Table_1_Predictive value of drug efficacy by M6A modification patterns in rheumatoid arthritis patients.xlsx

BackgroundRheumatoid arthritis is a highly heterogeneous autoimmune disease characterized by unpredictable disease flares and significant differences in therapeutic response to available treatments. One possible reason for poor efficacy is that it cannot be treated accurately due to no optimal stratification for RA patients.ObjectiveThis study aims to construct an RA classification model by m6A characters and further predict response to medication.MethodsTwenty m6A regulators were used to construct a random forest diagnosis model, and RNA-seq analysis was employed for external validation. The RNA modification patterns mediated by 20 m6A regulators were systematically evaluated in 1191 RA samples and explored different molecular clusters associated with other immune microenvironment characteristics and biological pathways. Then, we established an m6A score model to quantify the m6A modification patterns. The model was applied to patients at baseline to test the association between m6Ascore and infliximab responsiveness.ResultsThe m6A diagnosis model showed good discriminatory ability in distinguishing RA. Patients with RA were classified into three clusters with distinct molecular and cellular signatures. Cluster A displayed strongly activated inflammatory cells and pathways. Specific innate lymphocytes occupied cluster B. Cluster C was mainly enriched in prominent adaptive lymphocytes and NK-mediated cytotoxicity signatures with the highest m6A score. Patients with a low m6Ascore exhibited significantly infliximab therapeutic benefits compared with those with a high m6Ascore (p< 0.05).ConclusionOur study is the first to provide a comprehensive analysis of m6A modifications in RA, which provides an innovative patient stratification framework and potentially enables improved therapeutic decisions.

背景：类风湿性关节炎是一种高度异质性的自身免疫性疾病，其特征为疾病发作的不确定性以及对现有治疗方案的显著差异。疗效不佳的可能原因之一在于缺乏针对类风湿关节炎患者的最佳分层策略。目标：本研究旨在通过m6A特征构建类风湿关节炎分类模型，并进一步预测对药物的反应。方法：利用二十种m6A调节因子构建随机森林诊断模型，并采用RNA测序分析进行外部验证。在1191个类风湿关节炎样本中系统性地评估了由二十种m6A调节因子介导的RNA修饰模式，并探究了与其他免疫微环境和生物通路相关的不同分子簇。随后，我们建立了一个m6A评分模型以量化m6A修饰模式。该模型应用于基线患者，以测试m6A评分与依那西普反应性之间的关联。结果：m6A诊断模型在区分类风湿关节炎方面显示出良好的区分能力。类风湿关节炎患者被分为三个具有不同分子和细胞特征的簇。簇A表现出强烈激活的炎症细胞和通路。簇B主要由特定的先天淋巴细胞占据。簇C主要富集了显著的适应性淋巴细胞和NK介导的细胞毒性特征，具有最高的m6A评分。与m6A评分高的患者相比，m6A评分低的患者显示出显著的依那西普治疗益处（p< 0.05）。结论：本研究首次对类风湿关节炎中的m6A修饰进行了全面分析，提供了创新的病人分层框架，并有可能改善治疗决策。