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Identification and validation of feature genes of acute myocardial infarction based on ferroptosis-related genes
Yuting Zhou1,2, Yehong Liu1, Baida Xu1
1The 904th Hospital of Joint Logistic Support Force of PLA, Wuxi, 214000, Jiangsu, China.
Insights
Machine learning identified key ferroptosis-related genes (FDEGs) for diagnosing acute myocardial infarction (AMI). This diagnostic model shows excellent accuracy, offering new insights into ferroptosis in AMI.
Area of Science:
- Biomedical research
- Cardiovascular disease
- Cell death mechanisms
Background:
- Acute myocardial infarction (AMI) is a severe complication of coronary artery disease (CAD) and a leading global cause of mortality.
- Ferroptosis, a cell death mode involving iron accumulation and lipid peroxidation, is increasingly implicated in myocardial infarction.
- The precise role and prognostic significance of ferroptosis in AMI require further investigation.
Purpose of the Study:
- To identify ferroptosis-related differentially expressed genes (FDEGs) in AMI.
- To construct and validate a diagnostic model for AMI based on FDEGs using machine learning.
- To explore the potential of FDEGs as biomarkers for AMI.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets (GSE97320, GSE60993) for AMI and control samples.
- Applied Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to FDEGs.
- Employed Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for model construction and ROC curves for validation.
Main Results:
- Identified CYB5R1, TSC1, LAMP2, PARK7, and MGST1 as characteristic FDEGs in AMI.
- Developed a diagnostic model using these FDEGs demonstrating excellent diagnostic ability for AMI.
- Validated the model's accuracy using independent datasets (GSE48060, GSE59867) and qRT-PCR.
Conclusions:
- Machine learning is a reliable method for identifying FDEGs in AMI.
- The developed FDEG-based model shows promise for AMI diagnosis.
- This research provides novel insights into the role of ferroptosis in AMI pathogenesis and diagnosis.
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.
