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

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. 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 dr

SUBMITTER: Li L 

PROVIDER: S-EPMC12336706 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature