Abstract
Background:To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis.Methods:The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation, Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by Cyto-Hubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA.Results:Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1.Conclusion:we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.
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