Bioinformatics method to analyze the mechanism of pancreatic cancer disorder.

Pancreatic cancer is an aggressive malignancy with a five-year mortality of 97-98% due to widespread metastatic disease. A better understanding of the molecular mechanism of pancreatic cancer is beneficial for the development of novel approaches for early detection and monitoring of pancreatic cancer. We aim to comprehensively identify the gene expression profile in pancreatic cancer and explore the molecular pathway of pancreatic cancer disorder. Using GSE15471 datasets downloaded from Gene Expression Omnibus data, we first screened the differentially expressed genes in pancreatic cancer using packages in R language. The key pathways of differentially expressed genes were investigated with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and synergetic network construction based on weighted Jaccard index. A total of 13,211 differentially expressed genes were identified, and they were enriched in several pathways, such as mitogen-activated protein kinase (MAPK) signaling pathway, transforming growth factor (TGF)-beta signaling pathway, Janus kinase-signal transducers and activators of transcription (JAK-STAT) signaling pathway, and calcium signaling pathway, as well as cell cycle, focal adhesion, complement and coagulation cascades, and leukocyte transendothelial migration. Synergetic pathway network analysis revealed that cytokine-cytokine receptor interaction pathway, calcium signaling pathway, and focal adhesion pathway were three important pathways in the development of pancreatic cancer. The method introduced here is helpful to screen the key pathways for controlling pancreatic cancer progression and provide potential therapeutic targets in the treatment of pancreatic cancer.