Abstract

   The effective detection and management of muscle-invasive bladder
   Transition Cell Carcinoma (TCC) continues to be an urgent clinical
   challenge. While some differences of gene expression and function in
   papillary (Ta), superficial (T1) and muscle-invasive (≥T2) bladder
   cancers have been investigated, the understanding of mechanisms
   involved in the progression of bladder tumors remains incomplete.
   Statistical methods of pathway-enrichment, cluster analysis and
   text-mining can extract and help interpret functional information about
   gene expression patterns in large sets of genomic data. The public
   availability of patient-derived expression microarray data allows open
   access and analysis of large amounts of clinical data. Using these
   resources, we investigated gene expression differences associated with
   tumor progression and muscle-invasive TCC. Gene expression was
   calculated relative to Ta tumors to assess progression-associated
   differences, revealing a network of genes related to Ras/MAPK and PI3K
   signaling pathways with increased expression. Further, we identified
   genes within this network that are similarly expressed in superficial
   Ta and T1 stages but altered in muscle-invasive T2 tumors, finding 7
   genes (COL3A1, COL5A1, COL11A1, FN1, ErbB3, MAPK10 and CDC25C) whose
   expression patterns in muscle-invasive tumors are consistent in 5 to 7
   independent outside microarray studies. Further, we found increased
   expression of the fibrillar collagen proteins COL3A1 and COL5A1 in
   muscle-invasive tumor samples and metastatic T24 cells. Our results
   suggest that increased expression of genes involved in mitogenic
   signaling may support the progression of muscle-invasive bladder tumors
   that generally lack activating mutations in these pathways, while
   expression changes of fibrillar collagens, fibronectin and specific
   signaling proteins are associated with muscle-invasive disease. These
   results identify potential biomarkers and targets for TCC treatments,
   and provide an integrated systems-level perspective of TCC pathobiology
   to inform future studies.

Introduction

   Bladder cancer is a disease receiving growing attention within the
   cancer biology community. Transition Cell Carcinoma (TCC) occurs as
   papillary tumors (Ta stage), superficial tumors (T1 stage), and
   muscle-invasive tumors of increasing severity (T2, T3 and T4 stage).
   Approximately 20% of primary bladder cancers are muscle-invasive at
   presentation and are associated with a poor prognosis, with 5 year
   survival estimates for muscle-invasive TCC approaching the low survival
   rates for advanced metastatic pancreatic cancers, small cell lung
   cancers, liver and bile-duct cancers, stomach and non-small cell lung
   carcinomas [29][1], [30][2]. Although papillary and superficial tumors
   recur in 70% of patients after surgical removal, non-invasive tumors
   have a more favorable outcome than muscle-invasive tumors as only
   10–20% of these recurrences progress to muscle-invasive disease
   [31][2]. The regulatory mechanisms that are altered and disrupted in
   muscle-invasive bladder cancer may represent a barrier to progression
   in superficial tumors, and are candidate targets for therapeutic
   intervention.

   Accumulating evidence suggests that superficial and muscle-invasive
   tumors are pathobiologically distinct [32][3], [33][4]. Superficial
   tumors frequently overexpress or express constitutively active mutants
   of HRAS and FGFR3 leading to hyperactivated Ras/MAPK signaling activity
   [34][3], [35][4]. Muscle-invasive tumors demonstrate disrupted activity
   of p53 and Rb and other tumor suppressors, overexpress EGFR and ErbB2,
   MMP2 and MMP9, and other pro-angiogenic factors, while having deleted
   cyclin-dependent kinase inhibitor genes CDKN2A (p16^Ink4a) and CDKN2B
   (p15^Ink4b) [36][4]. However, there is evidence that suggests that Ta
   tumors in some patients may progress and become muscle-invasive. In
   addition to observations that 10–20% of patients who initially have Ta
   tumors later develop muscle-invasive disease, tumors ≥T1 share common
   chromosomal deletions, gains and amplifications that are distinct from
   those found in Ta stage tumors, suggesting that accumulated chromosomal
   aberrations may be involved in the progression from papillary to
   muscle-invasive tumors [37][1], [38][2]. Moreover, while activating
   mutations in FGFR3, ras isoforms, and PI3K are more common in papillary
   tumors, most high grade superficial tumors lack these mutations and are
   similar to invasive tumors, suggesting that these may be predisposed to
   progress to muscle-invasive disease [39][5]. At the same time, some
   tumor recurrences lose the activating mutations that are present in
   earlier tumors, which may also potentially drive progression [40][5].
   In all, the relationship of superficial and muscle-invasive bladder
   cancer remains controversial and largely unresolved.

