Identification of Biomarkers for Acute Myocardial Infarction based on Cell Senescence Genes and Machine Learning

Abstract

Abstract Background This study aims to identify senescence-related biomarkers for ST-elevation myocardial infarction (STEMI) prognosis.Methods RNA expression data for STEMI samples and controls were obtained from the Gene Expression Omnibus (GEO) database, and cellular senescence genes were acquired from CellAge database. Differential and overlap analyses were used to identify differentially expressed cellular senescence-related genes (DE-SRGs) in STEMI samples. DE-SRGs were further analyzed using plotting receiver operator characteristic (ROC) curves and machine learning algorithms. Gene Set Enrichment Analysis (GSEA) was employed on each biomarker. Immune related analyses, competing endogenous RNA (ceRNA) construction, and target drug prediction were performed on biomarkers.Results This study identified 7 DE-SRGs for STEMI prognosis. GSEA results showed enriched pathways, including ribosome, autophagy, allograft rejection, and autoimmune thyroid disease. Further, T cells CD4 memory resting, T cells gamma delta, Monocytes and Neutrophils represented significantly different proportions between STEMI samples and controls. In addition, CEBPB was positively correlated with Monocytes and Neutrophils, but negatively correlated with T cells CD8. A ceRNA network was established and eight FDA-approved drugs were predicted.Conclusion This study identified 7 cellular senescence-related biomarkers, which could lay a foundation for further study of the relationship between STEMI and cellular senescence.