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Updated: Jul 21, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Identification of novel biomarkers for lupus nephritis
Zhengyue Liao1, Liying He2, Jiaojiao Fu1
1College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China; Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Chengdu, China.
Abstract:
Lupus nephritis (LN) is an autoimmune disease that rapidly progresses as a secondary consequence of systemic lupus erythematosus (SLE) and has a very poor prognosis. Therefore, this study aimed to identify characteristics of immune cell infiltration and investigate potential therapeutic targets using bioinformatics methods and the Murphy Roths Large/lymphoproliferation (MRL/lpr) mouse model. In this study, a total of 2,810 differentially expressed genes (DEGs) were identified, which were primarily enriched in inflammatory and immune regulation pathways. From these DEGs, 226 immune-related genes (IRGs) were also identified. The single-sample gene set enrichment analysis (ssGSEA) revealed that patients with LN had increased infiltration of effector memory CD4+ T cells, effector memory CD8+ T cells, gamma delta T cells, myeloid-derived suppressor cells (MDSC), follicular helper T cells, Th1 cells, and Th2 cells, and this was closely correlated with the DEG-IRGs. Furthermore, the potential therapeutic biomarkers, CD244, S100 calcium binding protein P (S100P), and vascular endothelial growth factor C (VEGFC), were identified by Random Forest Approach (RFA), which were validated in LN mice. These findings provide new evidence and insights for further research on diagnosis and treatment of LN by identifying critical genes and their associations with immune infiltration.
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