2 forward primer: 59-ACT ACC GAG
GGC ATG TGA AC-39, F2 reverse primer: 59-GCT GCA CAG CTG
AGT TGA AG-39, KNG1 forward primer: 59-GGA ATC ACA GTC
CGA GGA AA-39, KNG1 reverse primer: 59-AAG TTC AAT CCA
GCC ACC AC-39, SERPING1 forward primer: 59-ATT CTC CTA
CCC AGC CCA CT-39, SERPING1 reverse primer: 59-GGC GTC
ACT GTT GTT GCT TA-39. The PCR reaction is performed with
melting temperature at 948C, annealing temperature at 608C,
and extension temperature at 728C for 30 cycles. The identity of
the products was verified by an automated DNA sequencer
(Applied Biosystems, Foster City, CA, USA). Equal amounts of
PCR products and a house-keeping gene ran on 1.5% agarose
gel and was visualized by ethidium bromide. Semi-quantitative
expression data were calculated by densitometry measurements
of band intensities.
Article in press - uncorrected proof
134 A.D. Romaschin et al.
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Received July 8, 2008; accepted October 1, 2008