Novel Biomarkers for the Precisive Diagnosis and Activity Classification of Takayasu Arteritis
Novel Biomarkers for the Precisive Diagnosis and Activity Classification of Takayasu Arteritis
复制标题
用于大动脉炎精确诊断和活动性分类的新型生物标志物
DOI：
10.1161/circgen.117.002080
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发表时间：
2019-01-01
影响因子：
7.4
通讯作者：
Cai, Jun
中科院分区：
文献类型：
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作者：
Cui, Xiao;Qin, Fang;Cai, Jun
背景：建立诊断和确定多发性大动脉炎（TA）的疾病活动仍然具有挑战性。新的生物标志物可能有助于解决这个问题。研究方法：在筛选阶段，通过使用大规模蛋白质阵列检测来自90名受试者的样本（TA活性，29; TA非活性31;和对照，30）。在验证阶段，通过使用酶联免疫吸附试验（ELISA），分别在独立队列中测量用于TA诊断和活动分类的潜在生物标志物。结果如下：在筛选阶段，鉴定出18种细胞因子在TA患者和对照组之间显著差异富集，另外15种细胞因子在TA患者的活动状态和非活动状态之间显著差异富集（校正P<0.05）。在验证阶段，TIMP（金属蛋白酶组织抑制剂）-1被确定为TA诊断的特异性生物标志物，其临界值为221.86 g/L，特异性为89.58%，阳性预测值为0.92。同时，我们发现使用单一生物标志物进行TA活性分类是不可靠的。考虑到这一点，我们进一步建立了一个基于多种细胞因子的logistic回归模型，包括CA（癌抗原）125，FLRG卵泡抑素相关蛋白（胰岛素样生长因子结合蛋白）-2，CA 15 -3，GROa（生长调节蛋白），LYVE淋巴管内皮透明质酸受体（UL 16-结合蛋白）-2和CD（分化簇）99，曲线下面积达到0.909，用于区分TA活性状态。结论：TIMP-1可作为TA诊断的特异性生物标志物，其临界值为221.86g/L。此外，我们提供了一个基于8个生物标志物的逻辑回归模型，用于TA的精确活性分类，曲线下面积为0.909。
Background: Establishing the diagnosis and determining disease activity of Takayasu arteritis (TA) remains challenging. Novel biomarkers might help to solve this problem. Methods: In the screening phase, by using large-scale protein arrays detecting samples from 90 subjects (TA active, 29; TA inactive 31; and controls, 30). In the validation phase, by using enzyme-linked immunosorbent assay (ELISA), potential biomarkers for TA diagnosis, and activity classification were measured in independent cohorts, respectively. Results: In the screening phase, 18 cytokines significantly differentially enriched between TA patients and controls and another 15 cytokines significantly differentially enriched between TA patient in active and inactive status were identified (adjusted P<0.05). In the validation phase, TIMP (tissue inhibitor of metalloproteinases)-1 was identified as a specific biomarker for TA diagnosis that a cutoff value of 221.86 &mgr;g/L could provide a specificity of 89.58% and a positive predictive value of 0.92. Meanwhile, we found it unreliable to use a single biomarker for TA activity classification. Considering this, we further built a logistic regression model based on multiple cytokines, including CA (cancer antigen) 125, FLRG (follistatin-related protein), IGFBP (insulin-like growth factor-binding protein)-2, CA15-3, GROa (growth-regulated alpha protein), LYVE (lymphatic vessel endothelial hyaluronic acid receptor)-1, ULBP (UL16-binding protein)-2, and CD (cluster of differentiation) 99, with an area under the curve reaching 0.909 for discriminating TA activity status. Conclusions: This study suggested TIMP-1 as a specific biomarker for TA diagnosis with a cutoff value of 221.86 &mgr;g/L. Furthermore, we provided a logistic regression model based on 8 biomarkers for the precisive activity classification of TA with an area under the curve of 0.909.