ArticleFrontiers in immunology2025
A serum biomarker panel and miniarray detection system for tracking disease activity and flare risk in lupus nephritis.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An explainable hybrid SMPR-Net-XGBoost framework for automated lupus nephritis detection using medical image analysis.International urology and nephrology · 2026Article
- Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology.Frontiers in medicine · 2025Review
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Authors and funding
10 authors.
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Abstract
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.
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