A Transcriptomics Analysis for Potential Biomarker in Pancreatic Cancer

Background: Pancreatic cancer, also known as Pancreatic ductal adenocarcinoma (PDAC), is the deadliest cancer. CA19-9 (carbohydrate antigen 19-9) is the only FDA-approved biomarker used in the diagnosis of PDAC. The majority of PDAC death is due to late screening and invasiveness. As a result, it is still essential to discover novel biomarkers for the early detection of PDAC. Method: Two datasets (GSE16515, GSE211398) were chosen for the bioinformatics analysis. The limma and limma voom packages were used to analyze the data for DEGs. The DAVID (Database for Annotation, Visualization, and Integrated Discovery) database was used to undertake pathway analysis of the DEGs utilizing the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases. The STRING database was used to identify the protein-protein interaction (PPI) network. The Gene Expression Profiling Interactive Analysis (GEPIA) method was applied to assess the differential expression of hub genes in PDAC patients. Result: 871 DEGs were found to be present in total and DEGs enriched in cell adhesion, extracellular matrix organization, cell migration, extracellular exosome, space, region, focal adhesion, integrin binding, pathways in cancer, and PI3K-Akt signaling pathway. Among the DEGs, 17 hub genes were identified. MKI67, AURKA, CDK1, ANLN, KIF20A, RRM2, BUB1B, CCNA2, CCNB1, SDC1, LGALS3, and ITGB1 were found to be associated with carcinogenesis and could be used as a diagnostic and therapeutic target in PDAC. Conclusion: The findings imply that DEGs and hub genes are important in the pathogenesis of the disease, and they are crucial in both the detection and treatment of PDAC.