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

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.

Highlights

Kidney cancer is the sixth most common form of cancer for men and the tenth most common form of cancer for women
By integrating gene expression and protein-protein interactions here, we developed a new method of identifying gene clusters in the pathways impacted by the disease
We examined the hypothesis using the The Cancer Genome Atlas (TCGA)-clear cell renal cell carcinomas (ccRCC) dataset and three independent ccRCC patient datasets obtained from Gene Expression Omnibus database (GEO) (Methods, Table 1) [4,9]

Summary

Introduction

Kidney cancer is the sixth most common form of cancer for men and the tenth most common form of cancer for women. The vast majority of kidney cancers are renal cell carcinomas (RCC), among which nearly 75% are clear cell renal cell carcinomas (ccRCC) [2]. Metastatic RCC remains largely an incurable disease [3,4]. Patients with this disease often have no apparent symptoms or laboratory abnormalities in the early stages. The incidence of ccRCC has been rising steadily in recent years due to Abbreviations: RCC, Renal cell cancer; ccRCC, Clear cell renal cell carcinoma; eQTL, Expression quantitative trait loci; SVM, Support vector machine; TCGA, The Cancer Genome Atlas; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEG, Differentially expressed gene; DGM, Differential gene module; AUC, Area Under Curve; ROC, Receiver Operating Characteristic

Methods

Results

Conclusion