Abstract
Acute myocardial infarction (AMI) stands as a major contributor to mortality and disability worldwide, with obesity playing a significant role in its development. This study aims to investigate potential biomarkers associated with obesity-related genes (ORGs) in patients with AMI. We downloaded four training gene expression datasets and one validation dataset from the Gene Expression Omnibus (GEO) database, and extracted ORGs from the GeneCards database. Feature genes were identified using machine learning techniques, and their diagnostic potential was evaluated through receiver operating characteristic (ROC) curves. To assess the identified critical pathways, Gene Set Enrichment Analysis (GSEA) was performed. Levels of immune infiltration were analyzed using the CIBERSORT algorithm and single-sample Gene Set Enrichment Analysis (ssGSEA). A signature comprising five genes (IL1RN, TLR2, NFKBIA, MMP9, and ITLN1) was established as a diagnostic biomarker for AMI, achieving an area under the curve (AUC) of 0.924, which was confirmed in the GSE59876 dataset (AUC = 0.825). According to the diagnostic model, comparisons between high- and low-risk groups revealed six distinct immune cell types and thirteen differing immune functions. Validation through reverse transcription quantitative polymerase chain reaction (RT-qPCR) reaffirmed the differential expression of the signature genes in AMI and control samples. Our findings provide crucial insights into the roles of ORGs in AMI and may facilitate the identification of valuable biomarkers for AMI diagnosis.