Zulong Sheng ^* Rui Zhang Zhenjun Ji Zhuyuan Liu Yaqing Zhou

• Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China, Nanjing, China

Background: Acute myocardial infarction (AMI) as a subset of acute coronary syndrome remains the major cause of mortality worldwide. Mitochondrial dysfunction is critically involved in AMI progression, and mitophagy plays a vital role in eliminating damaged mitochondria. This study aimed to explore the mitophagy-related biomarkers and their potential molecular basis in AMI. Methods: AMI datasets (GSE24519 and GSE34198) from Gene Expression Omnibus (GEO) database were combined and the batch effects were removed. Differentially expressed genes (DEGs) in AMI were selected and intersected with mitophagy-related genes for mitophagy-related differentially expressed genes (MRDEGs), and then subject to enrichment analyses. Next, MRDEGs were screened by machine learning methods (logistic regression analysis, RandomForest, LASSO) to construct a diagnostic risk model and select the key genes in AMI. The diagnostic efficacy of the model was evaluated using a nomogram. Moreover, infiltration pattern of different immune cells between two risk groups was compared. We also explore the interactions between key genes themselves or with miRNAs/TFs and drug compounds, and visualized the protein structure of key genes. Finally, we explored the validated the expression of key genes in plasma samples of AMI patients and healthy individuals. Results: We screened 28 MRDEGs in AMI. Based on machine learning methods, 12 key genes were screened for the diagnostic risk model, including AGPS, CA2, CAT, LTA4H, MYO9B, PRDX6, PYGB, SIRT3, TFEB, TOM1, UBA52, UBB. The nomogram further revealed the accuracy of the model in AMI diagnosis. Moreover, we found lower abundance of immune cells such as gamma delta T and Natural killer cell in high-risk group, and expression of key genes showed significant correlation with immune infiltration level in both groups. Finally, 64 pairs of miRNA-mRNA, 75 pairs of TF-mRNA, 119 pairs of RBP-mRNA and 32 pairs of drug-mRNA interaction networks were obtained. Conclusions: The 12 key MRDEGs were identified and a risk model have been constructed for AMI diagnosis. The findings of this study might provide novel biomarkers for improving the detection of AMI.