{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xu J"],"funding":["National Natural Science Foundation of China","Chinese Academy of Medical Science (CAMS) Innovation Fund for Medical Sciences"],"pagination":["237"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12183871"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<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 model"],"journal":["Cardiovascular diabetology"],"pubmed_title":["Inflammation-related 5-hydroxymethylation signatures as markers for clinical presentations of coronary artery disease."],"pmcid":["PMC12183871"],"funding_grant_id":["2016-I2M-1-009","82192902","82204399"],"pubmed_authors":["Yang J","He C","Li J","Zhang J","Chen L","Chen H","Zhang L","Li L","Xu J","Yuan J","Zhao X","Li X","Yang W","Yang Y","Wang T","Lu J","Tian T","Gao X","Lin J","Wu Y","Dong C"],"additional_accession":[]},"is_claimable":false,"name":"Inflammation-related 5-hydroxymethylation signatures as markers for clinical presentations of coronary artery disease.","description":"<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 model","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jun","modification":"2026-04-25T03:27:42.782Z","creation":"2026-04-25T03:22:11.762Z"},"accession":"S-EPMC12183871","cross_references":{"pubmed":["40551101"],"doi":["10.1186/s12933-025-02749-x"]}}