LC-hrms metabolomics profiling in breast cancer: Searching for biomarkers in human plasma samples
| Autor: | Reinald Pamplona Gras, Caridad Díaz Navarro, Francisco José García Verdejo, V Rivas, JA Olid, Maria Lomas Garrido, Olga Genilloud, Ricardo Collado Martín, Pedro Sánchez Rovira, José Pérez del Palacio, Ana Laura Ortega Granados, Mónica Fernández Navarro, Francisca Vicente Pérez, Ana Jaén Morago, Mariona Jové Font, David Fernández Garay, Capilla de la Torre Cabrera, Natalia Luque Caro, Cesar Ramirez Tortosa, María Ruiz Sanjuan |
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| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Journal of Clinical Oncology. 35:e23053-e23053 |
| ISSN: | 1527-7755 0732-183X |
| DOI: | 10.1200/jco.2017.35.15_suppl.e23053 |
| Popis: | e23053 Background: Breast cancer (BC) is most common cancer in women and development of new technologies for understanding molecular changes involved in BC progression is essential. Metabolic changes precede phenotypic changes, due to cellular regulation affects use of small-molecule substrates for cell division, growth or environmental changes, such as hypoxia. Metabolomics (profiling of metabolites in biofluid, cell and tissue) is routinely applied as a tool for biomarker discovery. Liquid chromatography−mass spectrometry(LC−MS)is the dominating platform. Methods: With the aim to find metabolomic profiles for diagnostic and prognostic of newly diagnosticated breast cancer (BC) we have investigated plasma samples in neoadjuvant setting (134 BC patients with median age of 51 years, stages T1-4,N1-2,M0 and 135 healthy controls) using high resolution mass spectrometry couple to liquid chromatography in reverse phase and HILIC modes which provided resolved chromatography of highly polar as well as hydrophobic analytes, enabling the analysis of a wide range of chemicals, necessary for untargeted metabolomics. Chromatograms were processed with software (Markerview) to generate a table listing retention times with associated ion masses and intensities. To identify potential biomarkers, we used in combination 2 independent variable selection techniques: principal component analysis and Student t-test. Results: We observed a significant difference in metabolic profile between the 2 groups. 15 molecular features [oleamide, KDNalpha2-3Galbeta1-4Glcbeta-Cer(d18:1/22:0)] were found significantly down/up regulated in BC patients compared with healthy subjects, and they were selected as diagnostic biomarkers. Additionally, ROC curve analysis was used to assess their clinical usefulness, obtaining an AUC of 0.963 (95% CI 0.919-0.989) when using a multivariate model of 3 features. As an outcome, we showed that selected biomarkers are useful as diagnostic biomarkers. Conclusions: The present study demonstrates potential of metabolomics in identifying novel biomarkers for BC. Further studies may reveal the potential of metabolites as diagnostics biomarkers for BC and their role in pathogenesis. |
| Databáze: | OpenAIRE |
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