Identification of disease-associated pathways in pancreatic cancer by integrating genome-wide association study and gene expression data

Abstract

In order to additionally understand the pathogenesis of pancreatic cancer (PC), the present study conducted pathway analysis based on genome-wide association study (GWAS) and gene expression data to predict genes that are associated with PC. GWAS data (accession no., pha002874.1) were downloaded from National Center for Biotechnology Information (NCBI) database of Genotypes and Phenotypes, which included data concerning 1,896 patients with PC and 1,939 control individuals. Gene expression data [accession no., GSE23952; human pancreatic carcinoma Panc-1 transforming growth factor-β (TGF-β) treatment assay] were downloaded from NCBI Gene Expression Omnibus. Gene set enrichment analysis was used to identify significant pathways in the GWAS or gene expression profiles. Meta-analysis was performed based on pathway analysis of the two data sources. In total, 58 and 280 pathways were identified to be significant in the GWAS and gene expression data, respectively, with 7 pathways significant in both the data profiles. Hsa 04350 TGF-β signaling pathway had the smallest meta P-value. Other significant pathways in the two data sources were negative regulation of DNA-dependent transcription, the nucleolus, negative regulation of RNA metabolic process, the cellular defense response, exocytosis and galactosyltransferase activity. By constructing the gene-pathway network, 5 pathways were closely associated, apart from exocytosis and galactosyltransferase activity pathways. Among the 7 pathways, 11 key genes (2.9% out of a total of 380 genes) from the GWAS data and 43 genes (10.5% out of a total of 409 genes) from the gene expression data were differentially expressed. Only Abelson murine leukemia viral oncogene homolog 1 from the nucleolus pathway was significantly expressed in by both data sources. Overall, the results of the present analysis provide possible factors for the occurrence of PC, and the identification of the pathways and genes associated with PC provides valuable data for investigating the pathogenesis of PC in future studies.

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@inproceedings{Long2016IdentificationOD, title={Identification of disease-associated pathways in pancreatic cancer by integrating genome-wide association study and gene expression data}, author={Jin Long and Zhe Liu and Xingda Wu and Yuanhong Xu and Chunlin Ge}, year={2016} }