Identification of genes and pathways involved in kidney renal clear cell carcinoma.

 
TitleIdentification of genes and pathways involved in kidney renal clear cell carcinoma.
Publication TypeJournal Article
Year of Publication2014
AuthorsYang, W, Yoshigoe, K, Qin, X, Liu, JS, Yang, JY, Niemierko, A, Deng, Y, Liu, Y, Dunker, A, Chen, Z, Wang, L, Xu, D, Arabnia, HR, Tong, W, Yang, M
JournalBMC Bioinformatics
Volume15 Suppl 17
PaginationS2
Date Published2014
ISSN1471-2105
KeywordsBiomarkers, Tumor, Carcinoma, Renal Cell, Case-Control Studies, Cluster Analysis, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Humans, Kidney, Kidney Neoplasms, Signal Transduction, Support Vector Machine
Abstract

BACKGROUND: Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development.

RESULTS: Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P

CONCLUSIONS: Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

DOI10.1186/1471-2105-15-S17-S2
Alternate JournalBMC Bioinformatics
PubMed ID25559354
PubMed Central IDPMC4304191
Grant List5P20GM10342913 / GM / NIGMS NIH HHS / United States
NHGRI 5U54HG003273-11 / / PHS HHS / United States