Pathway analysis of bladder cancer genome-wide association study identifies novel pathways involved in bladder cancer development
Meng Chen1,*, Nathaniel Rothman2,*, Yuanqing Ye1, , Jian Gu1, Paul A. Scheet1, Maosheng Huang1, David W. Chang1, Colin P. Dinney3, Debra T. Silverman2, Jonine D. Figueroa2, Stephen J. Chanock2,** and Xifeng Wu1,**
1 Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
2 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
3 Department of Urology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
* co-first author
** co-last author
Correspondence:
Xi-Feng Wu, email:
Keywords: pathway analysis, gene set enrichment analysis, GWAS, bladder cancer, susceptibility loci
Received: May 20, 2016 Accepted: July 28, 2016 Published: August 07, 2016
Abstract
Genome-wide association studies (GWAS) are designed to identify individual regions associated with cancer risk, but only explain a small fraction of the inherited variability. Alternative approach analyzing genetic variants within biological pathways has been proposed to discover networks of susceptibility genes with additional effects. The gene set enrichment analysis (GSEA) may complement and expand traditional GWAS analysis to identify novel genes and pathways associated with bladder cancer risk. We selected three GSEA methods: Gen-Gen, Aligator, and the SNP Ratio Test to evaluate cellular signaling pathways involved in bladder cancer susceptibility in a Texas GWAS population. The candidate genetic polymorphisms from the significant pathway selected by GSEA were validated in an independent NCI GWAS. We identified 18 novel pathways (P < 0.05) significantly associated with bladder cancer risk. Five of the most promising pathways (P ≤ 0.001 in any of the three GSEA methods) among the 18 pathways included two cell cycle pathways and neural cell adhesion molecule (NCAM), platelet-derived growth factor (PDGF), and unfolded protein response pathways. We validated the candidate polymorphisms in the NCI GWAS and found variants of RAPGEF1, SKP1, HERPUD1, CACNB2, CACNA1C, CACNA1S, COL4A2, SRC, and CACNA1C were associated with bladder cancer risk. Two CCNE1 variants, rs8102137 and rs997669, from cell cycle pathways showed the strongest associations; the CCNE1 signal at 19q12 has already been reported in previous GWAS. These findings offer additional etiologic insights highlighting the specific genes and pathways associated with bladder cancer development. GSEA may be a complementary tool to GWAS to identify additional loci of cancer susceptibility.
