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Metabolites
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NMR-Based Metabolomics: Investigating Biomarkers in Cancer Metabolism
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NMR-Based Metabolomics: Investigating Biomarkers in Cancer Metabolism

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Integrative Metabolomics".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 6434

Special Issue Editor

Prof. Dr. Jean-Marie Colet

E-Mail Website
Guest Editor
Department of Human Biology & Toxicology, Université de Mons, Mons, Belgium
Interests: NMR-based metabolomics; toxicology; cancer; drug development

Special Issue Information

Dear Colleagues,

Beyond the crucial roles of genes and proteins in the development, diagnosis, and prognosis of cancers, the importance of metabolites, organic molecules of low molecular weight, in this disease has more recently been discovered. Recent advances in NMR-based metabolomics have made it possible to establish very precise and specific metabolic signatures of various cancer types. From these signatures, some inferences have been allowed in biochemical and cellular signaling pathways privileged by cancer cells to maintain a high metabolic turnover. Key actors of this specific cancer metabolism are now exploited either as diagnostic or prognostic biomarkers or are foreseen as potential indicators of the patient’s response to targeted therapies. This Special Issue of Metabolites will take stock of current knowledge and perspectives concerning the roles of those metabolites in the mechanisms of cancer cells, in the acquisition of resistance to targeted therapies, as well as their future development in predictive diagnostic and prognostic biomarkers. Topics covering experimental approaches, including animal and cancer cell models, as well as clinical studies are welcome. Particular attention will be paid to translational studies in order to assess the relevance of transposing biomarkers from bench to patient’s bed. In parallel, papers describing advanced methods and software used in the processing, management, and integration of complex metabonomics data are expected, without mentioning quality control and statistical evaluation.

Prof. Dr. Jean-Marie Colet
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Metabolomics and cancer
  • Metabolomics and biomarkers
  • 1H-NMR and metabolites
  • Cancer metabolism
  • Biomarkers in cancer

Published Papers (2 papers)

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Research

19 pages, 3409 KiB  
Article
Targeting Metabolic Reprogramming to Improve Breast Cancer Treatment: An In Vitro Evaluation of Selected Metabolic Inhibitors Using a Metabolomic Approach
by Anaïs Draguet, Vanessa Tagliatti and Jean-Marie Colet
Metabolites 2021, 11(8), 556; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11080556 - 22 Aug 2021
Cited by 8 | Viewed by 2681
Abstract
Characteristic metabolic adaptations are recognized as a cancer hallmark. Breast cancer, like other cancer types, displays cellular respiratory switches—in particular, the Warburg effect—and important fluctuations in the glutamine and choline metabolisms. This cancer remains a world health issue mainly due to the side [...] Read more.
Characteristic metabolic adaptations are recognized as a cancer hallmark. Breast cancer, like other cancer types, displays cellular respiratory switches—in particular, the Warburg effect—and important fluctuations in the glutamine and choline metabolisms. This cancer remains a world health issue mainly due to the side effects associated with chemotherapy, which force a reduction in the administered dose or even a complete discontinuation of the treatment. For example, Doxorubicin is efficient to treat breast cancer but unfortunately induces severe cardiotoxicity. In the present in vitro study, selected metabolic inhibitors were evaluated alone or in combination as potential treatments against breast cancer. In addition, the same inhibitors were used to possibly potentiate the effects of Doxorubicin. As a result, the combination of CB-839 (glutaminase inhibitor) and Oxamate (lactate dehydrogenase inhibitor) and the combination of CB-839/Oxamate/D609 (a phosphatidylcholine-specific phospholipase C inhibitor) caused significant cell mortality in both MDA-MB-231 and MCF-7, two breast cancer cell lines. Furthermore, all inhibitors were able to improve the efficacy of Doxorubicin on the same cell lines. Those findings are quite encouraging with respect to the clinical goal of reducing the exposure of patients to Doxorubicin and, subsequently, the severity of the associated cardiotoxicity, while keeping the same treatment efficacy. Full article
(This article belongs to the Special Issue NMR-Based Metabolomics: Investigating Biomarkers in Cancer Metabolism)
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24 pages, 13122 KiB  
Article
Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor
by Elien Derveaux, Michiel Thomeer, Liesbet Mesotten, Gunter Reekmans and Peter Adriaensens
Metabolites 2021, 11(8), 537; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11080537 - 12 Aug 2021
Cited by 8 | Viewed by 3084
Abstract
Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the [...] Read more.
Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72). Full article
(This article belongs to the Special Issue NMR-Based Metabolomics: Investigating Biomarkers in Cancer Metabolism)
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