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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-? (TGF-?) 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-? 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.

SUBMITTER: Long J

PROVIDER: S-EPMC4906788 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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

Long Jin J Liu Zhe Z Wu Xingda X Xu Yuanhong Y Ge Chunlin C

Oncology letters 20160526 1

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 [ ...[more]

PMID: 27347177

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Project description:Osteoarthritis (OA) is the most common degenerative joint disorder worldwide. To identify more genetic signals, genome-wide association study (GWAS) has been widely used and elucidated some OA susceptibility genes. However, these susceptibility genes could only explain only a small part of heritability of OA. It is suggested that the identification of disease-related pathways may contribute to understand the genomic etiology of OA. Here, we integrated the GWAS into pathway analysis to identify novel OA risk pathways. In this study, we first selected 187 independent genetic variants identified by GWAS (P < 1.00E-05) and found that most of these genetic variants are noncoding mutations. We then conducted an expression quantitative trait loci analysis and found that 165 of these 187 genetic variants could significantly regulate the expression of nearby genes. Third, we identified OA susceptibility genes corresponding to these genetic variants, conducted a pathway analysis, and identified novel OA-related KEGG pathways, GO biological processes, GO molecular functions, and GO cellular components. In KEGG database, transforming growth factor β signaling pathway is the most significant signal (P = 5.98E-05) and is the only pathway after the BH multiple-test adjustment with false discovery rate (FDR) = 0.02. In GO database, we identified 24 statistically significant GO biological processes, one statistically significant GO molecular function, and five statistically significant GO cellular components (FDR < 0.05). These signals are related with chondrocyte differentiation and development, which are all known biological pathways associated with OA. Finally, we conducted an OA case-control gene expression analysis to evaluate the differential expression of these OA risk genes. Using an OA case-control gene expression analysis, we showed that 44 risk genes were suggestively differentially expressed in OA cases compared with controls (P < 0.05). Three genes, WWP2, COG5, and MAPT, were statistically differentially expressed in OA cases compared with controls (P < 0.05/122 = 4.10E-04). Hence, our findings may contribute to understanding the genomic etiology of OA.

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Project description:BackgroundMultiple myeloma (MM) is a severely debilitating and fatal B-cell neoplastic disease. The discovery of disease-associated proteins with causal genetic evidence offers a chance to uncover novel therapeutic targets.MethodsFirst, we comprehensively investigated the causal association between 2994 proteins and MM through two-sample mendelian randomization (MR) analysis using summary-level data from public genome-wide association studies of plasma proteome (N = 3301 healthy individuals) and MM (598 cases and 180,756 controls). Sensitivity analyses were performed for these identified causal proteins. Furthermore, we pursued the exploration of enriched biological pathways, prioritized the therapeutic proteins, and evaluated their druggability using the KEGG pathway analysis, MR-Bayesian model averaging analysis, and cross-reference with current databases, respectively.ResultsWe identified 13 proteins causally associated with MM risk (false discovery rate corrected P < 0.05). Six proteins were positively associated with the risk of MM, including nicotinamide phosphoribosyl transferase (NAMPT; OR [95% CI]: 1.35 [1.18, 1.55]), tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1; 1.14 [1.06, 1.22]), neutrophil cytosol factor 2 (NCF2; 1.27 [1.12, 1.44]), carbonyl reductase 1, cAMP-specific 3',5'-cyclic phosphodiesterase 4D (PDE4D), platelet-activating factor acetylhydrolase IB subunit beta (PAFAH1B2). Seven proteins were inversely associated with MM, which referred to suppressor of cytokine signaling 3 (SOCS3; 0.90 [0.86, 0.94]), Fc-gamma receptor III-B (FCGR3B; 0.75 [0.65,0.86]), glypican-1 (GPC1; 0.69 [0.58,0.83]), follistatin-related protein 1, protein tyrosine phosphatase non-receptor type 4 (PTPN4), granzyme B, complement C1q subcomponent subunit C (C1QC). Three of the causal proteins, SOCS3, FCGR3B, and NCF2, were enriched in the osteoclast differentiation pathway in KEGG enrichment analyses while GPC1 (marginal inclusion probability (MIP):0.993; model averaged causal effects (MACE): - 0.349), NAMPT (MIP:0.433; MACE: - 0.113), and NCF2 (MIP:0.324; MACE:0.066) ranked among the top three MM-associated proteins according to MR-BMA analyses. Furthermore, therapeutics targeting four proteins are currently under evaluation, five are druggable and four are future breakthrough points.ConclusionsOur analysis revealed a set of 13 novel proteins, including six risk and seven protective proteins, causally linked to MM risk. The discovery of these MM-associated proteins opens up the possibility for identifying novel therapeutic targets, further advancing the integration of genome and proteome data for drug development.

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

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

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