biostudies-literatureXu JNational Natural Science Foundation of ChinaChinese Academy of Medical Science (CAMS) Innovation Fund for Medical Sciences237https://www.ebi.ac.uk/biostudies/studies/S-EPMC12183871biostudies-literatureUnknown24(1)<h4>Background</h4>Coronary angiography remains the gold standard for diagnosing coronary artery disease (CAD), but its invasive nature limits its applicability for widespread screening. Identifying non-invasive molecular markers could improve CAD classification and risk assessment.<h4>Methods</h4>We employed 5hmC-Seal technology to generate genome-wide 5-hydroxymethylcytosine (5hmC) profiles from plasma cell-free DNA (cfDNA) in 724 CAD patients, stratified into stable CAD (sCAD), non-ST-elevation myocardial infarction (NSTEMI), and ST-elevation myocardial infarction (STEMI) groups. Using machine learning algorithms, we identified inflammation-related 5hmC modifications associated with disease severity and constructed a classification model based on key hydroxymethylated markers. The modelCardiovascular diabetologyInflammation-related 5-hydroxymethylation signatures as markers for clinical presentations of coronary artery disease.PMC121838712016-I2M-1-0098219290282204399Yang JHe CLi JZhang JChen LChen HZhang LLi LXu JYuan JZhao XLi XYang WYang YWang TLu JTian TGao XLin JWu YDong CfalseInflammation-related 5-hydroxymethylation signatures as markers for clinical presentations of coronary artery disease.<h4>Background</h4>Coronary angiography remains the gold standard for diagnosing coronary artery disease (CAD), but its invasive nature limits its applicability for widespread screening. Identifying non-invasive molecular markers could improve CAD classification and risk assessment.<h4>Methods</h4>We employed 5hmC-Seal technology to generate genome-wide 5-hydroxymethylcytosine (5hmC) profiles from plasma cell-free DNA (cfDNA) in 724 CAD patients, stratified into stable CAD (sCAD), non-ST-elevation myocardial infarction (NSTEMI), and ST-elevation myocardial infarction (STEMI) groups. Using machine learning algorithms, we identified inflammation-related 5hmC modifications associated with disease severity and constructed a classification model based on key hydroxymethylated markers. The model2025-01-01T00:00:00Z2025 Jun2026-04-25T03:27:42.782Z2026-04-25T03:22:11.762ZS-EPMC121838714055110110.1186/s12933-025-02749-x