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<p>Acute myocardial infarction (AMI) is a major global health issue, prompting the search for new biomarkers and therapies. This study seeks to develop a lactylation-related model for diagnosing AMI and identifying therapeutic targets. Using the GSE62646 dataset, we performed differential expression analysis applying the ‘limma’ package, which revealed 1896 up-regulated and 2403 down-regulated genes. Subsequently, functional enrichment analysis (gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA)) revealed that DEGs mostly enriched in modification, metabolism, and immune-related pathways. Moreover, weighted gene co-expression network analysis (WGCNA) found two key modules with 5697 genes, leading to 37 AMI candidate genes. Additionally, machine learning algorithms (including random forest (RF) and support vector machine (SVM)) narrowed this to 4 hub genes (CA5BP1, CTSD, CD46, CEBPB) for a diagnostic nomogram, and then confirmed in two external cohorts. Furthermore, the assessment of immune cell infiltration using CIBERSORT and ssGSEA demonstrated that immune dysregulation in AMI was noted, with hub genes linked to immune cells. Key transcription factors (TFs) and potential drugs associated with hub genes were subsequently identified via the Enrichr platform. And VDR emerged as the most important transcription factor linked to lactylation in AMI. Finally, 14 identified candidate drugs exhibited significant binding affinities to the hub genes through molecular docking. In conclusion, our findings present a novel lactylation-related diagnostic nomogram for AMI that holds promise for serum-based diagnostic applications, while further clinical investigations are necessary to assess its effectiveness.</p>

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A novel lactylation-related gene signature for diagnostic and therapeutic insights in acute myocardial infarction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月28日

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  • Tao Yi,
  • Qian Lei

摘要

Acute myocardial infarction (AMI) is a major global health issue, prompting the search for new biomarkers and therapies. This study seeks to develop a lactylation-related model for diagnosing AMI and identifying therapeutic targets. Using the GSE62646 dataset, we performed differential expression analysis applying the ‘limma’ package, which revealed 1896 up-regulated and 2403 down-regulated genes. Subsequently, functional enrichment analysis (gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA)) revealed that DEGs mostly enriched in modification, metabolism, and immune-related pathways. Moreover, weighted gene co-expression network analysis (WGCNA) found two key modules with 5697 genes, leading to 37 AMI candidate genes. Additionally, machine learning algorithms (including random forest (RF) and support vector machine (SVM)) narrowed this to 4 hub genes (CA5BP1, CTSD, CD46, CEBPB) for a diagnostic nomogram, and then confirmed in two external cohorts. Furthermore, the assessment of immune cell infiltration using CIBERSORT and ssGSEA demonstrated that immune dysregulation in AMI was noted, with hub genes linked to immune cells. Key transcription factors (TFs) and potential drugs associated with hub genes were subsequently identified via the Enrichr platform. And VDR emerged as the most important transcription factor linked to lactylation in AMI. Finally, 14 identified candidate drugs exhibited significant binding affinities to the hub genes through molecular docking. In conclusion, our findings present a novel lactylation-related diagnostic nomogram for AMI that holds promise for serum-based diagnostic applications, while further clinical investigations are necessary to assess its effectiveness.