ArticleFrontiers in molecular biosciences2025
Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning.
Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.Current opinion in allergy and clinical immunology · 2026Review
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3 authors.
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Abstract
Backgrounds: Asthma and ulcerative colitis (UC) are chronic inflammatory diseases linked through the "gut-lung axis," but their shared mechanisms remain unclear. This study aims to identify common biomarkers and pathways between asthma and UC using bioinformatics. Methods: Gene expression data for asthma and UC were retrieved from the GEO database, and differentially expressed genes (DEGs) were analyzed. Weighted Gene Coexpression Network Analysis (WGCNA) identified UC-associated gene modules. Shared genes between asthma and UC were derived by intersecting DEGs with UC-associated modules, followed by functional enrichment and protein-protein interaction (PPI) analysis. Machine learning identified hub genes, validated through external datasets using ROC curves, nomograms, and boxplots. Gene Set Enrichment Analysis (GSEA) explored pathway alterations, while immune infiltration patterns were analyzed using the CIBERSORT algorithm. Molecular docking (MD) was performed to predict therapeutic compounds, followed by molecular dynamics simulations on the top-ranked docked complex to assess its binding stability. Results: A total of 41 shared genes were identified, linked to inflammatory and immune pathways, including TNF, IL-17, and chemokine signaling. Four key hub genes-NOS2, TCN1, CHI3L1, and TIMP1-were validated as diagnostic biomarkers. Immune infiltration analysis showed strong correlations with multiple immune cells. Molecular docking identified several potential therapeutic compounds, with PD 98059, beclomethasone, and isoproterenol validated as promising candidates. The stability of the TIMP1-Beclomethasone complex was determined through molecular dynamics simulations. Conclusion: This study highlights NOS2, TCN1, CHI3L1, and TIMP1 as potential biomarkers and therapeutic targets for asthma and UC, providing insights into shared mechanisms and new strategies for diagnosis and treatment.
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