Identification of key candidate genes for pancreatic cancer by bioinformatics analysis

Although pancreatic cancer has the highest mortality rate among all neoplasms worldwide, its exact mechanism remains poorly understood. In the present study, three Gene Expression Omnibus (GEO) datasets were integrated to elucidate the potential genes and pathways that contribute to the development of pancreatic cancer. Initially, a total of 226 differentially expressed genes (DEGs) were identified in the three GEO datasets, containing 179 upregulated and 47 downregulated DEGs. Furthermore, function and pathway enrichment analyses were performed to explore the function and pathway of these genes, and the results indicated that the DEGs participated in extracellular matrix (ECM) processes. In addition, a protein-protein interaction network was constructed and 163 genes of the 229 DEGs were filtered into the network, resulting in a network complex of 163 nodes and 438 edges. Finally, 24 hub genes were identified in the network, and the top 2 most significant modules were selected for function and pathway analysis. The hub genes were involved in several processes, including activation of matrix, degradation of ECM and ECM organization. Taken collectively, the data demonstrated potential key genes and pathways in pancreatic cancer, which may provide novel insights to the mechanism of pancreatic cancer. In addition, these hub genes and pathways may be considered as targets for the treatment of pancreatic cancer.