   There are few effective systemic treatments for muscle-invasive bladder
   cancer, and an improved understanding of the molecular pathogenesis and
   progression of TCC is urgently needed. Analysis mRNA expression in
   different stages of bladder cancer could illustrate differences that
   exist which may promote progression from Ta and T1 tumors to higher
   stage recurrences, while similarities in expression patterns could
   clarify the relationship of tumor stages in progression of the disease.
   Insights into these molecular mechanisms of bladder tumor progression
   can provide targets for preventative and therapeutic interventions
   while providing biomarkers that reliably predict progression into
   muscle-invasive disease.

   Microarray technology provides a powerful tool to measure mRNA
   expression across the entire genome of biological samples, allowing
   detailed analysis of large numbers of experimental and clinical samples
   in relatively little time. Typically in studies of various cancers,
   clinical samples are analyzed to identify genes expressed at relatively
   high or low amounts in common patterns that correlate with tumor stage
   and patient survival [41][6]. While this approach may be appropriate
   for identifying potentially useful diagnostic and prognostic
   biomarkers, it is often difficult to determine whether signature genes
   in advanced cancers are functionally relevant to tumor progression.
   Strategies to derive functional information from gene-expression
   datasets include expression clustering to identify genes with similar
   patterns of expression [42][7]and pathway enrichment programs including
   WebGestalt [43][8] that query Gene Ontology, Kyoto Encyclopedia of
   Genes and Genomes (KEGG), and other databases to identify processes in
   which gene expression changes are focused. Another strategy is to
   analyze the published literature using automated text-mining programs
   such as PubGene and Chilibot to identify functional associations
   between genes, phenotypes and diseases within publications indexed in
   PubMed [44][9]–[45][12]. Such resources have made computational
   informatic tools available to scientists who need to analyze and
   interpret microarray data, yet few studies have taken advantage of this
   information.

   Studies of whole-genome expression using microarray technology require
   resources that may not be widely available to all researchers. Reliable
   microarray studies require large numbers of samples to provide for an
   adequate analysis and can be prohibitively expensive and time
   consuming. Access to human tissues required for relevant analyses is
   often not available to researchers outside of the medical field. The
   establishment of public microarray data archives, including the Gene
   Expression Omnibus ([46]http://www.ncbi.nlm.nih.gov/geo/), has allowed
   free-access to a wealth of clinical and experimental data that can be
   analyzed and re-purposed by any investigator with the interest and the
   means to do so. The development of open-access analytical programs,
   including Oncomine ([47]https://www.oncomine.org) [48][13] and Mayday
   [49][14], provide a means to investigate gene expression in data across
   multiple microarray studies. These advances allow researchers to pursue
   genome-scale investigations in clinically-derived human tissues without
   the time, expense, and administrative efforts associated with
   generating primary genomic data.

   Here, we use public data to identify genes that are associated with TCC
   tumor progression and muscle-invasive disease in male patients without
   metastases. We find that the differential expression of a network of
   genes related to the mitogenic Ras/MAPK and related signaling pathways
   are associated with progression beyond Ta stage. Additionally, we
   identified a subset of 7 genes within this network whose expression is
   associated with muscle-invasive TCC.

Materials and Methods

Microarray Dataset

   The Gene Expression Omnibus (GEO) website
   ([50]http://www.ncbi.nlm.nih.gov/geo/) was used to search publicly
   available datasets for recent studies of bladder cancers with at least
   3 samples representing all tumor grades and performed using up-to-date
   whole-genome microarray chips. We chose a dataset (GEO# GSE 31684;
   accessed 2/29/2012) which was originally used to develop predictive
   models of bladder cancer outcome [51][15], as it fulfilled these
   requirements and provided detailed patient data. Data was further
   selected from male TCC patients without associated metastases,
   carcinoma in situ or sarcoma to reduce potential variation in data,
   resulting in a total of 36 patient datasets ([52]Table 1).

Table 1. Tumor stage, sample size and age range of data selected from GSE
31684 dataset.

