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<p>Acute myocardial infarction (AMI) triggered cardiomyocyte senescence and impaired cardiac function. Methylation modifications and related enzymes in patients were also significantly altered, but the association between cellular senescence and demethylation remained unclear. This study obtained data from public databases, screened candidate genes by intersecting differentially expressed genes (DEGs) with related genes, identified characteristic genes using three machine learning methods to determine biomarkers, and explored their regulatory mechanisms through gene set enrichment analysis (GSEA) and other approaches. Finally, BCL3, MMP9, NAMPT, and TLR4 were identified as potential biomarkers. GSEA showed they were significantly enriched in 18 common pathways. Thirteen immune cells, such as CD8 T cells, differed significantly between AMI and control samples. A total of 58 miRNAs, 10 lncRNAs, and 242 transcription factors (TFs) were predicted to interact with these biomarkers. Targeted analysis predicted 25 potential therapeutic drugs for AMI, and the biomarkers could target Retinoic acid CTD 00006918. GeneMANIA analysis identified 20 functionally related genes, while Friends analysis showed BCL3 had prominent functional similarity. Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) results validated the expression of biomarkers, consistent with bioinformatics analyses. These findings may provide valuable clues for AMI treatment.</p>

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Identification and validation of biomarkers associated with cellular senescence and demethylation in acute myocardial infarction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Wenhua Su,
  • Hong Huang,
  • Qingrong Ruan,
  • Shiqi Liu,
  • Juhua Dan,
  • Yan Zhao,
  • Hong Zhang,
  • Qian Huo

摘要

Acute myocardial infarction (AMI) triggered cardiomyocyte senescence and impaired cardiac function. Methylation modifications and related enzymes in patients were also significantly altered, but the association between cellular senescence and demethylation remained unclear. This study obtained data from public databases, screened candidate genes by intersecting differentially expressed genes (DEGs) with related genes, identified characteristic genes using three machine learning methods to determine biomarkers, and explored their regulatory mechanisms through gene set enrichment analysis (GSEA) and other approaches. Finally, BCL3, MMP9, NAMPT, and TLR4 were identified as potential biomarkers. GSEA showed they were significantly enriched in 18 common pathways. Thirteen immune cells, such as CD8 T cells, differed significantly between AMI and control samples. A total of 58 miRNAs, 10 lncRNAs, and 242 transcription factors (TFs) were predicted to interact with these biomarkers. Targeted analysis predicted 25 potential therapeutic drugs for AMI, and the biomarkers could target Retinoic acid CTD 00006918. GeneMANIA analysis identified 20 functionally related genes, while Friends analysis showed BCL3 had prominent functional similarity. Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) results validated the expression of biomarkers, consistent with bioinformatics analyses. These findings may provide valuable clues for AMI treatment.