Convergent evidence from systematic analysis of GWAS revealed genetic basis of esophageal cancer

// Xue-xin Gao 1, * , Lei Gao 2, * , Jiu-qiang Wang 3 , Su-su Qu 4 , Yue Qu 5 , Hong-lei Sun 6 , Si-dang Liu 7 , Ying-li Shang 7 1 Department of Thoracic Surgery, Central Hospital of Tai’an, Tai’an, Shandong, China 2 Department of Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, Shandong, China 3 Department of State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China 4 Department of Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China 5 Department of Pathology, University of Texas, Medical Branch, Galveston, Texas, USA 6 Department of Key Laboratory of Animal Epidemiology and Zoonosis, Ministry of Agriculture, College of Veterinary Medicine and State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China 7 Department of Preventive Veterinary Medicine, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an, Shandong, China * These authors have contributed equally to this work Correspondence to: Lei Gao, email: gaolei@sdut.edu.cn Ying-li Shang, email: yinglish@126.com Keywords: GWAS, genetic basis, pathway, network, esophageal cancer Received: April 07, 2016     Accepted: May 29, 2016     Published: June 17, 2016 ABSTRACT Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce. In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis. Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC. Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC.