Abstract：
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation in the synovial lining of the joints. Key inflammatory cytokines such as interleukin-6 (IL-6), TNF-α, and others play a critical role in the activation of local synovial leukocytes and the induction of chronic inflammation. Tocilizumab (TCZ), a humanized anti-IL-6 receptor monoclonal antibody, has demonstrated significant clinical efficacy in treating RA patients. However, similar to other inflammatory cytokine blockers, such as TNF-alpha inhibitors, Interleukin-1 inhibitors, or CD20 inhibitors, some patients do not respond to treatment. To address this challenge, our study employed a high-precision proteomics approach to identify protein biomarkers capable of predicting clinical responses to Tocilizumab in RA patients. Through the use of data-independent acquisition (DIA) mass spectrometry, we analyzed serum samples from both TCZ responders and non-responders to discover potential biomarker candidates. These candidates were subsequently validated using individual serum samples from two independent cohorts: a training set (N = 70) and a test set (N = 18), allowing for the development of a robust multi-biomarker panel. The constructed multi-biomarker panel demonstrated an average discriminative power of 86 % between response and non-response groups, with a high area under the curve (AUC) value of 0.84. Additionally, the panel exhibited 100 % sensitivity and 60 % specificity. Collectively, our multi-biomarker panel holds promise as a diagnostic tool to predict non-responders to TCZ treatment in RA patients.
摘要：
类风湿性关节炎（RA）是一种慢性系统性自身免疫性疾病，其特征是关节滑膜衬里的炎症。关键的炎症细胞因子，如白细胞介素-6（IL-6），TNF-α,和其他人在局部滑膜白细胞的激活和慢性炎症的诱导中起关键作用。Tocilizumab（TCZ），人源化抗IL-6受体单克隆抗体，已证明在治疗RA患者中具有显著的临床疗效。然而,与其他炎性细胞因子阻断剂相似，如TNF-α抑制剂，白细胞介素-1抑制剂，或CD20抑制剂，有些患者对治疗没有反应。为了应对这一挑战，我们的研究采用高精度蛋白质组学方法来鉴定能够预测RA患者Tocilizumab临床疗效的蛋白质生物标志物.通过使用数据独立采集(DIA)质谱，我们分析了TCZ应答者和非应答者的血清样本,以发现潜在的候选生物标志物.随后使用来自两个独立队列的个体血清样品验证这些候选物:训练集(N=70)和测试集(N=18)。允许开发一个强大的多生物标志物小组。构建的多生物标志物组显示出反应组和无反应组之间的平均辨别能力为86％。曲线下面积（AUC）值为0.84。此外,该小组表现出100%的灵敏度和60%的特异性.总的来说，我们的多生物标志物组有望成为预测RA患者对TCZ治疗无反应者的诊断工具.
