Disulfidptosis-related gene in acute myocardial infarction and its diagnostic value and functions based on bioinformatics analysis and machine learning

Background Acute myocardial infarction (AMI) is a major cause of morbidity and mortality. Disulfidptosis, a novel form of programmed cell death, has been largely unexplored in AMI. This study aims to identify disulfidptosis-related genes in AMI and assess their diagnostic potential using bioinformatics and machine learning. Methods The microarray datasets GSE60993 and GSE61144, associated with Acute Myocardial Infarction (AMI), were obtained from the Gene Expression Omnibus (GEO) database. Differential disulfidptosis-associated genes were identified via differential expression analysis. The disulfidptosis related genes were collected from FerrDb V2 and the differentially expressed disulfidptosis related genes were utilized to construct a Protein-Protein Interaction (PPI) network. Key genes were identified utilizing a Protein-Protein Interaction (PPI) network and plugins available in Cytoscape. The key genes were used to detect potential biomarkers by receiver operating characteristic (ROC) analysis.Next, GO and KEGG analyses, as well as correlation analysis were performed on the key genes, and potential drug molecules targeting these genes were also analyzed. At the same time, key genes further screened by Support Vector Machine (SVM), Lasso regression, as well as random forest. By intersecting the results of the three, we ended up with hub genes for AMI. The expression of these key genes was verified using external dataset GSE61144. Results A total of 16 differentially expressed disulfidptosis related genes were identified and these genes were mainly enriched in the pathways of “regulation of actin cytoskeleton organization”, “regulation of actin filament-based process”, “regulation of actin filament organization”, “cell cortex”, “cell leading edge”, “cadherin binding”, “actin filament bindin, and “D-glucose transmembrane transporter activity”. The top 10 hub genes ACTB, RAC1, IQGAP1, FLNB, MYL6, ABI2, DBN1, PRDX1, SLC2A1 and SLC2A3 were identified from the PPI network. Further screening using Support Vector Machine (SVM), Lasso regression and random forest, and intersecting the results of these analyses, led to the identification of DBN1, RAC1, and SLC2A3 as final hub genes in AMI. While the final key genes DBN1 and SLC2A3 were significantly differentially expressed in external dataset GSE61144 with AUC ≥ 0.7. Conclusion In this study, we identified differentially expressed disulfidptosis related genes in blood samples from AMI patients using existing datasets. The research delved into the expression patterns and molecular mechanisms of differentially expressed disulfidptosis related genes in AMI, offering a foundation for precise AMI diagnosis and the identification of novel therapeutic targets.