Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction-a bioinformatics analysis

Accurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related molecular biological mechanisms of AMI in terms of accurate diagnosis and prognosis. The platform data and probe data of the GSE66360 data set were downloaded. The differential analysis was conducted by combining the m6A-related gene expression. Thereafter, a diagnostic model was established using the random-forest method. The diagnostic accuracy of the diagnostic models was assessed by using the area under the receiver operating characteristic (ROC) curve (AUC). Next, the patients with AMI were clustered by unsupervised machine learning using the R software. Finally, an immune cell clustering analysis for each cluster was conducted to determine the correlations between m6A-related gene expression and the infiltration amount of the immune cells. The case and control groups were not matched in terms of demographics. The GSE6636 data set comprised 99 participants (49 patients with AMI and 50 without in control group). The differential analysis identified 10 m6A-related genes: 5 writers [Methyltransferase-like 3 ( ), Methyltransferase-like 14 ( ), Wilms tumor 1-associated protein ( ), Zinc Finger CCCH-Type Containing 13 ( ), and Casitas B-lineage proto-oncogene like 1 ( )], 4 readers [YT521-B homology domain-containing family 3 ( ), Fragile X mental retardation type 1 ( ), YT521-B homology-domain-containing protein 1 ( ), and insulin-like growth factor binding protein 3 ( )] and 1 eraser [fat mass and obesity associated ( ) gene]. The Mean Decrease Gini (MDG) values of these 10 genes were greater than 2. The , and were included in the model with a C index of 0.842. , and were highly expressed in Type A, and was highly expressed in Type B. A diagnostic model of AMI was established based on the genes of , , and . Additionally, 2 molecular subtypes were successfully identified from the above-mentioned gene. Our findings could provide a novel method for the accurate diagnosis of AMI.