Identification of new biomarkers for pancreatic cancer management: A bioinformatics analysis

Abstract Background Pancreatic adenocarcinoma is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify key genes and the involved mechanisms in pancreatic adenocarcinoma, we used bioinformatics analyzes in our study to introduce potential biomarkers in pancreatic cancer management. Methods In this study, gene expression profiles of pancreatic adenocarcinoma patients and normal adjacent tissues were screened and downloaded from The Cancer Genom Atlas (TCGA) bioinformatics database. Differentially expressed genes (DEGs) were identified between normal and pancreatic cancer gene expression signatures using R software. Then, Enrichment analysis of DEGs [including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis] was performed by an enrichr (interactive and collaborative HTML5 gene list enrichment analysis) web-based tool. The protein-protein interaction (PPI) network was also constructed using STRING (Search Tool for the Retrieval of Interacting Genes) and Cytoscape software to identify the hub genes according to the top 100 DEGs in pancreatic adenocarcinoma. Results In our study, more than 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1-4, H1-5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 with 99.91% probability might distinguish pancreatic adenocarcinoma from normal tissue. GO analysis of these 20 top DEGs showed that they have more enriched in negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic adenocarcinoma and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In protein-protein interaction (PPI) network analysis, we found that different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic adenocarcinoma. Conclusions Our bioinformatics analysis showed that the DEGs and hub genes as key genes identified in this study may serve as new biomarkers in the near future for better management of pancreatic cancer. Although, H1.3 is currently one of the prognostic biomarkers in pancreatic cancer.