ArticleBMC pharmacology & toxicology2025
Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking.
Article in BMC pharmacology & toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Clinical and modifier gene profiles in reversible infantile respiratory chain deficiency: three Chinese cases report and literature review.Translational pediatrics · 2026Article
- Lactate-related biomarkers in severe acute pancreatitis: insights from machine learning, molecular dynamics, and experimental validation.Journal of biological engineering · 2025Article
- Lactylation at the crossroads of immune metabolism and epigenetic regulation: revealing its role in rheumatic immune diseases.Journal of translational medicine · 2025Review
- Integrated single-cell and bulk RNA-sequencing data reveal prognosis and therapeutic response in low-grade glioma based on hypoxia-lactylation related genes.Discover oncology · 2025Article
- Lactylation: the metabolic-immune hub in autoimmune diseases.Frontiers in immunology · 2025Review
- Exploring the Potential Value of Lactylation and Macrophage Polarization-Related Genes as Biomarkers for TNF-α Inhibitor Response in Inflammatory Bowel Disease.Journal of inflammation research · 2025Article
- The lactylation-macrophage interplay: implications for gastrointestinal disease therapeutics.Frontiers in immunology · 2025Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUlcerative colitis (UC), a chronic relapsing-remitting inflammatory bowel disease. Recent studies have shown that lactylation modifications may be involved in metabolic-immune interactions in intestinal inflammation through epigenetic regulation, but their specific mechanisms in UC still require in-depth validation.
methodsWe conducted comparative analyses of transcriptomic profiles, immune landscapes, and functional pathways between UC and normal cohorts. Lactylation-related differentially expressed genes were subjected to enrichment analysis to delineate their mechanistic roles in UC. Through machine learning algorithms, the diagnostic model was established. Further elucidating the mechanisms and regulatory network of the model gene in UC were GSVA, immunological correlation analysis, transcription factor prediction, immunofluorescence, and single-cell analysis. Lastly, the CMap database and molecular docking technology were used to investigate possible treatment drugs for UC.
resultsTwenty-two lactylation-related differentially expressed genes were identified, predominantly enriched in actin cytoskeleton organization and JAK-STAT signaling. By utilizing machine learning methods, 3 model genes (S100A11, IFI16, and HSDL2) were identified. ROC curves from the train and test cohorts illustrate the superior diagnostic value of our model. Further comprehensive bioinformatics analyses revealed that these three core genes may be involved in the development of UC by regulating the metabolic and immune microenvironment. Finally, regorafenib and R-428 were considered as possible agents for the treatment of UC.
conclusionThis study offers a novel strategy to early UC diagnosis and treatment by thoroughly characterizing lactylation modifications in UC.
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