   Tumor Stage n =  Age Range
   Ta          3    57–78
   T1          7    57–74
   T2          10   51–80
   T3          9    66–84
   T4          7    50–83
   [53]Open in a new tab

Data selection, calculations and statistics

   The meta-analysis of public data was performed according to PRISMA
   guidelines ([54]Table S1, [55]Table S2). Microarray expression data was
   organized, managed, and calculated using Microsoft Excel (Redmond, WA).
   The microarray expression data of all 36 sample datasets were expressed
   in log[2] scale, and the average expression values were calculated for
   samples of each pathological tumor grade. To identify general
   expression changes associated with progression of TCC, the gene probe
   expression values of tumor stage T1, T2, T3 and T4 samples were
   individually normalized to Ta by subtracting their log[2] averages.
   Gene probes were selected based on both an increase or decrease in
   expression by a factor of 2 (log[2]1/−1) and statistical significance
   (p<0.05) in at least one tumor stage based on Student's t-test as a
   more stringent method of selection than either criterion alone. To
   identify expression changes associated with muscle-invasive disease,
   genes were selected that did not significantly change from Ta to T1 but
   are significantly different by a factor of 2 in T2 tumors in addition
   to significance based on Student's t-test. Further, the average
   expression values of selected gene probes in each tumor grade were
   compiled, and 3×3 matrix self-organizing map-based clustering analysis
   of relative gene expression across tumor stages was performed with
   10,000 iterations using Mayday [56][14]. Clusters of gene probes with
   minimal expression changes, representing apparent false-positives, were
   arbitrarily omitted from subsequent analysis.

Statistical pathway enrichment analysis of differentially expressed genes

   To perform pathway enrichment analysis on selected genes, gene probe
   IDs were annotated with Entrez gene symbols (Affymetrix Human Genome
   U133 Plus 2.0 [HG-U133_Plus_2]; GEO# [57]GPL570) and analyzed using the
   WebGestalt website ([58]http://bioinfo.vanderbilt.edu/webgestalt/) to
   identify defined KEGG pathways that are significantly over-represented
   in the dataset [59][8], using default settings and the Fisher's Exact
   test, selecting pathways where p<0.01. These results produced lists of
   the genes included in the enriched pathways, and provided a
   connectivity map of each pathway. A model network based on these
   pathways was generated using the Visual Understanding Environment
   ([60]http://vue.tufts.edu/). Gene expression heatmaps were generated
   using Mayday ([61]http://www-ps.informatik.uni-tuebingen.de/mayday/wp/)
   [62][14].

Statistical validation of muscle-invasive tumor gene signature using outside
datasets

   To determine whether muscle-invasive-specific expression changes
   identified in our initial dataset are typical across an expanded number
   of samples and studies, we obtained expression data for each gene
   compiled within the Oncomine database [63][13]. This included 8
   datasets from 7 studies in which gene expression is measured in
   superficial and muscle-invasive bladder tumors [64][16]–[65][22]. The
   median expression of each gene in superficial and muscle-invasive
   tumors from each dataset was recorded and the averaged difference was
   calculated. A Mann-Whitney Rank-Sum test was performed to determine the
   significance of expression changes across datasets.

Bibliomic text-mining for functional gene association

   To obtain published results that link functional interactions of genes
   and phenotypes, selected genes were analyzed using PubGene [66][10],
   while genes and keywords “bladder cancer”, “metastasis”,
   “angiogenesis”, “invasive” were analyzed using Chilibot [67][11]. An
   association network based on co-occurrances and implied functional
   relationships determined by each program was constructed based on these
   results.

Immunohistochemistry of Bladder Tumors

   Samples (n = 5 each) of normal bladder and T1, T2 and T3 bladder tumors
   were purchased from Biomax.us (Rockville, MD). Samples were processed
   and stained by the TRIP Laboratory facility (Depatment of Pathology,
   University of Wisconsin School of Medince and Public Health) using
   standard immunostaining procedures. Antibodies to COL3A1 and COL5A1
   were purchased from Santa Cruz Biotechnology (Santa Cruz, CA) and
   visualized using DAB and Warp Red stains. Nuclei were visualized using
   hematoxylin staining. Microscopy and image processing was performed as
   previously described [68][23].

