ORIGINAL RESEARCH article
Front. Med.
Sec. Translational Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1554791
Transcriptomic Profiling of Burn Patients Reveals Key Lactylation-Related Genes and Their Molecular Mechanisms
Provisionally accepted
- Jinhua Central Hospital, Jinhua, China
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Introduction: Burn injury is a global health concern accompanied by complex pathophysiological alterations. Understanding the gene expression changes and molecular pathways, especially those related to lactylation, is crucial for developing effective treatments. This study aimed to analyze the transcriptomic profiles of burn patients and identify lactylation-related genes as potential biomarkers or therapeutic targets.Methods: Peripheral blood transcriptome data of burn patients and controls were obtained from GEO. After preprocessing to remove batch effects and normalize data, differential genes were screened. Functional enrichment, lactylation gene analysis, machine learning for key gene selection, immune cell infiltration analysis, gene correlation and GSEA analysis, patient clustering, and upstream regulatory factor prediction were performed using various R packages. Statistical analysis was done with R software, and P < 0.05 was considered significant.Results: Pathway enrichment analysis in burn patients showed significant differences in immune-related pathways. Lactylation genes were differentially expressed, with changes in RNA processing and cell interactions. Machine learning identified four key lactylation-related molecules (RPL14, SET, ENO1, PPP1CC). Immune microenvironment analysis revealed correlations with immune cell infiltration. Clustering analysis based on these molecules divided burn patients into two subgroups with distinct gene expression patterns and pathway enrichments.This study provides insights into the molecular alterations in burn patients, especially regarding lactylation. The identified molecules and pathways offer potential targets for personalized treatment. Future research should validate these findings and explore their clinical applications for improving burn patient management and prognosis.
Keywords: lactylation, machine learning, burn, RPL14, SET, ENO1, PPP1CC
Received: 07 Jan 2025; Accepted: 06 Jun 2025.
Copyright: © 2025 Li, Ma, Wang, Zhan and Wang. 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: Qian Wang, Jinhua Central Hospital, Jinhua, China
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