Identification of Hub Biomarkers and Immune Cell Infiltration Characteristics in Ulcerative Colitis by Bioinformatics Analysis and Machine Learning

Ulcerative colitis (UC) is a chronic inflammatory disease characterized by abdominal pain, diarrhea, mucous pus, and blood stool. However, effective and reliable diagnostic biomarkers are not available. This study aimed to identify the key biomarkers and potential therapeutic targets of UC and analyze the role of immune cell infiltration in the pathogenesis of the disease. Methods: Acquired the microarray dataset GSE94648 for UC from the Gene Expression Omnibus database. Using the limma package in R software, differentially expressed genes (DEGs) were examined. Obtain potential key genes by intersecting weighted gene co-expression networks with differentially expressed genes (DEGs), and perform enrichment analysis on key genes. The hub genes were confirmed using two machine learning algorithms, LASSO and RandomFores, and their diagnostic value was estimated. Furthermore, the level of 22 immune cell infiltration in UC patients was evaluated using the CIBERSORT method. Results: A total of 201 genes with differential expression were discovered. The combination of differentially expressed genes and WGCNA analysis resulted in the identification of 95 intersecting genes altogether. Subsequently, CARD16, GJB6, HIST1H4H, KCNJ15, and SLC22A4 were ultimately identified as potential biomarkers for UC by machine learning algorithms and ROC curves. Immune infiltration analysis revealed that T cells follicular helper, T cells regulatory, Macrophages M0 may be related to the advancement of UC. Conclusion: CARD16, GJB6, HIST1H4H, KCNJ15, and SLC22A4 genes could be potential biomarkers and therapeutic targets for UC. Immune cell infiltration may be crucial to the development and progression of UC.