Development and Validation of a Novel Plasma Metabolomic Signature for the Detection of Renal Cell Carcinoma
Abstract
BACKGROUND AND OBJECTIVE
Early diagnosis is critical for improving survival in renal cell carcinoma (RCC); yet, effective laboratory tests remain lacking. We aimed to characterise metabolic reprogramming in RCC and develop an artificial intelligence (AI)-enabled plasma metabolic model for RCC detection.
METHODS
In this multicentre diagnostic model development and validation study, plasma samples from RCC patients and healthy controls (HCs) were collected across five hospitals between December 2019 and October 2023. Eligible patients had pathologically confirmed RCC without prior treatment; HCs were recruited from routine physical examination. Participants with a history of malignancy were excluded. Untargeted plasma metabolomics was conducted to identify candidate metabolites via a support vector machine, further confirmed by a high-resolution targeted metabolic analysis. An AI-aided diagnostic model, Renal Cell Carcinoma Artificial Intelligence Detector (RCAID), was developed using selected metabolites and validated in six independent validation cohorts. Multiomic analyses were performed to elucidate the underlying metabolic mechanisms.
KEY FINDINGS AND LIMITATIONS
The study enrolled 1680 participants, comprising 920 RCC patients and 760 HCs. Among RCC cases, 744 (81%) had clear cell RCC and 633 (69%) had stage I disease. Seven key plasma metabolites, including 2-hydroxyphenylacetic acid, azelaic acid, N,N-dimethylglycine, N-acetyl-L-aspartic acid,…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1,680 participants or records.
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