Genome-wide association studies (GWAS) have successfully discovered novel loci associated with renal cell carcinoma (RCC) risk. However, a portion of genetic variation contributing to RCC susceptibility remains uncovered by traditional individual SNP analysis in GWAS. Several pathway-based analytical tools have been developed to complement individual SNP analysis. In this study, we performed gene set enrichment analysis using our RCC GWAS dataset comprised of 894 cases and 1,516 controls with three different tools: GenGen, SNP ratio test, and ALIGATOR. Seven pathways defined by KEGG, GO, and REACTOME were significantly associated with RCC susceptibility in all the three algorithms, with JAK-STAT-signaling (155 genes, PGenGen=0.033, PSNP ratio=0.002, and PALIGATOR=0.002) and Glycine-Serine-Threonine-metabolism (31 genes, PGenGen=0.04, PSNP ratio=0.043, and PALIGATOR<0.001) being the most significant. Gene-based analysis identified 13 genes in the JAK-STAT signaling pathway as being significantly associated with RCC risk, with OSM (oncostatin M) reaching the highest significance (p=0.003). Similar analysis in the Glycine -Serine-Threonine-metabolism pathway identified GLYCTK (glycerate kinase) as the most significant (p=0.003) among seven significant genes identified. We further validated the top significant variants from these two pathways in a replication GWAS dataset of 1,311 cases and 3,423 controls from NCI. One SNP (rs757903) located in PIK3CG (catalytic subunit of PI3K) in the JAK-STAT-signaling pathway was successfully validated with another three reaching borderline significance. Meta-analyses confirmed that these four SNPs were significantly associated with RCC risk, and increased the risk up to 20%. Individuals who carry three to four risk genotypes were at a 2-fold increase in RCC risk, compared to reference group with at most one risk genotype (OR: 1.73, 95% CI: 1.38–2.16, p= 1.47×10−6). Our results suggest that JAK-STAT-signaling may play an important role in RCC tumorigenesis and identify several new avenues for future investigation into the genetic factors mediating susceptibility to RCC.
Citation Format: Xiang Shu, Meng Chen, Yuanqing Ye, Maosheng Huang, Jian Gu, Christopher G. Wood, Xifeng Wu. Gene set enrichment analysis of renal cell carcinoma genome-wide association data identifies the JAK-STAT pathway mediating susceptibility. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A23.