Identification of Anoikis-Related Genes in Driving Immune-Inflammatory Responses in Ulcerative Colitis Based on Bioinformatics Analysis and Machine Learning

Bibliographic Details
Title: Identification of Anoikis-Related Genes in Driving Immune-Inflammatory Responses in Ulcerative Colitis Based on Bioinformatics Analysis and Machine Learning
Authors: Diao W, Huang X, Huang W, Jiang J, Li W, Liu H, Yan B, Shen L
Source: Journal of Inflammation Research, Vol Volume 18, Pp 3227-3242 (2025)
Publisher Information: Dove Medical Press, 2025.
Publication Year: 2025
Collection: LCC:Pathology
LCC:Therapeutics. Pharmacology
Subject Terms: ulcerative colitis, anoikis, biomarkers, machine learning, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
More Details: Wenxiu Diao,1,2,* Xu Huang,1,* Wensha Huang,1,* Jing Jiang,2 Wentao Li,2 He Liu,2 Bo Yan,1 Lei Shen1 1Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Department of Cardiology, No. 971 hospital of The People’s Liberation Army Navy, Qingdao, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lei Shen; Bo Yan, Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China, Email leishenwuhan@126.com; byanwuhan@126.comBackground: Ulcerative colitis (UC) is a challenging chronic intestinal inflammation. Anoikis, a type of programmed cell death triggered by detachment from the extracellular matrix, is crucial in various physiological and pathological contexts. This study aims to explore the biological and clinical implications of anoikis-related genes (ARGs) in UC.Methods: Gene expression microarrays from normal and UC mucosal tissues focused on ARGs. Differentially expressed genes (DEGs) related to anoikis in UC were identified. Weighted gene co-expression network analysis (WGCNA) screened UC-related module genes. GO, KEGG, GSEA, and GSVA analyses were used to uncover mechanisms. Machine learning identified hub ARG-DEGs highly correlated with UC, and diagnostic nomograms assessed their diagnostic potential. The CIBERSORT algorithm analyzed changes in the UC immune microenvironment related to hub UC-ARGs. Potential drugs, miRNAs, and transcription factors (TFs) interacting with these hub UC-ARGs were investigated, and animal experiments verified their expression.Results: 49 ARG-DEGs were identified, mainly linked to the PI3K-AKT signaling pathway, inflammatory signal regulation, and extracellular matrix (ECM)-receptor interactions. Notably, CDH3 and SERPINA1 showed significant diagnostic potential for UC, confirmed by the Wilcoxon rank-sum test, independent validation sets, Western blot, and immunohistochemical staining. Significant variations in immune cell infiltration and activation within UC samples correlated with hub UC-ARGs were observed using the CIBERSORT algorithm.Conclusion: Anoikis may drive UC progression by initiating an immune inflammatory response. CDH3 and SERPINA1 are promising biomarkers and therapeutic targets for UC.Keywords: ulcerative colitis, anoikis, biomarkers, machine learning
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1178-7031
Relation: https://www.dovepress.com/identification-of-anoikis-related-genes-in-driving-immune-inflammatory-peer-reviewed-fulltext-article-JIR; https://doaj.org/toc/1178-7031
Access URL: https://doaj.org/article/0d01b9c1808041d89d86ea78961e95ff
Accession Number: edsdoj.0d01b9c1808041d89d86ea78961e95ff
Database: Directory of Open Access Journals
More Details
ISSN:11787031
Published in:Journal of Inflammation Research
Language:English