ArticleJournal of inflammation research2025
Identification of Key Biomarkers and Immune Microenvironment Features in Ulcerative Colitis: An Integrated Analysis Using WGCNA and Multiple Machine Learning Algorithms.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Identifying coagulation-related biomarkers in steroid-induced osteonecrosis of the femoral head: implications for early diagnosis and treatment.Clinical and experimental medicine · 2026Article
- Integrated single-cell and transcriptomic profiling identifies machine-learning-based pyroptosis biomarkers in IBD.Frontiers in immunology · 2026Article
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Authors and funding
11 authors.
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Abstract
Background: Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) characterized by a dysregulated mucosal immune response in the intestine. The disease poses significant challenges for both diagnosis and treatment. This study aims to identify reliable biomarkers for UC and investigate its immunological characteristics, with the goal of improving diagnostic accuracy and informing treatment strategies. Methods: This study integrated multiple GEO datasets and identified UC-associated hub genes through differential expression and Weighted Gene Co-expression Network Analysis (WGCNA), with subsequent refinement using least absolute shrinkage and selection operator (LASSO), randomForest (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms. These genes were used to construct a feedforward neural network (FNN) diagnostic model, whose performance was assessed using receiver operating characteristic (ROC) curve analysis. Immune profiling based on CIBERSORT, ssGSEA, Gene set variation analysis (GSVA), and ESTIMATE revealed associations between hub gene expression, immune cell infiltration, and inflammatory activity. Immunohistochemistry was performed to validate the protein expression of hub genes. Results: Ten hub genes (ACOX2, MMP3, CPT2, CTSK, CHP2, VCAM1, SLC25A34, BASP1, NCF2, and GLB1L2) were identified, and the FNN model showed strong diagnostic accuracy. Notably, NCF2 expression correlated with immune cell infiltration and immune/inflammation scores, and was confirmed to be elevated in UC tissues, suggesting a key role in disease pathogenesis. Conclusion: This study identified ten hub genes with potential as biomarkers for the diagnosis and treatment of UC. NCF2 may contribute to UC pathogenesis by modulating inflammatory and immune responses, serving as a potential immune-related biomarker for improved UC diagnosis and treatment.
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