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

Peng Liu ¹ Chunyan Sun ¹ Xiaojuan Wang ² Bing Han ² Yuhao Sun ¹ Yanbing Liu ^1* Xin Zeng ^1*

• ¹ Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China, Shanghai, 200120, China
• ² Department of Pharmacy, Minhang Hospital, Fudan University, Shanghai 201199, China, Shanghai, 201199, China

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 (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. Moreover, CEACAM6, CFB, CX3CL1, and HLA-DMA exhibited positive associations with infiltrating immune cells in UC, whereas PDK4 displayed a negative correlation with all immune cells. Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. Our analysis identified five key anoikis-DEGs as characteristic biomarkers of UC. These genes were strongly associated with the infiltration of both innate and adaptive immune cells, as well as immune pathways. This study highlights the role of anoikis genes in UC pathophysiology and offers valuable insights for further elucidating UC pathogenesis and individualized therapy.