Western Immunoblotting

   T24 bladder cancer cells and E6-immortalized human urothelial cells
   (HUCs) were the kind gift of Dr. Dale Bjorling, University of
   Wisconsin. T24 cells were cultured in DMEM media +10% FBS, while HUCs
   were cultured in Ham's F12 media +10% FBS. Cells were cultured to 70%
   confluence, scraped from plates, collected, solubilized and processed,
   analyzed by western immunoblotting, and quantified using Image J, as
   previously described [69][23]. Antibodies to COL3A1 and COL5A1 are
   described above. Antibodies recognizing β-actin were used as a loading
   control.

Results

TCC progression is associated with differential expression of Ras/MAPK and
related pathway genes

   Our hypothesis was that gene expression differences exist between
   papillary/superficial and muscle-invasive bladder TCC tumors that
   define the regulation of tumor invasion, progression and metastasis. We
   developed an intuitive method, summarized in [70]Figure 1, to select,
   analyze and interpret public microarray data using open access
   computational resources. We found a suitable dataset from the GEO
   database ([71]GSE31684) containing expression data from 93 patient
   bladder tumors that were removed prior to any chemical treatment and
   used to identify gene expression signatures that predict outcome of
   high-risk bladder cancer [72][15]. To limit potential variation in
   sample expression data, sample data were selected from male patients
   with TCC of the bladder, without metastatic tumors, CIS or sarcoma,
   producing 36 total datasets that represented at least three patients
   per stage for all stages of TCC tumors ([73]Table 1).

Figure 1. Flow diagram detailing the methods used to identify genes
associated with TCC progression and muscle-invasive behavior from expression
microarray data.

   [74]Figure 1
   [75]Open in a new tab

   First, we calculated expression differences between Ta and T1–T4
   bladder tumors to identify changes broadly associated with bladder
   cancer progression beyond papillary tumor stages. Data were processed,
   averaged, normalized to the expression of Ta tumors, and selected based
   on a greater than 2-fold threshold difference in expression at any
   stage beyond Ta, and p<0.05 using Student's t-test. This produced a
   list of 8110 gene-associated probes. Expression data was retrieved for
   those selected probes and analyzed using the self-organizing map
   cluster analysis within Mayday [76][14] to arbitrarily remove probes
   that reflect little change in expression, selecting 5509 total probes.

   As receptor tyrosine kinases, mitogenic signaling pathways and tumor
   suppressors are each involved in superficial and muscle-invasive
   bladder cancer, our aim was to identify changes in gene expression that
   could compliment the activities of these pathways. The list of selected
   probes was analyzed with WebGestalt [77][8] to identify defined
   pathways that are significantly represented in the microarray data
   (p<0.05). Whether the selected genes were analyzed as a single group or
   separated based on increased or decreased expression, many of the
   pathways reliably associated with these genes are related to
   cancer-related signal transduction and similar pathways ([78]Table 2,
   [79]Table S3). The ten best supported pathways associated with the
   entire set of genes include “Cell Cycle”, “Pathways in Cancer”, and
   “Focal Adhesion”. Beyond these ten, “MAPK signaling”, “ErbB signaling”
   and “Bladder Cancer” pathways were also well supported. Likewise, when
   this list was separated into separate lists of 4816 increasing and 693
   decreasing genes, many similar cancer-related signal transduction
   pathways were found to be represented in these genes. While other
   pathways represented in the data could also be associated with cancer
   progression and metastasis, such as “Metabolism” and “DNA Repair”, the
   relationship of these pathways to current models of bladder progression
   is less direct. These results suggest that bladder cancer progression
   from Ta to T1+ stage tumors involves the increased expression of genes
   in mitogenic, cancer-associated pathways related to FGFR3 and ErbB
   family signaling.

Table 2. KEGG pathway enrichment of genes differentially expressed in T1–T4
staged bladder tumors versus Ta-stage tumors.

