Department of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Clin Rheumatol. 2018 Jan;37(1):13-23. doi: 10.1007/s10067-017-3639-0. Epub 2017 Jun 10.
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by progressive joint erosion. Tumor necrosis factor (TNF) antagonists are the most widely used biological disease-modifying anti-rheumatic drug in RA. However, there continue to be one third of RA patients who have poor or no response to TNF antagonists. Following consideration of the uncertainty of therapeutic effects and the high price of TNF antagonists, it is worthy to predict the treatment responses before anti-TNF therapy. According to the comparisons between the responders and non-responders to TNF antagonists by omic technologies, such as genomics, transcriptomics, proteomics, and metabolomics, rheumatologists are eager to find significant biomarkers to predict the effect of TNF antagonists in order to optimize the personalized treatment in RA.
类风湿关节炎（RA）是一种以进行性关节侵蚀为特征的系统性自身免疫性疾病。肿瘤坏死因子（TNF）拮抗剂是 RA 中应用最广泛的生物性疾病修正抗风湿药物。然而，仍有三分之一的 RA 患者对 TNF 拮抗剂反应不佳或无反应。鉴于治疗效果的不确定性和 TNF 拮抗剂的高昂价格，在进行抗 TNF 治疗之前预测治疗反应是值得的。根据基因组学、转录组学、蛋白质组学和代谢组学等组学技术对 TNF 拮抗剂应答者和无应答者的比较，风湿科医生渴望找到有意义的生物标志物来预测 TNF 拮抗剂的疗效，从而优化 RA 的个体化治疗。
