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ORIGINAL ARTICLE
Year : 2012  |  Volume : 8  |  Issue : 1  |  Page : 28-33

Transcriptome network analysis reveals candidate genes for renal cell carcinoma


Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China

Date of Web Publication19-Apr-2012

Correspondence Address:
Jun-Hua Zheng
Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.95170

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 > Abstract 

Context: Renal cell carcinoma (RCC) is a kidney cancer that originates in renal parenchyma and it is the most common type of kidney cancer with approximately 80% lethal cases.
Aims: To interpret the mechanism, explore the regulation of TF-target genes and TF-pathway, and identify the potential key genes of renal cell carcinoma.
Settings and Design: After constructing a regulation network from differently expressed genes and transcription factors, pathway regulation network and gene ontology (GO) enrichment analysis were made.
Materials and Methods: The gene expression profile set GSE6344, a renal cell carcinoma sample set, was collected from NCBI, pathway data from KEGG, and regulationship data from database TRANSFAC and TRED.
Statistical Analysis Used: Besides different expressed genes obtained by limma, impact analysis method and GO enrichment were applied to find the significant expressed pathways.
Results: Finally, we constructed a TF-target gene and TF-pathway regulation network of renal cell carcinoma. And some genes proved to be highly related to renal cell carcinoma were identified.
Conclusions: This study illustrated that by incorporating significantly expressed pathway into a regulation network based analysis, one can derive greater insights into the underlying mechanisms of renal cell carcinoma.

Keywords: Gene ontology, pathway, regulation network, renal cell carcinoma


How to cite this article:
Zhai W, Xu YF, Liu M, Zheng JH. Transcriptome network analysis reveals candidate genes for renal cell carcinoma. J Can Res Ther 2012;8:28-33

How to cite this URL:
Zhai W, Xu YF, Liu M, Zheng JH. Transcriptome network analysis reveals candidate genes for renal cell carcinoma. J Can Res Ther [serial online] 2012 [cited 2020 Jul 3];8:28-33. Available from: http://www.cancerjournal.net/text.asp?2012/8/1/28/95170


 > Introduction Top


Renal cell carcinoma (RCC) is a kidney cancer that originates in renal parenchyma. RCC is the most common type of kidney cancer and it is the most lethal of all the genitourinary tumors in adults, responsible for approximately 80% of cases. [1]

RCC is not a single entity, but comprises a group of tumors including clear cell RCC, papillary RCC and chromophobe RCC, which arise from the epithelium of renal tubules. [2] The majority of clear cell RCCs has genetic or epigenetic inactivation of the von Hippel-Lindau (VHL) gene, which is one component of the E3 ubiquitin-ligase complex, along with elongin B, elongin C, and cullin 2. VHL complex mutation led to HIF accumulation, high expression of VEGF, VEGF receptor (VEGFR), PDGF receptor, and basic fibroblast growth factor (bFGF) to cause carcinogenesis finally. [3],[4] Germline mutations in the MET and fumarate hydratase genes led to the development of type 1 and type 2 papillary RCCs, respectively, and such mutations of either the TSC1 or TSC2 gene increased the risk of RCC. For the above reasons, VEGF, PDGF, EGFR, CAIX, and mTOR, etc, have been shown to be suitable molecular targets in the treatment of RCC. [5]

DNA microarray has been used to find the gene expression patterns in renal cell carcinoma. The regulation network has been used to investigate the pathogenesis in the breast cancer and pancreatic cancer, [6],[7] based on the microarray data. Thus, this study applied the regulation network analysis to find the potential regulationships and investigate the mechanism respond to RCC.


 > Materials and Methods Top


A RCC gene expression profile GSE6344, [8] with 10 RCC samples and 10 controls, was obtained from NCBI (http://www.ncbi.nlm.nih.gov/geo/). The tissue samples for the microarray study consisted of 10 patient-matched normal renal cortex and ccRCC tissues, five from stage I and five from stage II ccRCC. Both early-stage I and II tumors were localized disease; stage I tumors were less than 7 cm and stage II tumors were greater than 7 cm. The differentially expressed genes (DEGs) were identified by the limma software package of Bioconductor with default settings. Those DEGs with fold change larger than 2 were used for the further analysis.

Pathway data and regulation data

Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of online databases dealing with genomes, enzymatic pathways, and biological chemicals. [9] The pathway database records networks of molecular interactions in the cells, variants of which are specific to particular organisms (http://www.genome.jp/kegg/). Total 130 human pathways, involving 2287 genes, were collected for our study.

TRANSFAC database contains data on transcription factors (TFs), including their experimentally-proven binding sites, and regulated genes. [10] 774 pairs of regulatory relationship between 219 TFs and 265 target genes were collected from TRANSFAC (http://www.gene-regulation.com/pub/databases.html).

