Nephrology Hospital, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China.
Institute of Nephrology, Zhengzhou University, Henan 450052, China.
J Immunol Res. 2022 Aug 4;2022:1830431. doi: 10.1155/2022/1830431. eCollection 2022.
Systemic lupus erythematosus (SLE) has become increasingly common in the clinic and requires complicated evidence of both clinical manifestations and laboratory examinations. Additionally, the assessment and monitoring of lupus disease activity are challenging. We hope to find efficient biomarkers and establish diagnostic models of SLE.
We detected and quantified 40 proteins using a quantitative protein array of 76 SLE patients and 21 healthy controls, and differentially expressed proteins were screened out by volcano plot. Logistic regression analysis was used to recognize biomarkers that could be enrolled in the disease diagnosis model and disease activity diagnosis model, and a receiver operating characteristic (ROC) curve was drawn to evaluate the efficiency of the model. A nomogram was depicted for convenient and visualized application of our models in the clinic. Decision curves and clinical impact curves were also plotted to validate our models.
The protein levels of TNF RII, BLC, TNF RI, MIP-1b, eotaxin, MIG, MCSF, IL-8, MCP-1, and IL-10 showed significant differences between patients with SLE and healthy controls. TNF RII and MIP-1b were included in the SLE diagnosis model with logistic regression analysis, and the value of the area under the ROC curve (AUC) was 0.914 (95% confidence interval (CI), 0.859-0.969). TNF RII, BLC, and MIP-1b were enrolled in the disease activity diagnosis model, and the AUC value was 0.823 (95% CI 0.729-0.916). Both of the models that we established showed high efficiency. Additionally, the three protein biomarkers contained in the disease activity distinguish model provided additional benefit to conventional biomarkers in predicting active lupus.
The disease diagnosis model and disease activity diagnosis model that we developed based on protein array chip results showed high efficiency in differentiating patients with SLE from healthy controls and recognizing SLE patients with high disease activity, and they have also been validated. This implied that they might greatly benefit clinical decisions and the treatment of SLE.
系统性红斑狼疮（SLE）在临床上越来越常见，需要结合临床表现和实验室检查的复杂证据。此外，狼疮疾病活动的评估和监测具有挑战性。我们希望找到有效的生物标志物并建立 SLE 的诊断模型。
我们使用 76 例 SLE 患者和 21 例健康对照者的定量蛋白质芯片检测和定量了 40 种蛋白质，通过火山图筛选出差异表达蛋白。使用逻辑回归分析识别可纳入疾病诊断模型和疾病活动诊断模型的生物标志物，并绘制受试者工作特征（ROC）曲线评估模型的效率。为了方便和直观地将我们的模型应用于临床，描绘了一个列线图。还绘制了决策曲线和临床影响曲线来验证我们的模型。
SLE 患者与健康对照者的 TNF RII、BLC、TNF RI、MIP-1b、嗜酸性粒细胞趋化因子、MIG、MCSF、IL-8、MCP-1 和 IL-10 的蛋白水平差异有统计学意义。通过逻辑回归分析，TNF RII 和 MIP-1b 被纳入 SLE 诊断模型，ROC 曲线下面积（AUC）值为 0.914（95%置信区间（CI），0.859-0.969）。TNF RII、BLC 和 MIP-1b 被纳入疾病活动诊断模型，AUC 值为 0.823（95%CI 0.729-0.916）。我们建立的两个模型均显示出高效性。此外，疾病活动区分模型中包含的三种蛋白质生物标志物在预测活动性狼疮方面为传统生物标志物提供了额外的益处。
我们基于蛋白质芯片结果建立的疾病诊断模型和疾病活动诊断模型在区分 SLE 患者和健康对照者以及识别 SLE 患者疾病活动度高方面具有高效性，并已得到验证。这表明它们可能极大地有益于临床决策和 SLE 的治疗。
