Identification of Immune-Related Genes in Patients with Acute Myocardial Infarction Using Machine Learning Methods

OBJECTIVE: This study aimed to analyze immune-related genes and immune cell components in the peripheral blood of patients with acute myocardial infarction (AMI). METHODS: Six datasets were obtained from the GEO repository comprising 88 healthy samples and 215 AMI samples. We performed the weighted gene co-expression analysis (WGCNA) and five machine learning (ML) methods to identify immune-related genes and construct diagnostic models. CIBERSORT algorithm was adopted for the assessment of the degree of immune infiltration. Finally, RT-PCR, immunofluorescence double and immunohistochemistry were conducted to analyze the expression level of the identification of featured immune-related genes and localization relationship in heart tissue of AMI mouse model. RESULTS: A total of 496 immune-related DEGs were obtained between AMI and normal samples. WGCNA finally determined the co-expression modules that showed the most significantly positively associated with AMI (r=0.41; P<0.001). Among the five ML models, XGBoost had the highest AUC (0.849) and accuracy (0.812) to discriminate patients with AMI from normal in the validation sets. Furthermore, we found that the proportion of chemokine receptor (CCR), macrophages, neutrophils, and Treg cells in the AMI groups was significantly higher than that in the normal groups. In vitro RT-PCR verification revealed that SOCS3, MMP9, and AQP9 expression increased significantly in the AMI mouse model. Among the 22 immune cells, AQP9, MMP9, and SOCS3 displayed the strongest positive correlation with neutrophils. In MI-mice, MPO stained strongly along the lateral cardiomyocytes, whereas it was weaker in sham mice. Combined immunofluorescence was observed in same parts of the cytoplasm of cardiomyocytes in myocardial infarction area, indicating co-localization of MPO with MMP9 and SOCS3 in these areas, respectively. CONCLUSION: Immune-related genes and immune cells are intimately related to AMI. Constructing different ML models based on these biomarkers could be a valuable approach to diagnosing AMI in clinical practice.