Yeting Li, Kai Ma, Chuanxin Zhao, Nannan Li, Shanshan Li, Man Zheng
{"title":"Diagnostic biomarkers for ST-segment elevation myocardial infarction using RNA methylation regulators","authors":"Yeting Li, Kai Ma, Chuanxin Zhao, Nannan Li, Shanshan Li, Man Zheng","doi":"10.1186/s43042-024-00532-3","DOIUrl":null,"url":null,"abstract":"Additional evidence has indicated a correlation between N6-methyladenosine (m6A) RNA methylation and cardiovascular disease. Nevertheless, the alterations in RNA methylation modification and the expression of numerous genes remains unclear. This study aimed to identify the role of m6A in ST-segment elevation myocardial infarction (STEMI). Two microarray datasets (GSE123342 and GSE59867) were downloaded from the GEO database. After merging the data and batch normalization, differentially expressed regulators were identified using the limma package. Subtyping consistency analysis was performed to group samples. The random forest algorithm and support vector machine were used to identify diagnostic biomarkers. Immune infiltration and inflammation levels among the subtypes were assessed using a single-sample gene set enrichment analysis. A total of 15 key differential m6A regulators (RBM15B, ELAVL1, ALKBH5, METTL16, ZC3H13, RBM15, YTHDC1, YTHDC2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, RBMX, FTO) were identified using the random forest classifier and were found to be highly correlated by PPI analysis. Two distinct RNA modification patterns (cluster A and B) were validated based on the expression levels of the 15 key m6A regulators. GO and KEGG annotations showed that immunity and inflammation pathways were enriched. Immune infiltration analysis revealed that cluster 2 had higher immune activation than cluster 1. Further analysis showed that cluster 2 had a higher inflammation level, with IL-4 and IL-33 showing differential expression (p < 0.05). A set of 15 m6A RNA methylation regulators could alter the STEMI microenvironment to improve risk stratification and clinical treatment.","PeriodicalId":39112,"journal":{"name":"Egyptian Journal of Medical Human Genetics","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Egyptian Journal of Medical Human Genetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s43042-024-00532-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Additional evidence has indicated a correlation between N6-methyladenosine (m6A) RNA methylation and cardiovascular disease. Nevertheless, the alterations in RNA methylation modification and the expression of numerous genes remains unclear. This study aimed to identify the role of m6A in ST-segment elevation myocardial infarction (STEMI). Two microarray datasets (GSE123342 and GSE59867) were downloaded from the GEO database. After merging the data and batch normalization, differentially expressed regulators were identified using the limma package. Subtyping consistency analysis was performed to group samples. The random forest algorithm and support vector machine were used to identify diagnostic biomarkers. Immune infiltration and inflammation levels among the subtypes were assessed using a single-sample gene set enrichment analysis. A total of 15 key differential m6A regulators (RBM15B, ELAVL1, ALKBH5, METTL16, ZC3H13, RBM15, YTHDC1, YTHDC2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, RBMX, FTO) were identified using the random forest classifier and were found to be highly correlated by PPI analysis. Two distinct RNA modification patterns (cluster A and B) were validated based on the expression levels of the 15 key m6A regulators. GO and KEGG annotations showed that immunity and inflammation pathways were enriched. Immune infiltration analysis revealed that cluster 2 had higher immune activation than cluster 1. Further analysis showed that cluster 2 had a higher inflammation level, with IL-4 and IL-33 showing differential expression (p < 0.05). A set of 15 m6A RNA methylation regulators could alter the STEMI microenvironment to improve risk stratification and clinical treatment.