Advanced Science (Sep 2024)

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis

  • Xiaoyu Xu,
  • Yuzheng Fang,
  • Qirui Wang,
  • Shuanfeng Zhai,
  • Wanshan Liu,
  • Wanwan Liu,
  • Ruimin Wang,
  • Qiuqiong Deng,
  • Juxiang Zhang,
  • Jingli Gu,
  • Yida Huang,
  • Dingyitai Liang,
  • Shouzhi Yang,
  • Yonghui Chen,
  • Jin Zhang,
  • Wei Xue,
  • Junhua Zheng,
  • Yuning Wang,
  • Kun Qian,
  • Wei Zhai

DOI
https://doi.org/10.1002/advs.202401919
Journal volume & issue
Vol. 11, no. 34
pp. n/a – n/a

Abstract

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Abstract Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle‐enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884–0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821–0.915), and 0.925–0.932 for classifying subtypes of RCC (95% CI, 0.821–0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.

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