Transcriptional Regulatory Element Database (TRED) has been built in response to increasing needs of an integrated repository for both cis- and trans- regulatory elements in mammals. [11] TRED made the curation for transcriptional regulation information, including transcription factor binding motifs and experimental evidence. The curation is currently focusing on target genes of 36 cancer-related TF families. 5722 pairs of regulatory relationship between 102 TFs and 2920 target genes were collected from TRED (http://rulai.cshl.edu/TRED/).

By combining the two regulation datasets, a total of 6328 regulatory relationships between 276 TFs and 3002 target genes were kept for constructed regulation network.

Co-expression analysis

For the gene expression profile data, the limma method was used to identify DEGs. [12] The original expression datasets from all conditions were processed into expression estimates using the RMA method with the default settings implemented in Bioconductor, and then construct the linear model. The DEGs with the absolute fold change value > 2 and P-value < 0.05 were selected. For demonstrating the potential regulatory relationships, the Pearson correlation coefficient (PCC) was calculated for all pair-wise comparisons of gene-expression values between TFs and DEGs. The regulationship with PCC > 0.6 was considered as significant.

Gene ontology enrichment

BiNGO is an open-source Java tool to determine which gene ontology (GO) terms are significantly overrepresented in a set of genes. [13] The hypergeometric test was applied to identify over-represented GO categories in biological process with P-value < 0.01.

Regulation network construction

Using the regulation data that have been collected from TRANSFAC database and TRED database, we matched the relationships between differentially expressed TFs and its differentially expressed target genes.

Based on the above two regulation datasets and the pathway relationships of the target genes, we build the regulation networks using Cytoscape. [14] Based on the significant relationships (PCC > 0.6 or PCC < -0.6) between TFs and their target genes, 101 putative regulatory relationships were predicted between 11 TFs and 77 target genes.

Pathway significance analysis and pathway regulation network construction

We adopted an impact analysis that includes not only the statistical significance of the set of pathway genes, but also considers other crucial factors such as the magnitude of each gene's expression change, the topology of the signaling pathway, and their interactions, etc. [15]

In this model, the impact factor (IF) of a pathway Pi is calculated as the sum of two terms (Formula 1):



The first term is a probabilistic term that captures the significance of the given pathway Pi from the perspective of the set of genes contained in it.

The second term is a functional term that depends on the identity of the specific genes that are differentially expressed as well as on the interactions described by the pathway. [15]

To further investigate the regulationship between TFs and pathways, we mapped the DEGs to pathways and got a regulation network between TFs and pathways.


 > Results Top


TF-target gene regulation network construction

To get DEGs of renal cell carcinoma, we obtained publicly available microarray data sets GSE6344 from GEO. After microarray analysis of GSE6344, the 1551 DEGs with the log fold change value larger than 2 and P-value less than 0.05 were selected.

To get the regulationship, the PCC value < 0.6 was chosen as the threshold for co-expression of TF and target genes. Finally, 101 regulationships between 11 TFs and their 77 differently expressed target genes were collected. Integrating above regulatory relationships, a regulation network of renal cell carcinoma was built between TFs and their target genes [Figure 1]. In this network, MYC, SP3, ETS2, and STAT1 with higher degree (>5) formed 4 sub-networks.
Figure 1: TF-target gene regulation network of renal cell carcinoma

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GO enrichment

Several GO categories were enriched among these genes in the regulatory network, including response to endogenous stimulus, response to organic substance, homeostatic process and regulation of biological quality [Table 1].
Table 1: Gene ontology enrichment results

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TF-pathway regulation network

Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. Pathway provides an alternative way to relax the significance threshold applied to single genes and may lead to a better biological interpretation. So, we adopted a pathway-based impact analysis method on DEGs. Some significant expressed pathways were found, including Adherens junction, phosphatidylinositol signaling system, circadian rhythm and so on [Table 2].
Table 2: Significantly expressed pathways

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To further investigate the regulationships between TFs and pathways, we mapped DEGs to the collected pathways and constructed a TF-PATHWY regulation network [Figure 2]. In the network, MYC, SP3, and STAT1 were hub nodes linked to lots of RCC related pathways. Some of TFs interactively regulated lots of pathways, such as Adherens junction regulated by SP3, STAT1, and MYC.
Figure 2: TF-pathway regulation network

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 > Discussion Top


According to our analysis results, we found that many TFs and pathways closely related with RCC had been linked by our method, such as WT1, MYC, SP3, STAT1, adherens junction, cell cycle. We would discuss the relationship between RCC and identified genes as follows, based on previous reports.

