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

BMC Bioinformatics

Research

Yunlong Liu¹  A Keith Dunker¹  William Yang²  Kenji Yoshigoe²  Hamid R Arabnia³  Dong Xu⁴  Zhongxue Chen⁵  Liangjiang Wang⁶  Jun S Liu⁷  Andrzej Niemierko⁸  Jack Y Yang⁹  Weida Tong^1,10  Xiang Qin^1,11  Mary Qu Yang^1,12  Youping Deng^1,13 

[1] Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 46202, Indianapolis, Indiana, USA;Department of Computer Science, George W. Donaghey College of Engineering and Information Technology, University of Arkansas at Little Rock, 2801 S. University Avenue, 72204, Little Rock, Arkansas, USA;Department of Computer Science, University of Georgia, 30602, Athens, Georgia, USA;Department of Computer Science, University of Missouri, 65211, Columbia, Missouri, USA;Department of Epidemiology and Biostatistics, Indiana University School of Public Health, 1025 E. 7th Street, PH C104, 47405, Bloomington, Indiana, USA;Department of Genetics and Biochemistry, Clemson University, 29634, Clemson, South Carolina, USA;Department of Statistics, Harvard University, 02138, Cambridge, Massachusetts, USA;Division of Biostatistics and Biomathematics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, 02114, Boston, Massachusetts, USA;Division of Biostatistics and Biomathematics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, 02114, Boston, Massachusetts, USA;Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 46202, Indianapolis, Indiana, USA;Divisions of Bioinformatics and Biostatistics, National Center for Toxicological Research, United States Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, Arkansas, USA;Human Genome Sequencing Center, and Department of Molecular and Human Genetics, Baylor College of Medicine, 77030, Houston, Texas, USA;MidSouth Bioinformatics Center, Department of Information Science, George W. Donaghey College of Engineering and Information Technology, University of Arkansas at Little Rock, 2801 S. University Avenue, 72204, Little Rock, Arkansas, USA;Joint Bioinformatics Graduate Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, 72204, Little Rock, Arkansas, USA;Rush University Cancer Center, and Departments of Internal Medicine and Biochemistry, Rush University Medical Center, 60612, Chicago, Illinois, USA;

关键词: Kidney Renal Clear Cell Carcinoma;    TCGA;    RNA-Seq;    Differentially Expressed Genes;    Pathways;    Gene Network Analysis;    Machine Learning Classifier;   

DOI  :  10.1186/1471-2105-15-S17-S2

来源: Springer

【 摘 要 】

BackgroundKidney 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.ResultsFollowed 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 < 0.01, |log(FC)| > 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 < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets.ConclusionsOur 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.

【 授权许可】

CC BY   
© Yang et al.; licensee BioMed Central Ltd. 2014