RNA methylation patterns, immune characteristics, and autophagy-related mechanisms mediated by N6-methyladenosine (m6A) regulatory factors in venous thromboembolism.

Recent studies have found a link between deep vein thrombosis and inflammatory reactions. N6-methyladenosine (m6A), a crucial element in immunological regulation, is believed to contribute to the pathophysiology of venous thromboembolism (VTE). However, how the m6A-modified immune microenvironment is involved in VTE remains unclear. In the present study, we identified a relationship between VTE and the expression of several m6A regulatory elements by analyzing peripheral blood samples from 177 patients with VTE and 88 healthy controls from public GEO databases GSE19151 and GSE48000. We used machine learning to identify essential genes and constructed a diagnostic model for VTE using multivariate logistic regression. Unsupervised cluster analysis revealed a marked difference between m6A modification patterns in terms of immune cell infiltration, inflammatory reactivity, and autophagy. We identified two m6A-related autophagy genes (i.e., CHMP2B and SIRT1) and the crucial m6A regulator YTHDF3 using bioinformatics. We also examined two potential mechanisms through which YTHDF3 may affect VTE. m6A modification, immunity, and autophagy are closely linked in VTE, offering novel mechanistic and therapeutic insights.

Integrative Analysis of N6-methyladenosine RNA modifications related genes and their Influences on Immunoreaction or fibrosis in myocardial infarction.

Increasing studies have shown that N6-methyladenosine (m6A) modification plays an important role in cardiovascular diseases. In this study, we systematically investigated the regulatory mode of m6A genes in myocardial infarction (MI) by combining bioinformatics analysis of clinical samples with animal experiments. We utilized gene expression data of clinical samples from public databases to examine the expression of m6A genes in heart tissues and found a large difference between the healthy control group and MI group. Subsequently, we established an MI diagnosis model based on the differentially expressed m6A genes using the random forest method. Next, unsupervised clustering method was used to classify all MI samples into two clusters, and the differences in immune infiltration and gene expression between different clusters were compared. We found LRPPRC to be the predominant gene in m6A clustering, and it was negatively correlated with immunoreaction. Through GO enrichment analysis, we found that most differentially expressed genes between the two clusters were profibrotic. By means of WGCNA, we inferred that GJA4 might be a core molecule in the m6A regulatory network of MI. This study demonstrates that m6A regulators probably affects the immune-inflammatory response and fibrosis to regulate the process of MI, which provides a potential therapeutic target.