一种用于追踪狼疮性肾炎疾病活动和复发风险的血清生物标志物面板及微阵列检测系统。
A serum biomarker panel and miniarray detection system for tracking disease activity and flare risk in lupus nephritis.
作者信息
Tang Chenling, Tan Gongjun, Teymur Aygun, Guo Jiechang, Haces-Garcia Arturo, Zhu Weihang, Williams Richard, Ning Jing, Saxena Ramesh, Wu Tianfu
机构信息
Department of Biomedical Engineering, University of Houston, Houston, TX, United States.
Department of Mechanical Engineering Technology, University of Houston, Houston, TX, United States.
出版信息
Front Immunol. 2025 May 1;16:1541907. doi: 10.3389/fimmu.2025.1541907. eCollection 2025.
INTRODUCTION
Lupus nephritis (LN) leads to end stage renal disease (ESRD), and early diagnosis and disease monitoring of LN could significantly reduce the risk. however, there is not such a system clinically. In this study we aim to develop a biomarker-panel based point-of-care system for LN.
METHODS
Immunoassay screening combined with genomic expression databases and machine learning techniques was used to identify a biomarker panel of LN. A quantitative biomarker-panel mini-array (BPMA) system was developed and the sensitivity, specificity, reproducibility, and stability of the were examined. The performance of BPMA in disease monitoring was validated with machine models using a larger cohort of LN. The BPMA was also used to determine LN flare using a machine-learning generated flare score (F-Score).
RESULTS
Among 32 promising LN serum biomarkers, VSIG4, TNFRSF1b, VCAM1, ALCAM, OPN, and IgG anti-dsDNA antibody were selected to constitute an LN biomarker Panel, which exhibited excellent discriminative value in distinguishing LN from healthy controls (AUC = 1.0) and active LN from inactive LN (AUC = 0.92), respectively. Also, the 6-biomarker panel exhibited a strong correlation with key clinical parameters of LN. A multiplexed immunoarray was constructed with the 6-biomarker panel (named BPMA-S6 thereafter). An LN-specific 8-point standard curve was generated for each protein biomarker. Cross-reaction between these biomarkers was minimal (< 1%). BPMA-S6 test results were highly correlated with those from ELISA (Spearman's correlation: fluorescent detection, rs = 0.95; colorimetric detection, rs = 0.91). The discriminative value of BPMA-S6 for LN was further validated using an independent cohort (AUC = 0.94). Using a longitudinal cohort of LN, the derived F-Score exhibited superior discriminative value in the training dataset (AUC = 0.92) and testing dataset (AUC=0.82) to distinguish flare vs remission.
CONCLUSION
BPMA-S6 may represent a promising point-of-care test (POCT) for the diagnosis, disease monitoring, and assessment of LN flare.
引言
狼疮性肾炎（LN）可导致终末期肾病（ESRD），对LN进行早期诊断和疾病监测可显著降低风险。然而，临床上尚无此类系统。在本研究中，我们旨在开发一种基于生物标志物组的LN即时检测系统。
方法
采用免疫分析筛选结合基因组表达数据库和机器学习技术来识别LN的生物标志物组。开发了一种定量生物标志物组微阵列（BPMA）系统，并检测了其灵敏度、特异性、重复性和稳定性。使用更大规模的LN队列，通过机器学习模型验证了BPMA在疾病监测中的性能。BPMA还用于通过机器学习生成的发作评分（F-Score）来确定LN发作。
结果
在32种有前景的LN血清生物标志物中，选择VSIG4、TNFRSF1b、VCAM1、ALCAM、OPN和IgG抗双链DNA抗体组成LN生物标志物组，其在区分LN与健康对照（AUC = 1.0）以及活动期LN与非活动期LN（AUC = 0.92）方面分别表现出优异的判别价值。此外，6种生物标志物组与LN的关键临床参数具有很强的相关性。用6种生物标志物组构建了一种多重免疫阵列（此后称为BPMA-S6）。为每种蛋白质生物标志物生成了LN特异性的8点标准曲线。这些生物标志物之间的交叉反应极小（<1%）。BPMA-S6检测结果与ELISA结果高度相关（斯皮尔曼相关性：荧光检测，rs = 0.95；比色检测，rs = 0.91）。使用独立队列进一步验证了BPMA-S6对LN的判别价值（AUC = 0.94）。使用LN纵向队列，得出的F-Score在训练数据集（AUC = 0.92）和测试数据集（AUC = 0.82）中表现出卓越的判别价值，以区分发作与缓解。
结论
BPMA-S6可能是一种用于LN诊断、疾病监测和发作评估的有前景的即时检测（POCT）方法。
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