Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

Congcong  Wang; Jianping  Guo; Xiaoyang  Zhao; Jia  Jia; Wenting  Xu; Peng  Wan; Changgang  Sun

doi:10.18502/ijph.v50i11.7578

Congcong Wang Clinical Medical College, Cheeloo College of Medicine, Shandong University, Jinan 250100, Shandong, China
Jianping Guo Department of Oncology, Zibo Maternal and Children Hospital, Zibo 255000, Shandong, China
Xiaoyang Zhao Department of Oncology Surgery, 4th People’s Hospital of Zibo, Zibo 255000, Shandong, China
Jia Jia Department of Oncology Surgery, 4th People’s Hospital of Zibo, Zibo 255000, Shandong, China
Wenting Xu Department of Oncology Surgery, 4th People’s Hospital of Zibo, Zibo 255000, Shandong, China
Peng Wan Department of Gastroenterology, Zibo Central Hospital, Zibo 255000, Shandong, China
Changgang Sun Department of Oncology, Weifang Traditional Chinese Hospital, Weifang 261053, Shandong, China

DOI: https://doi.org/10.18502/ijph.v50i11.7578

Keywords: Bioinformatics analysis; Differently expressed genes; Hub genes; Pancreatic ductal adenocarcinoma

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 CytoHubba 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.