ArticleMedComm2023
Diagnosis of acute myocardial infarction using a combination of circulating circular RNA cZNF292 and clinical information based on machine learning.
Article in MedComm, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
- The emerging roles of small nucleolar RNAs, piwi-interacting RNAs, tRNA-derived small RNAs, and circular RNAs in the regulation of cardiac hypertrophy, ischemic heart disease, and heart failure.Genes & diseases · 2026Review
- Artificial intelligence-driven precision treatment of reproductive medicine-related diseases: the optimal protocol choice for IVF-ET.Journal of advanced research · 2026Article
- CircRNA-regulated programmed cell death networks in cardiomyocytes: Molecular crosstalk and therapeutic translation.Non-coding RNA research · 2026Review
- CircATXN2 exacerbates pulmonary arterial hypertension by modulating the miR-138-5p/SMURF1 axis.Respiratory research · 2026Article
- The Latest Advances in Omics Technology for Assessing Tissue Damage: Implications for the Study of Sudden Cardiac Death.International journal of molecular sciences · 2025Review
- Emerging role and clinical applications of circular RNAs in human diseases.Functional & integrative genomics · 2025Review
- CircRNA Networks in CAD: Multi-Cellular Mechanisms and Clinical Potential.International journal of general medicine · 2025Review
- Circular RNAs in Heart Diseases.Advances in experimental medicine and biology · 2025Review
- Research trends and hotspots of circular RNA in cardiovascular disease: A bibliometric analysis.Non-coding RNA research · 2024Article
- Review
- Review
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- From multi-omics approaches to personalized medicine in myocardial infarction.Frontiers in cardiovascular medicine · 2023Review
- Circular RNAs in breast cancer diagnosis, treatment and prognosis.Oncology research · 2023Review
Corrections and comments
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Authors and funding
15 authors at 9 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Circulating circular RNAs (circRNAs) are emerging as novel biomarkers for cardiovascular diseases (CVDs). Machine learning can provide optimal predictions on the diagnosis of diseases. Here we performed a proof-of-concept study to determine if combining circRNAs with an artificial intelligence approach works in diagnosing CVD. We used acute myocardial infarction (AMI) as a model setup to prove the claim. We determined the expression level of five hypoxia-induced circRNAs, including cZNF292, cAFF1, cDENND4C, cTHSD1, and cSRSF4, in the whole blood of coronary angiography positive AMI and negative non-AMI patients. Based on feature selection by using lasso with 10-fold cross validation, prediction model by logistic regression, and ROC curve analysis, we found that cZNF292 combined with clinical information (CM), including age, gender, body mass index, heart rate, and diastolic blood pressure, can predict AMI effectively. In a validation cohort, CM + cZNF292 can separate AMI and non-AMI patients, unstable angina and AMI patients, acute coronary syndromes (ACS), and non-ACS patients. RNA stability study demonstrated that cZNF292 was stable. Knockdown of cZNF292 in endothelial cells or cardiomyocytes showed anti-apoptosis effects in oxygen glucose deprivation/reoxygenation. Thus, we identify circulating cZNF292 as a potential biomarker for AMI and construct a prediction model "CM + cZNF292."
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