m6A regulator-mediated methylation modification patterns and immune microenvironment infiltration characterization in osteoarthritis

Abstract

Osteoarthritis (OA) is a common disease in orthopedics. RNA N6-methyladenosine (m6A) exerts an essential effect in a variety of biological processes in the eukaryotes. In this study, we determined the effect of m6A regulators in the OA along with performing the subtype classification. Differential analysis of OA and normal samples in the database of Gene Expression Omnibus identified 9 significantly differentially expressed m6A regulators. These regulators were monitored by a random forest algorithm so as to evaluate the risk of developing OA disease. On the basis of these 9 moderators, a nomogram was established. The results of decision curve analysis suggested that the patients could benefit from a nomogram model. The OA sample was classified as 2 m6A models through a consensus clustering algorithm in accordance with these 9 regulators. These 2 m6A patterns were then assessed with principal component analysis. We also determined the m6A scores for the 2 m6A patterns and their correlation with immune infiltration. The results indicated that type A had a higher m6A score than type B. Thus, we suggest that the m6A pattern may provide a new approach for diagnose and provide novel ideas for molecular targeted therapy of OA.

17β‐estradiol promotes the progression of temporomandibular joint osteoarthritis by regulating the FTO/IGF2BP1/m6A‐NLRC5 axis

“… 33 m6A methylation is the most prevalent RNA modification that regulates the development of multiple diseases, including the regulation of the immune microenvironment in OA. 34 , 35 m6A methylation is mediated by methyltransferase complex, demethylases, and methylated reading proteins, which act as “writers,” “erasers,” and “readers,” respectively. 36 Based on the demethylation effect of FTO, it is natural for us to think that FTO regulates the m6A methylation modification of genes.…”

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

“…Subsequently, we integrated the genes from the LASSO, SVM-REF, and MCODE modules to obtain three important genes. Finally, a diagnostic model was developed using five machine learning techniques, including logistic regression [ 33 ], Bayesian logistic regression [ 34 ], decision tree [ 35 ], random forest [ 36 ], and extreme gradient boosting [ 37 ], to evaluate the diagnostic value of these three genes in TB disease.…”