ArticleFrontiers in immunology2026
Machine learning-driven identification and experimental validation of key biomarkers in the bile acid metabolic pathway associated with ulcerative colitis.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Bile acids are shown to participate in inflammatory responses. This study was designed to investigate the functions of bile acid metabolism-associated genes (BAMGs) in ulcerative colitis (UC), identify the potential biomarkers based on eleven machine learning algorithms. Methods: Seven independent UC transcriptomic datasets were retrieved from the GEO database. Differentially expressed genes, weighted gene co-expression network analysis (WGCNA), and multiple machine learning algorithms were integrated to identify key BAMGs. Subsequently, enrichment analysis, immune cell analysis and single cell analysis were performed to explore the biological functions and immunological characteristics. The dextran sulfate sodium (DSS) induced colitis model in mice was then established and validated the results through western blot and immunohistochemical (IHC) analysis. In addition, peripheral blood samples were collected from UC patients for the detection of feature gene expression by quantitative real-time PCR (RT-qPCR). Results: Through integrative analysis, three feature BAMGs ( Conclusion: This study identified a novel of BAMGs and preliminary revealed their interaction with immune cells in the development of UC. Downregulation of
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