Identification of key regulators of pancreatic ductal adenocarcinoma using bioinformatics analysis of microarray data. Issue 2 (January 2019)

• Record Type: Journal Article
• Authors: Li, Nan; Zhao, Xin; You, Shengyi
• Other Names: Schaller. Bernhard section editor.
• Is Part Of: Medicine. Volume 98:Issue 2(2019)
• Publication Date: 2019-01
• Subjects: bioinformatics analysis -- differentially expressed gene -- microarray -- pancreatic ductal adenocarcinoma; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000014074
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal forms of cancer, and its etiology remains largely unknown. This study aimed to screen a panel of key genes and to identify their potential impact on the molecular pathways associated with the development of PDAC. Four gene expression profiles, GSE28735, GSE15471, GSE102238, and GSE43795, were downloaded from the Gene Expression Omnibus (GEO) database. The intersection of the differentially expressed genes (DEGs) in each dataset was obtained using Venn analysis. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) analysis were subsequently carried out. To screen for hub genes, a protein–protein interaction (PPI) network was constructed. The intersection of the DEGs revealed 7 upregulated and 9 downregulated genes. Upon relaxation of the selection criteria, 58 upregulated and 32 downregulated DEGs were identified. The top 5 biological processes identified by GO analysis involved peptide cross-linking, extracellular matrix (ECM) disassembly, regulation of the fibroblast growth factor receptor signaling pathway, mesoderm morphogenesis, and lipid digestion. The results of KEGG analysis revealed that the DEGs were significantly enriched in pathways involved in protein digestion and absorption, ECM-receptor interaction, pancreatic secretion, and fat digestion and absorption. The top ten hub genes were identified based on the PPI network. In conclusion, the identified hub genes may contribute to the elucidation of the underlying molecular mechanisms of PDAC and serve as promising candidates that can be utilized for the early diagnosis and prognostic prediction of PDAC. However, further experimental validation is required to confirm these results.