Integrated multi-omics and experimental validation for identifying novel biomarkers of acute myocardial infarction.

Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality, and early diagnosis and personalized therapy are constrained by complex molecular mechanisms. We integrated high-throughput bulk RNA sequencing with weighted gene co-expression network analysis (WGCNA) to prioritize AMI-associated genes, externally validated findings in two independent cohorts, and screened candidates using machine learning models. Diagnostic performance was assessed by receiver operating characteristic area under the curve, and immune-cell associations were explored. Single-cell RNA sequencing (scRNA-seq) across AMI stages provided orthogonal validation via gene set variation analysis, inferred cell-cell communication, pseudotime trajectories, and cell-type proportion analyses. Genetic support for causality was evaluated using two-sample bidirectional Mendelian randomization (MR) and summary-data-based MR (SMR). We additionally predicted compound-target interactions through in silico molecular docking of Comparative Toxicogenomics Database sourced compounds and examined histology and gene expression in rat and H9C2 models using hematoxylin and eosin (HE) staining, Western blotting, and qRT-PCR. MR/SMR analyses supported potential causal contributions of STAT3 (OR 1.417, 95% CI 1.063-1.889, P