A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

Mary Qu Yang; Dan Li; William Yang; Yifan Zhang; Jun Liu; Weida Tong

doi:10.1016/j.csbj.2017.09.003

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

Comput Struct Biotechnol J. 2017 Oct 10:15:463-470. doi: 10.1016/j.csbj.2017.09.003. eCollection 2017.

Authors

Mary Qu Yang^{1

2}, Dan Li^{1

2}, William Yang³, Yifan Zhang^{1

2}, Jun Liu⁴, Weida Tong⁵

Affiliations

¹ Joint Bioinformatics Graduate Program, Department of Information Science, George W. Donaghey College of Engineering and Information Technology, University of Arkansas at Little Rock, USA.
² University of Arkansas for Medical Sciences, 2801 S. University Ave, Little Rock, AR 72204, USA.
³ School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.
⁴ Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
⁵ Divisions of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.

Keywords: AUC, Area Under Curve; Causative mutation; DEG, Differentially expressed gene; DGM, Differential gene module; Gene module; KEGG, Kyoto Encyclopedia of Genes and Genomes; Pathways; Protein-protein interaction; RCC, Renal cell cancer; ROC, Receiver Operating Characteristic; SVM, Support vector machine; TCGA, The Cancer Genome Atlas; ccRCC; ccRCC, Clear cell renal cell carcinoma; eQTL; eQTL, Expression quantitative trait loci.

Abstract

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