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

2019Journal article Open access
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dc.contributor.authorLima, Ana Rita
dc.contributor.authorPinto, Joana
dc.contributor.authorAzevedo, Ana Isabel
dc.contributor.authorBarros-Silva, Daniela
dc.contributor.authorJerónimo, Carmen
dc.contributor.authorHenrique, Rui
dc.contributor.authorBastos, Maria de Lourdes
dc.contributor.authorGuedes de Pinho, Paula
dc.contributor.authorCarvalho, Márcia
dc.date.accessioned2021-07-02T13:42:49Z
dc.date.available2021-07-02T13:42:49Z
dc.date.issued2019
dc.description.abstractBackground: 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1038/s41416-019-0585-4pt_PT
dc.identifier.eissn1532-1827
dc.identifier.issn0007-0920
dc.identifier.urihttp://hdl.handle.net/10284/10029
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherNature Publishing Grouppt_PT
dc.relationThis work received financial support from the European Union (FEDER funds POCI/01/0145/FEDER/007728) and National Funds (FCT/MEC, Fundação para a Ciência e a Tecnologia and Ministério da Educação e Ciência) under the Partnership Agreement PT2020 UID/MULTI/04378/2013. The study is a result of the project NORTE-01–0145-FEDER-000024, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement (DESignBIOtecHealth-New Technologies for three Health Challenges of Modern Societies: Diabetes, Drug Abuse and Kidney Diseases), through the European Regional Development Fund (ERDF). A.R.L. was the recipient of a PhD fellowship from FCT (SFRH/BD/123012/2016) and M.C. acknowledges financial support from FCT through the UID/MULTI/04546/2019 project.pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectProstatept_PT
dc.subjectMetabolomicspt_PT
dc.subjectVolatile organic compoundspt_PT
dc.subjectEarly detection of cancerpt_PT
dc.titleIdentification of a biomarker panel for improvement of prostate cancer diagnosis by volatile metabolic profiling of urinept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage868pt_PT
oaire.citation.issue10pt_PT
oaire.citation.startPage857pt_PT
oaire.citation.titleBritish Journal of Cancerpt_PT
oaire.citation.volume121pt_PT
person.familyNameCarvalho
person.givenNameMarcia
person.identifier2017111
person.identifier.ciencia-id8B10-171E-E63E
person.identifier.orcid0000-0001-9884-4751
person.identifier.ridD-5999-2013
person.identifier.scopus-author-id7201413997
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication3837b828-ba57-47f7-a811-cce65e4922c6
relation.isAuthorOfPublication.latestForDiscovery3837b828-ba57-47f7-a811-cce65e4922c6

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