Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with an idiopathic origin, characterized by persistent mucosal inflammation. Anoikis is a programmed cell death mechanism activated during carcinogenesis to eliminate undetected isolated cells from the extracellular matrix. Although existing evidence indicates that anoikis contributes to the modulation of immune response, the involvement of anoikis-related genes (ARGs) in UC pathogenesis and their interaction with infiltrating immune cells has not been thoroughly explored. The GSE75214, GSE92415, and GSE16879 datasets were acquired and integrated from the GEO database. Additionally, 58 ARGs were identified through the GSEA database. Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). Receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic accuracy of each gene. Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. Besides, unsupervised cluster analysis was conducted to categorize the UC samples into distinct subgroups, followed by comparing subtype differences. Finally, the upstream regulatory network was constructed and visualized. A comprehensive analysis of the involvement of ARGs in UC was performed, revealing their expression profile, correlation with infiltrating immune cells, and enrichment analyses. We identified five key anoikis-DEGs (