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

• Published in: British journal of cancer Vol. 121; no. 10; pp. 857 - 868
• Main Authors: Lima, Ana Rita, Pinto, Joana, Azevedo, Ana Isabel, Barros-Silva, Daniela, Jerónimo, Carmen, Henrique, Rui, de Lourdes Bastos, Maria, Guedes de Pinho, Paula, Carvalho, Márcia
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.11.2019; Nature Publishing Group
• Subjects: 631/67/2327; 692/53/2421; Accuracy; Aged; Biomarkers; Biomarkers, Tumor - metabolism; Biomarkers, Tumor - urine; Biomedical and Life Sciences; Biomedicine; Cancer Research; Carbonyl compounds; Drug Resistance; Early Detection of Cancer; Epidemiology; Gas Chromatography-Mass Spectrometry; Humans; Male; Medical diagnosis; Metabolites; Metabolomics; Metabolomics - methods; Middle Aged; Molecular Medicine; Oncology; Organic compounds; Prostate - metabolism; Prostate - pathology; Prostate cancer; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - metabolism; Prostatic Neoplasms - pathology; Prostatic Neoplasms - urine; Urine; VOCs; Volatile organic compounds; Volatile Organic Compounds - urine
• DOI: 10.1038/s41416-019-0585-4
• ISSN: 0007-0920; 1532-1827

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.