ArticleInternational journal of molecular sciences2026
Multi-Omics and Machine Learning-Based Characterization of the Lactylation Microenvironment and Biomarker Identification in Crohn's Disease Intestinal Fibrosis.
Article in International journal of molecular sciences, 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
Crohn's disease (CD) is characterized by transmural inflammation and intestinal fibrosis, in which metabolic reprogramming may contribute to fibrotic remodeling through lactate-associated epigenetic and transcriptional regulation, but key cellular states and candidate biomarkers remain unclear. Therefore, we integrated single-cell RNA sequencing (scRNA-seq) with a lactylation-associated transcriptional score to estimate lactate/lactylation-related transcriptional activity in CD intestinal tissues. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and an integrated machine learning framework identified core lactylation-associated biomarkers, which were validated in clinical tissue and a TNBS-induced mouse model. Additionally, we evaluated the ability of these core genes to predict anti-TNF-α treatment response in an independent clinical cohort, and analyzed cellular communication and trajectories, using CellChat, Monocle 2, and spatial transcriptomics. In this study, an enterocyte state with high lactylation-associated transcriptional scores was identified.
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