ArticleWorld journal of urology2025
Development and validation of a plasma-urine metabolism diagnostic model for renal cell carcinoma using machine learning.
Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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Authors and funding
9 authors.
Funding
Abstract
backgroundRenal cell carcinoma (RCC), which accounts for 70-90% of kidney malignancies, remains difficult to diagnose early due to its asymptomatic onset and the lack of reliable biomarkers. This study aimed to develop a robust diagnostic model by integrating plasma and urine metabolomics profiling.
methodsA total of 482 plasma and 434 urine samples from RCC patients, benign renal disease cases, other urological cancers, and healthy controls were analyzed using multi-platform mass spectrometry. Participants were assigned to a discovery or validation cohort to identify RCC-specific metabolites and construct diagnostic models.
resultsTwenty-six plasma and twelve urine metabolites were selected to build individual models. The integrated plasma-urine model achieved superior diagnostic accuracy (AUC = 0.88) compared with plasma (AUC = 0.86) and urine (AUC = 0.78) models in the validation cohort, with notably improved sensitivity for early-stage RCC. In asymptomatic screening populations, it performed excellently (AUC = 0.94) and maintained high specificity, yielding significantly lower scores for other cancer types (p < 0.01). Pathway analysis identified glycine, serine, and threonine metabolism as the key dysregulated pathway shared across plasma and urine, suggesting a potential therapeutic target.
conclusionThis study demonstrates that integrating plasma and urine metabolomics with machine learning yields a robust, non-invasive diagnostic model for renal cell carcinoma. The combined plasma-urine panel outperformed single-fluid models, achieving high accuracy and specificity, and maintained stable performance across tumor stages, grades, and clinical subgroups. The identified metabolic signatures, particularly alterations in glycine-serine-threonine metabolism pathway, provide novel insights into RCC metabolic reprogramming. These findings support the model's potential for early detection and clinical application, while also offering a basis for future therapeutic exploration.
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Registered trials
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