ArticleClinical epigenetics2022
Identification of DNA methylation-regulated genes as potential biomarkers for coronary heart disease via machine learning in the Framingham Heart Study.
Article in Clinical epigenetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 28 citations in OpenAlex.
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Peripheral blood mononuclear cell DNA methylation signatures guide surgical decision-making in indeterminate pulmonary nodules.Communications medicine · 2026Article
- Early risk stratification of acute kidney injury severity in patients with acute myocardial infarction and diabetes using machine learning.Scientific reports · 2026Article
- Autophagy, telomerase, and endothelial dysfunction in COVID-19-induced cardiac injury: an evidence-graded genetic and epigenetic synthesis.Frontiers in cardiovascular medicine · 2026Review
- Twin pairs discordant for incident coronary artery disease reveal epigenetic and transcriptomic differences by gene region.Molecular genetics and genomics : MGG · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Epigenetic Mechanisms in Heart Diseases.Reviews in cardiovascular medicine · 2025Review
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- Transformative Potential of Induced Pluripotent Stem Cells in Congenital Heart Disease Research and Treatment.Children (Basel, Switzerland) · 2025Review
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Multi-Omic Insight Into the Molecular Networks in the Pathogenesis of Coronary Artery Disease.Journal of the American Heart Association · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Clinical promise and applications of epigenetic biomarkers.Clinical epigenetics · 2024Article
- Multi-omics association study of DNA methylation and gene expression levels and diagnoses of cardiovascular diseases in Danish Twins.Clinical epigenetics · 2024Article
- The Use of Precision Epigenetic Methods for the Diagnosis and Care of Stable Coronary Heart Disease Reduces Healthcare Costs.Advances in therapy · 2024Article
- Epigenomic insights into common human disease pathology.Cellular and molecular life sciences : CMLS · 2024Review
- Insights on E1-like enzyme ATG7: functional regulation and relationships with aging-related diseases.Communications biology · 2024Review
- The Reversion of the Epigenetic Signature of Coronary Heart Disease in Response to Smoking Cessation.Genes · 2023Article
- [Association betweenNan fang yi ke da xue xue bao = Journal of Southern Medical UniversityArticle
- DHCR24 in Tumor Diagnosis and Treatment: A Comprehensive Review.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA methylation-regulated genes have been demonstrated as the crucial participants in the occurrence of coronary heart disease (CHD). The machine learning based on DNA methylation-regulated genes has tremendous potential for mining non-invasive predictive biomarkers and exploring underlying new mechanisms of CHD.
resultsFirst, the 2085 age-gender-matched individuals in Framingham Heart Study (FHS) were randomly divided into training set and validation set. We then integrated methylome and transcriptome data of peripheral blood leukocytes (PBLs) from the training set to probe into the methylation and expression patterns of CHD-related genes. A total of five hub DNA methylation-regulated genes were identified in CHD through dimensionality reduction, including ATG7, BACH2, CDKN1B, DHCR24 and MPO. Subsequently, methylation and expression features of the hub DNA methylation-regulated genes were used to construct machine learning models for CHD prediction by LightGBM, XGBoost and Random Forest. The optimal model established by LightGBM exhibited favorable predictive capacity, whose AUC, sensitivity, and specificity were 0.834, 0.672, 0.864 in the validation set, respectively. Furthermore, the methylation and expression statuses of the hub genes were verified in monocytes using methylation microarray and transcriptome sequencing. The methylation statuses of ATG7, DHCR24 and MPO and the expression statuses of ATG7, BACH2 and DHCR24 in monocytes of our study population were consistent with those in PBLs from FHS.
conclusionsWe identified five DNA methylation-regulated genes based on a predictive model for CHD using machine learning, which may clue the new epigenetic mechanism for CHD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.