ORIGINAL RESEARCH article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders: Autoinflammatory Disorders
This article is part of the Research TopicInnovations in targeting intestinal immunity for chronic inflammatory disordersView all 23 articles
A lactylation modification-related prediction model for the diagnosis of ulcerative colitis based on machine learning
Provisionally accepted
- 1Wuhan University Renmin Hospital, Wuhan, China
- 2Xi'an Jiaotong University, Xi'an, China
- 3Renmin Hospital of Wuhan University, Wuhan, China
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Background: Lactylation modification serves as a critical link between metabolic reprogramming and epigenetic regulation, playing a significant role in the progression of both malignant tumors and inflammatory diseases. Nevertheless, its specific function in the pathogenesis of ulcerative colitis (UC) remains poorly understood. Methods: The hub genes associated with lactylation in UC were identified and validated by mining three UC-related datasets (GSE206285, GSE75214 and GSE87466) from the GEO database, and we created a lactylation-related prediction model for the diagnosis of UC. The lactylation levels of different immune cells were also investigated via single-cell (sc) RNA-sequencing data. Finally, the core genes of lactylation were validated in vitro. Results: Four lactylation-related core genes (HIF1A, SLC25A12, SLC16A3, PFKFB2) that are closely correlated with UC were identified by three machine learning methods, and the lactylation-related prediction model based on the four genes exhibited outstanding diagnostic performance for UC (AUC:0.976, 95%CI:0.941-1.00). scRNA-sequencing analysis revealed that HSC, NK and macrophage cells were exhibited higher lactylation-related score in UC compared to other immune cells. After Nala intervention, the expressions of the four core genes were significantly increased. While the expressions of the four genes were significantly decreased after treatment with 2-DG. Conclusion: By applying machine learning methods to analyze sequencing data, we identified core lactylation-related genes in UC and developed a diagnostic model with high predictive performance. Furthermore, based on scRNA-seq data, we investigated lactylation modifications across seven types of immune cells in UC patients, providing valuable insights into the interplay between lactylation and immune cells in UC.
Keywords: lactylation, machine learning, predictive model, random forest, ulcerative colitis
Received: 01 Oct 2025; Accepted: 16 Feb 2026.
Copyright: © 2026 LIU, Kang, Zhou and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiao Li
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