A plasma metabolite panel as biomarkers for early primary breast cancer detection

• Verfasst von: Yuan, Baowen [VerfasserIn]
• Verfasst von: Tang, Qiuqiong [VerfasserIn]
• Verfasst von: Nees, Juliane [VerfasserIn]
• Verfasst von: Heil, Jörg Moritz [VerfasserIn]
• Verfasst von: Schott, Sarah [VerfasserIn]
• Verfasst von: Golatta, Michael [VerfasserIn]
• Verfasst von: Wallwiener, Markus [VerfasserIn]
• Verfasst von: Sohn, Christof [VerfasserIn]
• Verfasst von: Schneeweiss, Andreas [VerfasserIn]
• Verfasst von: Burwinkel, Barbara [VerfasserIn]
• Titel: A plasma metabolite panel as biomarkers for early primary breast cancer detection
• Verf.angabe: Baowen Yuan, Simon Schafferer, Qiuqiong Tang, Matthias Scheffler, Juliane Nees, Jörg Heil, Sarah Schott, Michael Golatta, Markus Wallwiener, Christof Sohn, Therese Koal, Barbara Wolf, Andreas Schneeweiß and Barbara Burwinkel
• Jahr: 2019
• Jahr des Originals: 2018
• Umfang: 10 S.
• Fussnoten: Gesehen am 01.08.2019 ; Online 13 Nov 2018
• Titel Quelle: Enthalten in: International journal of cancer
• Ort Quelle: Bognor Regis : Wiley-Liss, 1966
• Jahr Quelle: 2019
• Band/Heft Quelle: 144(2019), 11, Seite 2833-2842
• ISSN Quelle: 1097-0215
• DOI: doi:10.1002/ijc.31996
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: biomarker
• Sach-SW: breast cancer
• Sach-SW: detective marker
• Sach-SW: metabolites
• Sach-SW: metabolomics
• K10plus-PPN: 1670425339

Abstract

In recent years, metabolites have attracted substantial attention as promising novel biomarkers of various diseases. However, breast cancer plasma metabolite studies are still in their infancy. Here, we investigated the potential of metabolites to serve as minimally invasive, early detection markers of primary breast cancer. We profiled metabolites extracted from the plasma of primary breast cancer patients and healthy controls using tandem mass spectrometry (UHPLC-MS/MS and FIA-MS/MS). Two metabolites were found to be upregulated, while 16 metabolites were downregulated in primary breast cancer patients compared to healthy controls in both the training and validation cohorts. A panel of seven metabolites was selected by LASSO regression analysis. This panel could differentiate primary breast cancer patients from healthy controls, with an AUC of 0.87 (95% CI: 0.81 0.92) in the training cohort and an AUC of 0.80 (95% CI: 0.71 0.87) in the validation cohort. These significantly differentiated metabolites are mainly involved in the amino acid metabolism and breast cancer cell growth pathways. In conclusion, using a metabolomics approach, we identified metabolites that have potential value for development of a multimarker blood-based test to complement and improve early breast cancer detection. The panel identified herein might be part of a prescreening tool, especially for younger women or for closely observing women with certain risks, to facilitate decision making regarding which individuals should undergo further diagnostic tests. In the future, the combination of metabolites and other blood-based molecular marker sets, such as DNA methylation, microRNA, and cell-free DNA mutation markers, will be an attractive option.