ArticleJournal of translational medicine2025
Screening and experimental study of potential biomarkers for ulcerative colitis based on weighted gene co-expression network analysis and machine learning.
Article in Journal of translational medicine, 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.
- From Food Contaminant to Therapeutic Target: Identification of KCNE2 and 5-Azacytidine for Gastric Cancer via Multi-Omics, Machine Learning, and In Vitro Validation.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Epithelial cell-based multi-omics integration identifies SPINK5 and SRI as novel diagnostic biomarkers for ulcerative colitis.Frontiers in pharmacology · 2026Article
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4 authors.
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Abstract
backgroundUlcerative colitis (UC) is a chronic nonspecific inflammatory intestinal disease affecting the mucosa and submucosa, characterized by continuous and diffuse active inflammation. However, its underlying pathogenesis remains unclear.
objectiveThis study aimed to identify potential UC biomarkers by integrating weighted gene co-expression network analysis (WGCNA) with machine learning, followed by validation in an experimental UC mouse model.
methodsThe Gene Expression Omnibus database was systematically queried, and the GSE87466 dataset, comprising of colonic tissues from 87 patients with UC and 21 healthy controls, was retrieved. Differentially expressed genes (DEGs) were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. WGCNA was used to extract UC-related DEGs. Two machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen potential biomarkers. These biomarkers were then validated using animal experiments.
resultsA total of 1,097 DEGs were identified. WGCNA constructed nine co-expression gene modules, with the turquoise module (520 genes) exhibiting the highest relevance to UC. LASSO and SVM-RFE analysis identified poly(ADP-ribose) polymerase family member 8 (PARP8) as a potential biomarker of UC. Immunological analysis revealed significantly higher proportions of naive B cells, activated CD4
conclusionPARP8 may contribute to UC pathogenesis via immune-related pathways and holds promise as a diagnostic and predictive biomarker.
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