AUTHOR=Shi Xin , Cao Yaochen , Zhang Xiaobin , Gu Chang , Liang Feng , Xue Jieyuan , Ni Han-Wen , Wang Zi , Li Yi , Wang Xia , Cai Zhaohua , Hocher Berthold , Shen Ling-Hong , He Ben TITLE=Comprehensive Analysis of N6-Methyladenosine RNA Methylation Regulators Expression Identify Distinct Molecular Subtypes of Myocardial Infarction JOURNAL=Frontiers in Cell and Developmental Biology VOLUME=Volume 9 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.756483 DOI=10.3389/fcell.2021.756483 ISSN=2296-634X ABSTRACT=Background: Myocardial infarction (MI) is one of the leading threats to human health. N6-methyladenosine (m6A) modification, as a pivotal regulator of mRNA stability, protein expression, and cellular processes, exhibits important roles in the development of cardiac remodeling and cardiomyocyte contractile function. Methods: The expression levels of m6A regulators were analyzed using GSE5406 database. We analyzed GWAS data and single cell sequencing data to confirm the functional importance of m6A regulators in MI. Three molecular subtypes with different clinical characteristics were established to tailor treatment strategies for patients with MI. We applied pathway analysis and Differentially Expressed Gene (DEG) analysis to study the changes of gene expression and identified 4 common DEGs. Furthermore, we constructed the PPI network and confirmed several hub genes in 3 clusters of MI. To lucubrate the potential functions, we performed Clue-GO analysis of these hub networks Results: In this study, we identified the levels of FTO, YTHDF3, ZC3H13, WTAP were dramatically differently expressed in MI tissues compared to controls. M6A regulators were related to regulate Glucose measurement, elevated blood glucose level. Furthermore, GWAS data analysis showed WTAP SNP was significantly related to progression of MI. In addition, single-cell sequencing found that WTAP is widely expressed in the heart tissues. Moreover, we conducted Consensus Clustering for MI in view of the dysregulated m6A regulators expression in MI. According to the expression levels, we found MI patients could be clustered into 3 subtypes. Pathway analysis showed the DEGs among different clusters in MI were assigned to HIF-1, IL-17, MAPK, PI3K-Akt signaling pathways, etc. The module analysis detected several genes, including BAG2, BAG3, MMP2, etc. We also found MI related network was significantly related to positive and negative regulation of angiogenesis, and response to heat. The hub networks in MI clusters were significantly related to Antigen processing and Ubiquitin mediated proteolysis, RNA splicing and stability, indicating that these processes may contribute to the development of MI. Conclusion: Collectively, our study could provide more information for understanding the roles of m6A in MI, which may provide a novel insight into identify biomarkers for MI treatment and diagnosis.