Identification of Potential Biomarkers associated with Prognosis and Pathogenesis of Pancreatic Cancer

Abstract

Abstract Pancreatic cancer(PC), which is difficult to detect in its early stages and has a relatively rapid progression and poor prognosis, urgently requires the exploration of new biomarkers that used to develop new methods for early detection and monitoring of pancreatic cancer. Here, we downloaded the GSE16515 dataset from the GEO database, screened for differentially expressed genes in pancreatic cancer using GEO2R, analyzed the differential genes for GO and KEGG enrichment using Sento Academic, constructed a protein-protein interaction (PPI) network using STRING database and Cytoscape, and determined the protein-protein interactions (PPIs) by plug-in CytoHubba determined the hub genes of DEGs and used GEPIA to validate the expression and survival analysis of the hub genes, analyzed the transcription factors and kinases of the differential genes in the ChEA and X2K databases, and finally analyzed the target miRNAs of the differential genes in the Enrichr database.The methods presented in this paper can help to screen and correlate with pancreatic cancer prognosis and pathogenesis for key regulators and provide potential biomarkers for the diagnosis and prognosis of pancreatic cancer.