Bioinformatics Analysis of Key Candidate Genes and Pathways in Ulcerative Colitis

Abstract

Ulcerative colitis (UC) is chronic, idiopathic disease that affects the colon and the rectum and the underlying pathogenesis of UC remains to be known. The clinical drugs are mainly work based on anti-inflammation and immune system. However, most of them are expensive and have severe side effects. Therefore, identification of novel targets and exploring new drugs are urgently needed. In this study, several bioinformatics approaches were used to discover key genes and further in order to explore the pathogenesis of UC. Two microarray datasets, GSE38713 and GSE9452 were selected from NCBI-Gene Expression Omnibus database. Differentially expression genes (DEGs) were identified by using LIMMA Package of R. Then, we filtered clustered candidate genes into Gene Ontology (GO) and pathway enrichment analysis with the Database for Annotation, Visualization and Integrated Discovery (DAVID), KEGG pathway based on functions and signaling pathways with significant enrichment analysis. The protein-protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes/ Proteins (STRING) analysis, and visualized by Cytoscape and further analyzed by Molecular Complex Detection. Lastly, 353 up-regulated and 145 down-regulated genes were than recognized. After consulting a number of references and network degree analysis, four hub genes, namely FCGR2A, C3, INPP5A, and ACAA1 were identified, and these genes were mainly enriched in complement and coagulation cascades, mineral absorption, and Peroxisome Proliferator-Activated Receptor (PPAR) signaling pathways. In conclusion, this study would provide new clues for the pathogenesis and identification of drug targets of UC in the near future.

Keywords: differentially expression gene; gene chip; network analysis; ulcerative colitis.