Abstract
BACKGROUND: Sphingolipid metabolism (SM) is linked to acute myocardial infarction (AMI), but its role remains unclear. This study explored SM-related genes (SMRGs) in AMI to support clinical diagnosis. METHODS: We analyzed datasets GSE48060 and GSE123342 to identify differentially expressed genes (DEGs) and key module genes. Protein-protein interaction (PPI) network analysis and machine learning were used to screen potential biomarkers, which were validated via receiver operating characteristic (ROC) curves and expression assessment. Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. Single-cell RNA sequencing (scRNA-seq) identified key cell types and their functions. Biomarkers were validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR). RESULTS: Intersection of 95 DEGs and 2,196 module genes yielded 20 genes, with ANXA3 and SOCS3 identified as biomarkers. The ANN model showed superior diagnostic performance compared to individual markers. Biomarkers were enriched in the toll-like receptor (TLR) signaling pathway. Immune infiltration analysis revealed differences in five immune cell types between AMI and control groups. ANXA3 correlated positively with neutrophils and negatively with resting memory CD4 T cells. Drugs targeting ANXA3 included ethanolamine, difluocortolone, and fluocinolone acetonide, with strong binding affinity. scRNA-seq identified B cells and monocytes as key cells; ANXA3 and SOCS3 expression increased during monocyte differentiation before decreasing, while B cells showed no significant changes. CONCLUSION: ANXA3 and SOCS3 were identified as SM-related biomarkers in AMI, providing insights for clinical diagnosis.