   KEGG Pathway Number of Pathway Genes in Dataset Enrichment Factor “R”
   Fisher's Exact, Adjusted P-value
   All Changes, Top 10
   Metabolic pathways 332 3.46 5.26E-93
   Cell cycle[80]^* 68 6.11 2.46E-36
   Spliceosome 68 6.11 2.46E-36
   DNA replication 29 9.27 3.29E-23
   Pyrimidine metabolism 47 5.52 1.05E-22
   Ubiquitin mediated proteolysis 54 4.50 6.33E-21
   Pathways in cancer[81]^* 86 3.00 2.50E-19
   Focal adhesion[82]^* 63 3.61 1.30E-18
   Huntington's disease 60 3.73 1.32E-18
   Purine metabolism 52 3.96 1.75E-17
   Selected Pathways
   MAPK signaling pathway[83]^* 60 2.57 6.88E-11
   ErbB signaling pathway[84]^* 26 3.44 5.92E-08
   Bladder cancer[85]^* 16 4.38 7.25E-07
   Genes Up, Top 10
   Metabolic pathways 299 3.57 1.64E-85
   Spliceosome 66 6.79 1.34E-37
   Cell cycle[86]^* 62 6.38 2.13E-33
   DNA replication 29 10.61 7.10E-25
   Pyrimidine metabolism 46 6.18 3.64E-24
   Ubiquitin mediated proteolysis 50 4.77 3.54E-20
   Huntington's disease 58 4.13 7.22E-20
   Purine metabolism 51 4.45 3.88E-19
   Oocyte meiosis 42 4.85 1.95E-17
   Alzheimer's disease 50 3.91 4.12E-16
   Parkinson's disease 43 4.26 1.71E-15
   Genes Down, Top 10
   Complement and coagulation cascades 11 14.12 1.22E-08
   Pathways in cancer[87]^* 21 5.64 1.22E-08
   Focal adhesion[88]^* 17 7.49 1.22E-08
   MAPK signaling pathway[89]^* 17 5.60 4.00E-07
   Small cell lung cancer 10 10.54 8.56E-07
   Metabolic pathways 35 2.81 1.07E-06
   p53 signaling pathway 9 11.55 1.31E-06
   Tight junction 11 7.27 5.35E-06
   NOD-like receptor signaling pathway 8 11.43 5.84E-06
   Metabolism of xenobiotics by cytochrome P450 8 10.12 1.36E-05
   [90]Open in a new tab
   ^(*)

   indicates KEGG pathways that involve mechanisms of signal transduction.
   P value represents the results of Fisher's Exact tests reported by
   WebGestalt.

   KEGG pathways and other related ontologies and annotations are highly
   redundant, with individual genes associated with many multiple limited
   and interconnecting pathways. For this and other reasons, pathway
   enrichment analysis alone often does not adequately represent the
   global scope and relationships within the data. To better understand
   the relationships of these genes and how they may function in bladder
   cancer progression, we built an integrated pathway network model based
   on the KEGG maps of selected signaling pathways represented in the data
   ([91]Table 2; [92]Table S3). These results reveal that gene expression
   differences between Ta and T1 stage tumors largely occur within a
   signaling network related to Ras/MAPK signaling pathways, with
   increased expression of a majority of these genes ([93]Figure 2).

Figure 2. Altered expression in a network of Ras/MAPK associated signaling
pathway genes in TCC progression.

   [94]Figure 2
   [95]Open in a new tab

   The stimulatory and inhibitory interactions of gene products
   differentially expressed in T1–T4 versus Ta tumors were mapped based on
   KEGG pathway maps. Node color denotes relative expression:
   Yellow = Increased expression in muscle-invasive tumors;
   Blue = Decreased expression in muscle-invasive tumors. Green and red
   arrows represent stimulatory and inhibitory interactions, respectively.
   The names of genes with supported associations with muscle-invasive
   tumors are highlighted in bold italics.

Muscle-invasive TCC is associated with specific expression changes in ECM and
signaling proteins

   We further hypothesized that the expression of a subset of the genes
   within this signaling network model is specifically altered in
   muscle-invasive versus superficial tumors. Returning to the complete
   dataset, we selected genes that are expressed at similar levels within
   a 2 fold difference between Ta and T1 tumors, and are changed greater
   than 2 fold in T2 stage tumors with Student's t-test p<0.05 ([96]Figure
   1). After performing expression clustering analysis and arbitrarily
   removing clusters with minimal expression changes, as above, 496 total
   gene probes were selected for pathway enrichment analysis using
   WebGestalt. As before, these results show that the selected genes are
   associated with many signal transduction and cancer-related pathways
   that involve 23 of the selected genes ([97]Table 3, [98]Table S4,
   [99]Figure 3). These genes were not focused in any single pathway, but
   instead were distributed across the network of pathways ([100]Figure
   2). Visualizing gene expression data relative to Ta tumors in a
   heatmap, the expression changes of selected genes are consistent in T2,
   T3 and T4 tumors ([101]Figure 3). These results suggest that the
   differences of selected gene expression are distinct fundamental
   molecular characteristics of muscle-invasive tumors.

