Identification of Diagnostic Biomarkers for Myocardial Infarction Using Bioinformatics and Disulfidptosis-Targeted Computational Drug Discovery.

Disulfidptosis, a newly discovered form of regulated cell death, is involved in multiple disease processes. This study applied computational methods to identify disulfidptosis-related genes in myocardial infarction (MI). Differentially expressed genes (DEGs) from GSE66360 dataset were screened using the limma package and intersected with genes in weighted gene coexpression network analysis (WGCNA) modules to obtain candidate genes. Biomarkers were selected via support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO), and validated by quantitative real-time (qRT)-PCR, CCK-8, and flow cytometry. Enrichment and immune infiltration analyses were performed using clusterProfiler and CIBERSORT tools. Potential drugs were predicted via the Coremine database and visualized with Cytoscape. Seurat and CellChat packages were employed to perform single-cell transcriptomic analysis and develop cell-cell communication network, respectively. The genes in the lightgreen module that had the highest correlation with immune scores were selected. Next, we identified 10 biomarkers (, , , , , , , , , and ), all of which showed significantly higher mRNA levels in AC16-oxygen-glucose deprivation (OGD) cells compared to controls. Silencing and enhanced cell viability and reduced apoptosis in AC16-OGD cells. Immune infiltration analysis suggested that and modulated T cell function, contributing to MI pathogenesis. Drug analysis predicted 15 candidate drugs targeting both and . Single-cell analysis showed that distinguished six cell types in MI, with adipocytes serving as a communication hub interacting closely with cardiomyocytes, fibroblasts, endothelial cells, and macrophages. These findings highlighted the potential of the identified biomarkers as novel therapeutic targets for MI.