ArticleFrontiers in genetics2022
Machine Learning Revealed Ferroptosis Features and a Novel Ferroptosis-Based Classification for Diagnosis in Acute Myocardial Infarction.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 23 citations in OpenAlex.
- Generative Artificial Intelligence vs. Transformer and Benchmarking Against Deep/Machine Learning: Classification and Scientific Validation of Heart Failure Patients Using Women's Transcriptomic Gene Data.Diagnostics (Basel, Switzerland) · 2026Article
- TWAS atlas 2.0: an updated data resource for transcriptome-wide association studies.Nucleic acids research · 2026Article
- Ginsenoside Re attenuates homocysteine-induced endothelial cell ferroptosis through upregulation of GPX4/xCT signaling.Experimental and therapeutic medicine · 2026Article
- Deciphering the clinical implication of an obesity-related gene signature as the novel biomarker for acute myocardial infarction diagnosis.Scientific reports · 2025Article
- Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation.Molecular biotechnology · 2025Review
- Interaction between post-tumor inflammation and vascular smooth muscle cell dysfunction in sepsis-induced cardiomyopathy.Frontiers in immunology · 2025Article
- Molecular subtype identification of cerebral ischemic stroke based on ferroptosis-related genes.Scientific reports · 2024Article
- Expression levels and clinical significance of ferroptosis-related genes in patients with myocardial infarction.Scientific reports · 2024Article
- Identification and Verification of Ferroptosis-Related Genes in Keratoconus Using Bioinformatics Analysis.Journal of inflammation research · 2024Article
- Identification of angiogenesis-related genes in diabetic foot ulcer using machine learning algorithms.Heliyon · 2023Article
- Potential diagnostic biomarkers: 6 cuproptosis- and ferroptosis-related genes linking immune infiltration in acute myocardial infarction.Genes and immunity · 2023Article
- Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning.Frontiers in endocrinology · 2023Article
- Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning.Frontiers in genetics · 2022Article
- Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction.Oxidative medicine and cellular longevity · 2022Article
- Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network.Frontiers in cardiovascular medicine · 2022Article
- Roles of Ferroptosis in Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2022Review
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
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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