Applying machine learning to screen for acute myocardial infarction-related biomarkers and immune infiltration features and validate it clinically and experimentally

Acute myocardial infarction (AMI) has been responsible for 8.5 million deaths worldwide each year over the past decade and is the leading cause of death. It is a severe illness worldwide and can happen in multiple age categories. Despite the significant progress in fundamental and clinical studies of AMI, biomarkers for AMI development have not been adequately investigated. The present research aimed to characterize potential new biomarkers of AMI by comprehensive analysis and to explore the immune infiltration characteristics of this pathophysiological process. In this study, we identified 68 DEGs and performed gene set enrichment analysis, GO, disease oncology, and KEGG analysis, and the results suggested that several functional signaling pathways and essential genes were strongly related to the onset and progression of AMI. In addition, combining multiple algorithms, FCER1G, CLEC4D, SRGN, and SLC11A1 were determined to be prospective biomarkers of AMI and showed good diagnostic value. Immuno-infiltration analysis suggested that neutrophils, CD8+ T cells, monocytes, and M0 macrophages might be involved in the onset and progress of AMI. In conclusion, a combined approach was employed to select biomarkers associated with AMI and to probe the critical function of immune cells in the progression of AMI. In addition, clinical studies were applied to analyze the correlation between the occurrence of AMI and lipid dysregulation.