Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanisms
IntroductionBurn injury is a global health concern characterized by complex pathophysiological changes. Understanding gene expression changes and molecular pathways, especially those related to lactylation, is crucial for developing effective treatments. This study aimed to analyze the transcriptomi...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1554791/full |
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| author | Yang Li Jizhong Ma Yeping Wang Weibin Zhan Qian Wang |
| author_facet | Yang Li Jizhong Ma Yeping Wang Weibin Zhan Qian Wang |
| author_sort | Yang Li |
| collection | DOAJ |
| description | IntroductionBurn injury is a global health concern characterized by complex pathophysiological changes. Understanding 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.MethodsPeripheral blood transcriptome data of burn patients and controls were obtained from the GEO database. After preprocessing to remove batch effects and normalize the 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 conducted using R software, with a p-value of < 0.05 considered significant.ResultsPathway enrichment analysis in burn patients showed significant alterations 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, and PPP1CC). Immune microenvironment analysis revealed correlations with immune cell infiltration. Clustering analysis based on these four molecules divided burn patients into two subgroups, each exhibiting distinct gene expression patterns and pathway enrichments.ConclusionThis study provides insights into the molecular alterations in burn patients, especially regarding lactylation. The identified key 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. |
| format | Article |
| id | doaj-art-4d176eef632d456f800f3cedc3395a07 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-4d176eef632d456f800f3cedc3395a072025-08-20T03:24:06ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15547911554791Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanismsYang LiJizhong MaYeping WangWeibin ZhanQian WangIntroductionBurn injury is a global health concern characterized by complex pathophysiological changes. Understanding 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.MethodsPeripheral blood transcriptome data of burn patients and controls were obtained from the GEO database. After preprocessing to remove batch effects and normalize the 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 conducted using R software, with a p-value of < 0.05 considered significant.ResultsPathway enrichment analysis in burn patients showed significant alterations 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, and PPP1CC). Immune microenvironment analysis revealed correlations with immune cell infiltration. Clustering analysis based on these four molecules divided burn patients into two subgroups, each exhibiting distinct gene expression patterns and pathway enrichments.ConclusionThis study provides insights into the molecular alterations in burn patients, especially regarding lactylation. The identified key 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.https://www.frontiersin.org/articles/10.3389/fmed.2025.1554791/fulllactylationmachine learningburnRPL14SETENO1 |
| spellingShingle | Yang Li Jizhong Ma Yeping Wang Weibin Zhan Qian Wang Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanisms Frontiers in Medicine lactylation machine learning burn RPL14 SET ENO1 |
| title | Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanisms |
| title_full | Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanisms |
| title_fullStr | Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanisms |
| title_full_unstemmed | Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanisms |
| title_short | Transcriptomic profiling of burn patients reveals key lactylation-related genes and their molecular mechanisms |
| title_sort | transcriptomic profiling of burn patients reveals key lactylation related genes and their molecular mechanisms |
| topic | lactylation machine learning burn RPL14 SET ENO1 |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1554791/full |
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