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OBJECTIVES: To investigate the expression and function of WNT16, a member of the WNT family protein, in the context of systemic lupus erythematosus (SLE). METHODS: WNT16 expression was assessed in peripheral blood mononuclear cells (PBMCs) from 35 SLE patients and 25 healthy individuals using quantitative polymerase chain reaction. Additionally, serum WNT16 protein levels were quantified via enzyme-linked immunosorbent assay in 162 SLE patients, 96 healthy controls (HC), and disease controls comprised 154 individuals with rheumatoid arthritis (RA) and Sjögren's syndrome (SS). We investigated the associations between WNT16 protein levels and clinical manifestations, laboratory indices, and disease activity in SLE patients. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic potential of serum WNT16 for SLE. Furthermore, we performed a knockdown assay on Jeko-1 cells and assessed cell proliferation and apoptosis using Cell Counting Kit-8 and flow cytometry. RESULTS: WNT16 mRNA in SLE patients' PBMCs were significantly lower than those in HC. Furthermore, serum WNT16 in SLE patients were markedly reduced compared to HC, RA, and SS cohorts. ROC curve analysis indicated that plasma WNT16 levels could serve as a potential biomarker for SLE identification (AUC=0.809, SLE vs. HC; AUC=0.760, SLE vs. RA; AUC=0.710, SLE vs. SS). Notably, a weak positive correlation was observed between WNT16 protein and both alkaline phosphatase and lymphocyte percentages. Conversely, a weak negative correlation existed between WNT16 and low-density lipoprotein, neutrophil percentage, and the incidence of pleurisy and disease activity. Additionally, our study confirmed that WNT16 knockdown impairs cell proliferation and enhances apoptosis. CONCLUSIONS: Serum WNT16 levels effectively differentiate SLE patients from healthy controls and individuals with other autoimmune disorders. WNT16 serves as a potential biomarker with high sensitivity. The diminished expression of WNT16 in SLE may have a significant role in its pathogenesis through the regulation of cell proliferation and apoptosis.
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Objective: This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe. Methods: This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers. Results: Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN. Conclusion: This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.