Table 3. KEGG pathway enrichment of genes differentially expressed in T2
versus Ta and T1 stage bladder tumors.

   KEGG Pathway Number of Pathway Genes in Dataset Enrichment Factor “R”
   Fisher's Exact, Adjusted P-value
   All Changes, Top 10
   Focal adhesion[102]^* 14 8.4 1.44E-07
   Tight junction 11 9.92 7.39E-07
   Pancreatic cancer 8 13.43 4.23E-06
   Androgen and estrogen metabolism 6 16.12 3.96E-05
   Adherens junction 7 10.99 6.08E-05
   ECM-receptor interaction 7 10.08 9.07E-05
   Retinol metabolism 6 11.33 2.00E-04
   MAPK signaling pathway[103]^* 11 4.94 2.00E-04
   Ascorbate and aldarate metabolism 4 19.34 5.00E-04
   Drug metabolism – other enzymes 5 11.85 5.00E-04
   Selected Pathways
   Pathways in cancer[104]^* 10 3.6 1.90E-03
   Cell cycle[105]^* 6 5.67 2.30E-03
   Genes Up, Top 10
   Focal adhesion[106]^* 11 12.79 5.49E-08
   ECM-receptor interaction 7 19.48 1.66E-06
   Cell cycle[107]^* 5 9.13 2.10E-03
   MAPK signaling pathway[108]^* 7 6.08 2.10E-03
   Tight junction 5 8.72 2.50E-03
   Regulation of actin cytoskeleton 6 6.49 2.70E-03
   Progesterone-mediated oocyte maturation 4 10.87 2.90E-03
   Valine, leucine and isoleucine degradation 3 15.93 4.60E-03
   Sulfur metabolism 2 35.95 6.40E-03
   Vascular smooth muscle contraction 4 8.13 6.60E-03
   Genes Down, Top 10
   Retinol metabolism 6 12024 1.09E-05
   Androgen and estrogen metabolism 5 27.68 1.63E-05
   Adherens junction 6 19.41 1.63E-05
   Drug metabolism – other enzymes 5 24.42 2.29E-05
   Ascorbate and aldarate metabolism 4 39.86 2.73E-05
   Pentose and glucuronate interconversions 4 36.91 3.14E-05
   Pancreatic cancer 5 17.3 5.67E-05
   Drug metabolism – cytochrome P450 5 17.3 5.67E-05
   Metabolism of xenobiotics by cytochrome P450 5 17.79 5.67E-05
   [109]Open in a new tab
   ^(*)

   indicates KEGG pathways that involve mechanisms of signal transduction.
   P value represents the results of Fisher's Exact tests reported by
   WebGestalt.

Figure 3. Expression heatmap of candidate muscle-invasive genes.

   Figure 3
   [110]Open in a new tab

   Yellow and blue indicate increased and decreased expression in
   candidate muscle-invasive genes, respectively, relative to the average
   expression in Ta tumors.

   We then determined whether the relative expression of the selected
   genes could be observed in outside microarray datasets of superficial
   and muscle-invasive bladder tumors. Using Oncomine [111][24], we
   identified archived datasets containing expression data for each of the
   23 selected candidate genes. The median gene expression of non-invasive
   and invasive tumors in each dataset was recorded, and the average
   change in expression of each gene was calculated across all datasets
   ([112]Table 4). The Mann-Whitney Rank-Sum test was performed to
   determine the significance of the changes in median gene expression
   across datasets. These results show that expression of extracellular
   matrix genes COL3A1, COL5A1, COL11A1 and FN1 are significantly
   increased in muscle-invasive bladder tumors relative to Ta/T1 (p<0.05),
   while changes in CDC25C, MAPK10 and ErbB3 expression approached
   significance (p≤0.08) ([113]Table 4). These final selected genes
   represent a sub-network of consistently-observed differences in gene
   expression of muscle-invasive and non-invasive bladder tumors that are
   associated with the activity of Ras/MAPK, PI3K and other signaling
   pathways ([114]Figure 4).

Table 4. Average normalized median expression of selected genes
differentially expressed in non-invasive and muscle-invasive TCC in at least
3 datasets within the Oncomine Collection.

   Gene Average Change in Median (Fold Increase vs Superficial) St.Dev.
   Mann-Whitney Rank-Sum p =  References