A plasma metabolomic signature discloses human breast cancer
Purpose: 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...
| Autores: | , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10459.1/70713 |
| Acceso en línea: | https://doi.org/10.18632/oncotarget.14521 http://hdl.handle.net/10459.1/70713 |
| Access Level: | acceso abierto |
| Palabra clave: | Breast cancer Biomarker Mass spectrometry Metabolites Metabolomics |
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A plasma metabolomic signature discloses human breast cancerJové Font, MarionaCollado, RicardoQuiles, Jose L.Ramírez Tortosa, MCarmenSol, JoaquimRuiz-Sanjuan, MariaFernandez, Mónicade la Torre Cabrera, CapillaRamírez-Tortosa, CesarGranados Principal, SergioSánchez Rovira, PedroPamplona Gras, ReinaldBreast cancerBiomarkerMass spectrometryMetabolitesMetabolomicsPurpose: 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.This 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’).Impact Journals202120212017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.18632/oncotarget.14521http://hdl.handle.net/10459.1/70713http://hdl.handle.net/10459.1/70713reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a https://doi.org/10.18632/oncotarget.14521Oncotarget, 2017, vol. 8, núm. 12, p. 19522-19533cc-by (c) Jové et al., 2017info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/707132026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
A plasma metabolomic signature discloses human breast cancer |
| title |
A plasma metabolomic signature discloses human breast cancer |
| spellingShingle |
A plasma metabolomic signature discloses human breast cancer Jové Font, Mariona Breast cancer Biomarker Mass spectrometry Metabolites Metabolomics |
| title_short |
A plasma metabolomic signature discloses human breast cancer |
| title_full |
A plasma metabolomic signature discloses human breast cancer |
| title_fullStr |
A plasma metabolomic signature discloses human breast cancer |
| title_full_unstemmed |
A plasma metabolomic signature discloses human breast cancer |
| title_sort |
A plasma metabolomic signature discloses human breast cancer |
| dc.creator.none.fl_str_mv |
Jové Font, Mariona Collado, Ricardo Quiles, Jose L. Ramírez Tortosa, MCarmen Sol, Joaquim Ruiz-Sanjuan, Maria Fernandez, Mónica de la Torre Cabrera, Capilla Ramírez-Tortosa, Cesar Granados Principal, Sergio Sánchez Rovira, Pedro Pamplona Gras, Reinald |
| author |
Jové Font, Mariona |
| author_facet |
Jové Font, Mariona Collado, Ricardo Quiles, Jose L. Ramírez Tortosa, MCarmen Sol, Joaquim Ruiz-Sanjuan, Maria Fernandez, Mónica de la Torre Cabrera, Capilla Ramírez-Tortosa, Cesar Granados Principal, Sergio Sánchez Rovira, Pedro Pamplona Gras, Reinald |
| author_role |
author |
| author2 |
Collado, Ricardo Quiles, Jose L. Ramírez Tortosa, MCarmen Sol, Joaquim Ruiz-Sanjuan, Maria Fernandez, Mónica de la Torre Cabrera, Capilla Ramírez-Tortosa, Cesar Granados Principal, Sergio Sánchez Rovira, Pedro Pamplona Gras, Reinald |
| author2_role |
author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Breast cancer Biomarker Mass spectrometry Metabolites Metabolomics |
| topic |
Breast cancer Biomarker Mass spectrometry Metabolites Metabolomics |
| description |
Purpose: 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. |
| publishDate |
2017 |
| dc.date.none.fl_str_mv |
2017 2021 2021 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://doi.org/10.18632/oncotarget.14521 http://hdl.handle.net/10459.1/70713 http://hdl.handle.net/10459.1/70713 |
| url |
https://doi.org/10.18632/oncotarget.14521 http://hdl.handle.net/10459.1/70713 |
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Inglés |
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Inglés |
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Reproducció del document publicat a https://doi.org/10.18632/oncotarget.14521 Oncotarget, 2017, vol. 8, núm. 12, p. 19522-19533 |
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cc-by (c) Jové et al., 2017 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
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cc-by (c) Jové et al., 2017 http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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Impact Journals |
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Impact Journals |
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