A serum biomarker panel and miniarray detection system for tracking disease activity and flare risk in lupus nephritis

作者：Chenling Tang, Gongjun Tan, Aygun Teymur, Jiechang Guo, Arturo Haces-Garcia, Weihang Zhu, Richard D. Williams, Jing Ning, Ramesh Saxena, Tianfu Wu · 发表于：Frontiers in Immunology · 年份：2025 · DOI：10.3389/fimmu.2025.1541907 · 被引用次数：2 · 研究领域：Systemic Lupus Erythematosus Research、Advanced Biosensing Techniques and Applications、Single-cell and spatial transcriptomics

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 h...