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Objectives <p>To investigate lactylation-driven mechanisms in the pathogenesis of sepsis-induced acute respiratory distress syndrome (ARDS).</p> Methods <p>Multi-cohort transcriptomic data sets (GSE10474, GSE32707, and GSE66890) were integrated with machine learning algorithms (LASSO, support vector machine, random forest) to identify differentially expressed lactylation-related genes (LRGs). Five hub genes (ALDH1A1, CALM1, CCNA2, HIST1H2BN, SH3GL1) were prioritized. Orthogonal experimental validation was performed using qRT-PCR and Western blotting. Subsequent analyses explored immune cell correlations (focusing on ALDH1A1), regulatory networks (transcription factors and miRNAs), and potential therapeutic drug candidates.</p> Results <p>Integration of bioinformatics analyses identified 25 differentially expressed LRGs and prioritized 5 hub genes. Experimental validation (qRT-PCR/Western blot) consistently demonstrated downregulation of all five hub gene proteins. Notably, this contradicted the bioinformatically predicted upregulation of CCNA2, HIST1H2BN, and SH3GL1, revealing a significant transcriptional–translational discordance. Further analysis revealed ALDH1A1-associated myeloid-derived suppressor cell and neutrophil correlations, a regulatory network comprising 68 transcription factors and 79 miRNAs, and 26 prioritized drug candidates.</p> Conclusions <p>This study establishes a protein-verified lactylation-related gene signature with diagnostic relevance for sepsis-induced ARDS. Crucially, it highlights post-transcriptional regulation, evidenced by the discordance between mRNA levels and protein expression of key hub genes, as a key mechanistic feature in the pathogenesis of this condition. The identified regulatory networks and drug candidates provide potential avenues for further research and therapeutic development.</p>

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Lactylation modulates immune infiltration in sepsis-induced acute respiratory distress syndrome: a multi-omics and machine learning study with experimental confirmation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月10日

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  • Tao Suo,
  • Mengmeng Xu,
  • Jin Fang

摘要

Objectives

To investigate lactylation-driven mechanisms in the pathogenesis of sepsis-induced acute respiratory distress syndrome (ARDS).

Methods

Multi-cohort transcriptomic data sets (GSE10474, GSE32707, and GSE66890) were integrated with machine learning algorithms (LASSO, support vector machine, random forest) to identify differentially expressed lactylation-related genes (LRGs). Five hub genes (ALDH1A1, CALM1, CCNA2, HIST1H2BN, SH3GL1) were prioritized. Orthogonal experimental validation was performed using qRT-PCR and Western blotting. Subsequent analyses explored immune cell correlations (focusing on ALDH1A1), regulatory networks (transcription factors and miRNAs), and potential therapeutic drug candidates.

Results

Integration of bioinformatics analyses identified 25 differentially expressed LRGs and prioritized 5 hub genes. Experimental validation (qRT-PCR/Western blot) consistently demonstrated downregulation of all five hub gene proteins. Notably, this contradicted the bioinformatically predicted upregulation of CCNA2, HIST1H2BN, and SH3GL1, revealing a significant transcriptional–translational discordance. Further analysis revealed ALDH1A1-associated myeloid-derived suppressor cell and neutrophil correlations, a regulatory network comprising 68 transcription factors and 79 miRNAs, and 26 prioritized drug candidates.

Conclusions

This study establishes a protein-verified lactylation-related gene signature with diagnostic relevance for sepsis-induced ARDS. Crucially, it highlights post-transcriptional regulation, evidenced by the discordance between mRNA levels and protein expression of key hub genes, as a key mechanistic feature in the pathogenesis of this condition. The identified regulatory networks and drug candidates provide potential avenues for further research and therapeutic development.