Abstract
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.
Highlights
The global cancer burden is estimated to have risen to 18.1 million new cases and 9.6 million deaths in 2018 (WHO) [1], being the second leading cause of death worldwide
Urinary Metabolomic Pattern Based on 1 H nuclear magnetic resonance spectroscopy (NMR)
This study assessed the metabolomic urinary profile of Breast cancer (BC) patients in active and follow-up stages compared with that in healthy volunteers, using 1 H NMR combined with multivariate statistical tools (PCA, partial least square-discriminant analysis (PLS-DA), and orthogonal partial least square-discriminant (OPLS-DA)) that were applied to two groups (BC and CTL)
Summary
The global cancer burden is estimated to have risen to 18.1 million new cases and 9.6 million deaths in 2018 (WHO) [1], being the second leading cause of death worldwide. Common methods of routine surveillance for BC include periodic mammography, self- or physician performed examination, and blood tests of tumor markers, including cancer antigens (CA 15-3, CA 27.29), carcinoembryonic antigen (CEA), tissue polypeptide specific-antigen, and human epidermal growth factor receptor 2 (the shed form). An additional factor that contributes to the poor prognosis of patients diagnosed with BC is the fact that the diagnosis is often delayed due to limitations in screening tests [5]. A recent approach is metabolomics that study a subset of small molecules derived from the global or targeted analysis of metabolic profiles from biological samples such as blood [6], urine [7,8,9,10], cells, or tissue [11,12,13,14,15], representing a valuable tool in the detection of several diseases including cancer. Several reports have already demonstrated the importance of studying the metabolome as means to discover a set of metabolites to be used as cancer biomarkers [16,17,18]
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