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Urinary Volatilomics Unveils a Candidate Biomarker Panel for Noninvasive Detection of Clear Cell Renal Cell Carcinoma

Title
Urinary Volatilomics Unveils a Candidate Biomarker Panel for Noninvasive Detection of Clear Cell Renal Cell Carcinoma
Type
Article in International Scientific Journal
Year
2021
Authors
Pinto, J
(Author)
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Amaro, F
(Author)
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Lima, AR
(Author)
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Carvalho Maia, C
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Maria de Lourdes Bastos
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Carvalho, M
(Author)
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Journal
Vol. 20
Pages: 3068-3077
ISSN: 1535-3893
Other information
Authenticus ID: P-00T-XHR
Abstract (EN): Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer usually associated with asymptomatic development and risk of systemic progression. Hence, reliable molecular biomarkers of ccRCC are needed to provide early and minimally invasive detection. In this study, urinary volatilome profiling of patients diagnosed with ccRCC (n = 75), and cancer-free controls (n = 75), was performed to investigate the presence of a volatile signature characteristic of ccRCC. Volatile organic compounds (VOCs) in general, and more specifically volatile carbonyl compounds (VCCs), present in urine were extracted by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS). Supervised multivariate models showed a good discriminatory power of ccRCC patients from controls in urine. Overall, 22 volatile metabolites were found significantly altered between the two groups, including aldehydes, ketones, aromatic hydrocarbons, and terpenoids. A candidate six-biomarker panel, comprising octanal, 3-methylbutanal, benzaldehyde, 2-furaldehyde, 4-heptanone, and p-cresol, depicted the best performance for ccRCC detection with 83% sensitivity, 79% specificity, and 81% accuracy. Moreover, the ccRCC urinary volatilome signature suggested dysregulation of energy metabolism and overexpression of enzymes associated with carcinogenesis. These findings provide the molecular basis for the fine-tuning of gas-sensing materials for application in the development of a bioelectronic sensor.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 10
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