Identification of Oxidative Stress-Related Biomarkers in Acute Myocardial Infarction.

Purpose: Acute Myocardial Infarction (AMI) is globally prevalent, with oxidative stress as a key contributor to its pathogenesis. This study aimed to explore oxidative stress-related genes as potential AMI biomarkers, elucidating their role in disease progression. Patients and Methods: Gene expression data from AMI samples in the Gene Expression Omnibus (GEO) database and oxidative stress-related genes (OSRGs) from the GeneCards database were extracted. Weighted Gene Co-expression Network Analysis (WGCNA) identified key module genes associated with AMI. Intersecting OSRGs, key module genes, and differentially expressed genes (DEGs) between AMI and normal samples led to the extraction of differentially expressed ORSGs (DE-ORSGs) related to AMI. Feature genes were mined using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine (SVM) algorithm, followed by potential diagnostic value assessment using receiver operating characteristic (ROC) curves. Gene Set Enrichment Analysis (GSEA) was executed on the identified key genes. Immune infiltration levels were explored using the CIBERSORT algorithm, and a Transcription Factor (TF) -mRNA regulatory network of key genes was created. The key genes were validated using qRT-PCR. Results: We authenticated three key genes (MMP9, TGFBR3, and S100A12) from 6 DE-ORSGs identified in AMI. GSEA revealed that these key genes were enriched in immune-related signaling pathways. Immune infiltration analysis identified three differential immune cell types (resting NK cells, Monocytes, and M0 Macrophages) between AMI and normal groups. Correlation analysis revealed positive associations of MMP9 with M0 Macrophages and S100A12 with Monocytes and M0 Macrophages, whereas TGFBR3 was negatively related to Monocytes. A TF-mRNA regulatory network was generated based on these key genes. qRT-PCR validation confirmed the differential expression of S100A12 and TGFBR3 between AMI and control samples. Conclusion: TGFBR3, and S100A12 were identified as potential oxidative stress-related biomarkers in AMI, providing new insights for AMI diagnosis and treatment.