Abstract
BACKGROUND/OBJECTIVES: Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) with a relapsing nature and complex etiology. Bioinformatics analysis has been widely applied to investigate various diseases. This study aimed to identify crucial differentially expressed genes (DEGs) and explore potential therapeutic agents for UC. METHODS: The GSE47908 and GSE55306 colon tissue transcriptome gene datasets were downloaded from the Gene Expression Omnibus-NCBI (GEO) database. GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA). GO-BP analysis and KEGG enrichment analysis were performed on the intersecting differential genes via the Metascape website, while hub genes were analyzed by STRING11.0 and Cytoscape3.7.1. The expression of hub genes was verified in the dataset GSE38713 colon tissue specimens. Finally, the gene expression profiles of the validation set were analyzed by immuno-infiltration through the ImmuCellAI online tool, and the CMap database was used to screen for negatively correlated small molecule compounds. RESULTS: A total of 595 and 926 genes were screened by analysis of GSE47908 and GSE55306 datasets, respectively. Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC. GSEA analysis yielded intersecting pathways for both datasets (colorectal cancer pathway, base excision repair, cell cycle, apoptosis). GO-BP and KEGG enrichment analyses were performed to obtain key biological processes (inflammatory response, response to bacteria, leukocyte activation involved in the immune response, leukocyte-cell adhesion, apoptosis, positive regulation of immune effector processes) and key signaling pathways (cytokine-cytokine receptor interactions, IBD, NOD-like receptor signaling pathways). The immune cell infiltration analysis suggested that the incidence of UC was mainly related to the increase in CD4+T cells, depletion of T cells, T follicular helper cells, natural killer cells, γδ T cells and the decrease in CD8 naive T cells, helper T cells 17 and effector T cells. The CMap database results showed that small molecule compounds such as vorinostat, roxarsone, and wortmannin may be therapeutic candidates for UC. CONCLUSIONS: This study not only aids in early prediction and prevention but also provides novel insights into the pathogenesis and treatment of UC.