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Machine Learning Revealed Ferroptosis Features and a Novel Ferroptosis-Based Classification for Diagnosis in Acute
Dan Huang1, Shiya Zheng2, Zhuyuan Liu1
1Department of Cardiology, Zhongda Hospital, Southeast University, Nanjing, China.
Insights
This study identifies 11 ferroptosis-related genes (FRGs) for early acute myocardial infarction (AMI) diagnosis. Machine learning models using these FRGs show promising diagnostic efficiency for AMI.
Area of Science:
- Cardiovascular Medicine
- Genomics
- Biomarker Discovery
Background:
- Acute myocardial infarction (AMI) is a major global health concern.
- Current diagnostic markers for AMI lack sufficient sensitivity and specificity for early detection.
- Ferroptosis, a form of regulated cell death, significantly contributes to cardiac ischemic injury, but its regulatory mechanisms in AMI are not fully understood.
Purpose of the Study:
- To evaluate the diagnostic efficiency of ferroptosis-related genes (FRGs) for the early detection of acute myocardial infarction (AMI).
- To integrate transcriptome-wide association studies (TWAS) and mRNA expression data to identify novel AMI biomarkers.
- To develop and validate a machine learning-based prediction model for AMI diagnosis using FRGs.
Main Methods:
- Screened three Gene Expression Omnibus (GEO) datasets of peripheral blood samples.
- Integrated TWAS and mRNA expression data to identify FRGs associated with AMI.
- Utilized multiple machine learning algorithms to construct and validate an AMI prediction model.
Main Results:
- Identified 11 FRGs specifically expressed in the peripheral blood of AMI patients.
- Developed a prediction model with satisfactory diagnostic efficiency: AUC = 0.794 (training), 0.745 (validation 1), and 0.711 (validation 2).
- Demonstrated the involvement of FRGs in AMI progression.
Conclusions:
- Ferroptosis-related genes (FRGs) show potential as novel biomarkers for the early diagnosis of acute myocardial infarction (AMI).
- The developed machine learning model offers a promising tool for improving AMI diagnostic accuracy.
- FRGs represent potential molecular targets for future therapeutic strategies in AMI treatment.
Abstract:
Acute myocardial infarction (AMI) is a leading cause of death and disability worldwide. Early diagnosis of AMI and interventional treatment can significantly reduce myocardial damage. However, owing to limitations in sensitivity and specificity, existing myocardial markers are not efficient for early identification of AMI. Transcriptome-wide association studies (TWASs) have shown excellent performance in identifying significant gene-trait associations and several cardiovascular diseases (CVDs). Furthermore, ferroptosis is a major driver of ischaemic injury in the heart. However, its specific regulatory mechanisms remain unclear. In this study, we screened three Gene Expression Omnibus (GEO) datasets of peripheral blood samples to assess the efficiency of ferroptosis-related genes (FRGs) for early diagnosis of AMI. To the best of our knowledge, for the first time, TWAS and mRNA expression data were integrated in this study to identify 11 FRGs specifically expressed in the peripheral blood of patients with AMI. Subsequently, using multiple machine learning algorithms, an optimal prediction model for AMI was constructed, which demonstrated satisfactory diagnostic efficiency in the training cohort (area under the curve (AUC) = 0.794) and two external validation cohorts (AUC = 0.745 and 0.711). Our study suggests that FRGs are involved in the progression of AMI, thus providing a new direction for early diagnosis, and offers potential molecular targets for optimal treatment of AMI.
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