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dc.contributor.authorJové Font, Mariona
dc.contributor.authorCollado, Ricardo
dc.contributor.authorQuiles, Jose L.
dc.contributor.authorRamírez Tortosa, MCarmen
dc.contributor.authorSol, Joaquim
dc.contributor.authorRuiz-Sanjuan, Maria
dc.contributor.authorFernandez, Mónica
dc.contributor.authorde la Torre Cabrera, Capilla
dc.contributor.authorRamírez-Tortosa, Cesar
dc.contributor.authorGranados Principal, Sergio
dc.contributor.authorSánchez Rovira, Pedro
dc.contributor.authorPamplona Gras, Reinald
dc.date.accessioned2021-03-10T11:51:16Z
dc.date.available2021-03-10T11:51:16Z
dc.date.issued2017
dc.identifier.issn1949-2553
dc.identifier.urihttp://hdl.handle.net/10459.1/70713
dc.description.abstractPurpose: Metabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype. Methods: Plasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer. Results: Metabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer. Conclusions: In breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.ca_ES
dc.description.sponsorshipThis research was funded by the Spanish Ministry of Economy and Competitiveness, Institute Carlos III (FIS grant PI14/00328), and the Autonomous Government of Catalonia (2014SGR168) to R.P. This study has been co-financed by FEDER funds from the European Union (‘Una manera de hacer Europa’).ca_ES
dc.language.isoengca_ES
dc.publisherImpact Journalsca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.18632/oncotarget.14521ca_ES
dc.relation.ispartofOncotarget, 2017, vol. 8, núm. 12, p. 19522-19533ca_ES
dc.rightscc-by (c) Jové et al., 2017ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBreast cancerca_ES
dc.subjectBiomarkerca_ES
dc.subjectMass spectrometryca_ES
dc.subjectMetabolitesca_ES
dc.subjectMetabolomicsca_ES
dc.titleA plasma metabolomic signature discloses human breast cancerca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec026368
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.18632/oncotarget.14521


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