Identification of hub genes and analysis of prognostic values in pancreatic ductal adenocarcinoma by integrated bioinformatics methods.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the world, and more molecular mechanisms should be illuminated to meet the urgent need of developing novel detection and therapeutic strategies. We analyzed the related microarray data to find the possible hub genes and analyzed their prognostic values using bioinformatics methods. The mRNA microarray datasets GSE62452, GSE15471, GSE102238, GSE16515, and GSE62165 were finally chosen and analyzed using GEO2R. The overlapping genes were found by Venn Diagrams, functional and pathway enrichment analyses were performed using the DAVID database, and the protein-protein interaction (PPI) network was constructed by STRING and Cytoscape. OncoLnc, which was linked to TCGA survival data, was used to investigate the prognostic values. In total, 179 differentially expressed genes (DEGs) were found in PDAC, among which, 130 were up-regulated genes and 49 were down-regulated. DAVID showed that the up-regulated genes were significantly enriched in extracellular matrix and structure organization, collagen catabolic and metabolic process, while the down-regulated genes were mainly involved in proteolysis, reactive oxygen species metabolic process, homeostatic process and cellular response to starvation. From the PPI network, the 21 nodes with the highest degree were screened as hub genes. Based on Molecular Complex Detection (MCODE) plug-in, the top module was formed by ALB, TGM, PLAT, PLAU, EGF, MMP7, MMP1, LAMC2, LAMA3, LAMB3, COLA1, FAP, CDH11, COL3A1, ITGA2, and VCAN. OncoLnc survival analysis showed that, high expression of ITGA2, MMP7, ITGB4, ITGA3, VCAN and PLAU may predict poor survival results in PDAC. The present study identified hub genes and pathways in PDAC, which may be potential targets for its diagnosis, treatment, and prognostic prediction.