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

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...

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Bibliographic Details
Published in:Advanced science 2024-09, Vol.11 (34), p.e2401919-n/a
Main Authors: Xu, Xiaoyu, Fang, Yuzheng, Wang, Qirui, Zhai, Shuanfeng, Liu, Wanshan, Liu, Wanwan, Wang, Ruimin, Deng, Qiuqiong, Zhang, Juxiang, Gu, Jingli, Huang, Yida, Liang, Dingyitai, Yang, Shouzhi, Chen, Yonghui, Zhang, Jin, Xue, Wei, Zheng, Junhua, Wang, Yuning, Qian, Kun, Zhai, Wei
Format: Article
Language:English
Subjects:
Adult
Aged
Automation
Biomarkers
Biomarkers, Tumor - urine
Biopsy
Carcinoma, Renal Cell - diagnosis
Carcinoma, Renal Cell - metabolism
Carcinoma, Renal Cell - urine
Classification
Early Detection of Cancer - methods
Female
Humans
Kidney cancer
Kidney Neoplasms - diagnosis
Kidney Neoplasms - metabolism
Kidney Neoplasms - urine
Machine learning
Male
Mass spectrometry
Medical prognosis
metabolic fingerprinting
Metabolism
Metabolites
Metastasis
Middle Aged
Nanoparticles
Prognosis
renal diagnosis
Reproducibility
Scientific imaging
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods
subtype classification
Tumors
Urine
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Summary: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 
ISSN:2198-3844
2198-3844
DOI:10.1002/advs.202401919