Anatolian journal of cardiology · 2025
Identification of Biomarkers for Acute Myocardial Infarction Based on Cell Senescence Genes and Machine Learning.
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Li Liya, Qin Pei
Аннотация
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. Differentially expressed cellular senescence-related genes were further analyzed by plotting receiver operating 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. Gene Set Enrichment Analysis results showed enriched pathways, including ribosomes, autophagy, allograft rejection, and autoimmune thyroid disease. Furthermore, 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-cell CD8. A ceRNA network was established, and 8 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.
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. Differentially expressed cellular senescence-related genes were further analyzed by plotting receiver operating 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. Gene Set Enrichment Analysis results showed enriched pathways, including ribosomes, autophagy, allograft rejection, and autoimmune thyroid disease. Furthermore, 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-cell CD8. A ceRNA network was established, and 8 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.