AUTHOR=Guo Yiqun , Jiang Hua , Wang Jinlong , Li Ping , Zeng Xiaoquan , Zhang Tao , Feng Jianyi , Nie Ruqiong , Liu Yulong , Dong Xiaobian , Hu Qingsong 

TITLE=5mC modification patterns provide novel direction for early acute myocardial infarction detection and personalized therapy

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.1053697

DOI=10.3389/fcvm.2022.1053697

ISSN=2297-055X

ABSTRACT=<sec><title>Background</title><p>Most deaths from coronary artery disease (CAD) are due to acute myocardial infarction (AMI). There is an urgent need for early AMI detection, particularly in patients with stable CAD. 5-methylcytosine (5mC) regulatory genes have been demonstrated to involve in the progression and prognosis of cardiovascular diseases, while little research examined 5mC regulators in CAD to AMI progression.</p></sec><sec><title>Method</title><p>Two datasets (GSE59867 and GSE62646) were downloaded from Gene Expression Omnibus (GEO) database, and 21 m5C regulators were extracted from previous literature. Dysregulated 5mC regulators were screened out by “limma.” The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm were employed to identify hub 5mC regulators in CAD to AMI progression, and 43 clinical samples (Quantitative real-time PCR) were performed for expression validation. Then a logistic model was built to construct 5mC regulator signatures, and a series of bioinformatics algorithms were performed for model validation. Besides, 5mC-associated molecular clusters were studied <italic>via</italic> unsupervised clustering analysis, and correlation analysis between immunocyte and 5mC regulators in each cluster was conducted.</p></sec><sec><title>Results</title><p>Nine hub 5mC regulators were identified. A robust model was constructed, and its prominent classification accuracy was verified <italic>via</italic> ROC curve analysis (area under the curve [AUC] = 0.936 in the training cohort and AUC = 0.888 in the external validation cohort). Besides, the clinical effect of the model was validated by decision curve analysis. Then, 5mC modification clusters in AMI patients were identified, along with the immunocyte infiltration levels of each cluster. The correlation analysis found the strongest correlations were TET3—Mast cell in cluster-1 and TET3-MDSC in cluster-2.</p></sec><sec><title>Conclusion</title><p>Nine hub 5mC regulators (<italic>DNMT3B</italic>, <italic>MBD3</italic>, <italic>UHRF1</italic>, <italic>UHRF2</italic>, <italic>NTHL1</italic>, <italic>SMUG1</italic>, <italic>ZBTB33</italic>, <italic>TET1</italic>, and <italic>TET3</italic>) formed a diagnostic model, and concomitant results unraveled the critical impact of 5mC regulators, providing interesting epigenetics findings in AMI population vs. stable CAD.</p></sec>