European journal of medical research · 2025
Identification and validation of feature genes of acute myocardial infarction based on ferroptosis-related genes.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhou Yuting, Liu Yehong, Xu Baida, Jin Tianhui, Ye Ting, Su Wentao, Li Chengsi, Kang Tingting, Xie Haoran, Zong Gangjun
Аннотация
BACKGROUND: Acute myocardial infarction (AMI) represents one of the most severe complications of coronary artery disease (CAD) and is a leading cause of mortality from noncommunicable diseases globally. In recent years, with the deepening of research on ferroptosis, the role of this cell death mode accompanied by iron accumulation and lipid peroxidation in myocardial infarction has been increasingly confirmed, which eventually leads to cell oxidative death. However, prognosis mechanism, and participation degree of ferroptosis in the occurrence of AMI still need to be further explored.
METHODS: The gene expression data sets (GSE97320, GSE60993) of AMI patients and the control group were obtained from the Gene Expression Omnibus (GEO) data sets. Screening for ferroptosis-related differentially expressed genes (FDEGs). In addition, the biological functions of these FDEGs were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) were used to further check FDEGs to construct the prognostication model. ROC curves were used to validate the model's accuracy. GSE48060 and GSE59867 were set as a validation set to verify the model. Finally, the peripheral blood of AMI and control samples was collected for qRT-PCR to examine the differential expression situation of FDEGs.
RESULTS: The CYB5R1, TSC1, LAMP2, PARK7, and MGST1 were identified as the most characteristic FDEGs, and the diagnostic model composed of them has excellent diagnostic ability for AMI.
CONCLUSIONS: The identification of FDEGs in AMI by machine learning is a reliable research method that can provide further ideas for the study of ferroptosis in AMI.
METHODS: The gene expression data sets (GSE97320, GSE60993) of AMI patients and the control group were obtained from the Gene Expression Omnibus (GEO) data sets. Screening for ferroptosis-related differentially expressed genes (FDEGs). In addition, the biological functions of these FDEGs were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) were used to further check FDEGs to construct the prognostication model. ROC curves were used to validate the model's accuracy. GSE48060 and GSE59867 were set as a validation set to verify the model. Finally, the peripheral blood of AMI and control samples was collected for qRT-PCR to examine the differential expression situation of FDEGs.
RESULTS: The CYB5R1, TSC1, LAMP2, PARK7, and MGST1 were identified as the most characteristic FDEGs, and the diagnostic model composed of them has excellent diagnostic ability for AMI.
CONCLUSIONS: The identification of FDEGs in AMI by machine learning is a reliable research method that can provide further ideas for the study of ferroptosis in AMI.