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Advisor(s)
Abstract(s)
: Breast cancer (BC) remains the second leading cause of death among women worldwide.
An emerging approach based on the identification of endogenous metabolites (EMs) and the
establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical
diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this
work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy
controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful
approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of
BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR
processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting
a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the
highest contribution towards discriminating BC patients from healthy controls (variable importance
in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were
ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification
of some metabolites with the highest sensitivities and specificities to discriminate BC patients from
healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic
pathway analysis indicated several metabolism pathway disruptions, including amino acid and
carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained
results support the high throughput potential of NMR-based urinary metabolomics patterns in
discriminating BC patients from CTL. Further investigations could unravel novel mechanistic
insights into disease pathophysiology, monitor disease recurrence, and predict patient response
towards therapy.
Description
Keywords
Breast cancer 1H NMR Urine Metabolomics Chemometric tools . Faculdade de Ciências Exatas e da Engenharia Centro de Química da Madeira
Citation
Silva, C. L., Olival, A., Perestrelo, R., Silva, P., Tomás, H., & Câmara, J. S. (2019). Untargeted urinary 1H NMR-based metabolomic pattern as a potential platform in breast cancer detection. Metabolites, 9(11), 269. https://doi.org/10.3390/metabo9110269
Publisher
MDPI