Identification of a biomarker panel for improvement of prostate cancer diagnosis by volatile metabolic profiling of urine

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Lima, AR (Author) Other; Pinto, J (Author) Other; Azevedo, AI (Author) Other; Barros Silva, D (Author) Other; Carmen Jeronimo (Author) ICBAS; henrique, rmf (Author) ICBAS; Maria de Lourdes Bastos (Author) FFUP; de Pinho, PG (Author) FCUP; Carvalho, M (Author) Other

Journal

Title: British Journal of Cancer

Vol. 121

Pages: 857-868

ISSN: 0007-0920

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 58 Citations

Scopus - 66 Citations

Other information

Authenticus ID: P-00R-88W

DOI: 10.1038/s41416-019-0585-4

Abstract (EN): BACKGROUND: The lack of sensitive and specific biomarkers for the early detection of prostate cancer (PCa) is a major hurdle to improve patient management. METHODS: A metabolomics approach based on GC-MS was used to investigate the performance of volatile organic compounds (VOCs) in general and, more specifically, volatile carbonyl compounds (VCCs) present in urine as potential markers for PCa detection. RESULTS: Results showed that PCa patients (n = 40) can be differentiated from cancer-free subjects (n = 42) based on their urinary volatile profile in both VOCs and VCCs models, unveiling significant differences in the levels of several metabolites. The models constructed were further validated using an external validation set (n = 18 PCa and n = 18 controls) to evaluate sensitivity, specificity and accuracy of the urinary volatile profile to discriminate PCa from controls. The VOCs model disclosed 78% sensitivity, 94% specificity and 86% accuracy, whereas the VCCs model achieved the same sensitivity, a specificity of 100% and an accuracy of 89%. Our findings unveil a panel of 6 volatile compounds significantly altered in PCa patients' urine samples that was able to identify PCa, with a sensitivity of 89%, specificity of 83%, and accuracy of 86%. CONCLUSIONS: It is disclosed a biomarker panel with potential to be used as a non-invasive diagnostic tool for PCa.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12