WT1 gene encodes a transcription factor that has an essential role in the normal development of the urogenital system. The aberrant expression of functional WT1 in RCC may represent a reversion to a more de-differentiated phenotype and may contribute to the tumorigenic phenotype by inappropriately activating or repressing genes involved in growth regulation, [16] such as hTERT. WT1 demonstrated lower RNA expression in RCC compared with renal cortical tissue, whereas hTERT was increased, showing a negative correlation between WT1 and hTERT in RCC regulation. [17]

SP3 gene belongs to a family of SP1 related genes that encode transcription factors that regulate transcription by binding to consensus GC- and GT-box regulatory elements in target genes. SP1/SP3 contributed to the regulated expression of HIF-1 and CAIX gene in RCC. [18],[19]

START STAT1, as a transcription factor, can be activated by various ligands including interferon-alpha, interferon-gamma, EGF, PDGF, and IL6 in response to different cell stimuli and pathogens. STAT1 expression was significantly greater in human RCC samples and RCC cell lines. STAT1 expression was reduced by both fludarabine and siRNA, significantly increasing the radiosensitivity in both RCC cell lines suggesting that STAT1 may play a key role in RCC radioresistance and manipulation of this pathway may enhance the efficacy of radiotherapy. [20]

MYC protein is a multifunctional, nuclear phosphoprotein that plays a role in cell cycle progression, apoptosis and cellular transformation. The up-regulation of MYC expression was validated in RCC tissues and cell lines. Furthermore, knockdown of MYC expression by MYC-specific siRNA significantly inhibited the abilities of uncontrolled proliferation, anchorage-independent growth and arrested cell cycle in the G0/G1 phase in RCC cells. Moreover, knockdown of MYC also suppressed the expression of these MYC-target genes, such as BCL2, CCND1, PCNA, PGK1, and VEGFA in RCC cells. All these results suggest that MYC pathway is activated and plays an essential role in the proliferation of RCC cells. [21]

NOL3 gene encodes an anti-apoptotic protein that has been shown to down-regulate the enzyme activities of caspase 2, caspase 8 and tumor protein p53. Dysfunction in apoptosis plays a role in development of RCC. NOL3 was found significantly up-regulated in RCC RNA microarray. NOL3 had significantly decreased whole-cell protein expression, but had strongly localized nuclear positivity in RCC in the immunohistochemistry. Hence, NOL3 may have the potential for improving patient outcome in RCC. [22]

CCND1 protein belongs to the highly conserved cyclin family, whose activity is required for cell cycle G1/S transition. Aberrations in the G1/S transition of the cell cycle have been observed in many malignancies. Similarly, conclusive evidences indicated that CCND1 is over-expressed in RCCs. [23] Further study indicated that RCC is associated with mutation of the von Hippel-Lindau (VHL) tumor suppressor gene. But CCND1 is over-expressed and remains inappropriately high in during contact inhibition in pVHL-deficient cell lines. This suggests the loss of pVHL leads to constitutively elevated cyclin D1 and abnormal proliferation under normal growth conditions. [24],[25]

The proto-oncogene MET product is the hepatocyte growth factor receptor. MET was diffusely and strongly expressed in papillary RCC and collecting duct carcinomas. On the contrary, clear cell RCC, chromophobe RCC, and oncocytomas were negative or focally positive for MET expression. In conclusion, MET expression in papillary RCC and collecting duct carcinoma might be helpful in discriminating from the other subtypes of RCC with tubular or papillary growth. In case of MET expression observed in clear cell RCC, it might correlate with those clinicopathological parameters implying aggressive behavior. [26]

ISG15 is an ubiquitin-like protein that becomes conjugated to many cellular proteins upon activation by interferon-alpha and -beta. ISG15 was found differentially up-expressed in RCC cell lines and were frequently methylated. [27]

E-cadherin, which is the principal component of adherens junctions in epithelial cells, mediates adhesion by homophilic interactions between cells. A defining step in the pathogenesis of RCC is the epithelial-mesenchymal transition, during which E-cadherin-mediated cell-cell adhesion is lost due to loss of VHL expression in RCC, and cells acquire invasive and metastatic properties in the end. [28]

The cell cycle is tightly regulated through a complex network of positive and negative regulatory molecules including cyclin dependent kinases (Cdks), cyclins, and Cdk inhibitors (Cdkis). RCC cell lines development was the consequence of impaired cell cycle pathway, such as over-expressed CCND1, decreased expression of phosphorylated pRb, and down-regulated B-cell translocation gene 2 (BTG2). [29],[30] Therefore, it has been one potential chemo- or radiotherapeutic molecular target for RCC therapy. [31]

In conclusion, we used network analysis as a conceptual framework to explore the pathobiology of RCC, based on the assumption that RCC is a contextual attribute of distinct patterns of interactions between multiple genes. The salient results of our study include many related transcription factors, target genes and pathways such as WT1, MYC, SP3, STAT1, adherens junction, and cell cycle, which all have related with RCC in direct or indirect manner based on above discussions. However, further experiments are still indispensable to confirm our conclusion.

 
 > References Top

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