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      Plasma metabolites associated with colorectal cancer: A discovery‐replication strategy

      research-article
      1 , 2 , 3 , 4 , 5 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 1 , 12 , 2 , 2 , 9 , 6 , 7 , 5 , 1 , 13 , 14 , 3 , 6 , 7 , 5 , 8 , 12 , 14 , 1 , 3 , 8 , 15 , 2 , , 6 , 7
      International Journal of Cancer
      John Wiley & Sons, Inc.
      colorectal cancer, metabolomics, discovery‐replication approach, UHPLC‐QTOF‐MS

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          Abstract

          Colorectal cancer is known to arise from multiple tumorigenic pathways; however, the underlying mechanisms remain not completely understood. Metabolomics is becoming an increasingly popular tool in assessing biological processes. Previous metabolomics research focusing on colorectal cancer is limited by sample size and did not replicate findings in independent study populations to verify robustness of reported findings. Here, we performed a ultrahigh performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) screening on EDTA plasma from 268 colorectal cancer patients and 353 controls using independent discovery and replication sets from two European cohorts (ColoCare Study: n = 180 patients/n = 153 controls; the Colorectal Cancer Study of Austria (CORSA) n = 88 patients/n = 200 controls), aiming to identify circulating plasma metabolites associated with colorectal cancer and to improve knowledge regarding colorectal cancer etiology. Multiple logistic regression models were used to test the association between disease state and metabolic features. Statistically significant associated features in the discovery set were taken forward and tested in the replication set to assure robustness of our findings. All models were adjusted for sex, age, BMI and smoking status and corrected for multiple testing using False Discovery Rate. Demographic and clinical data were abstracted from questionnaires and medical records.

          Abstract

          What's new?

          Colorectal cancer exhibits certain characteristic changes in metabolic pathways. To expand upon previous findings, these authors performed a discovery‐replication study using two large independent study populations from different countries, Germany and Austria. They tested metabolic profiles of cancer patients and controls, identifying 691 statistically significant features in the discovery cohort. Testing the second cohort narrowed it to 97. These corresponded to 28 metabolites, of which 15 could be identified. It will be useful to go forward with prospective analysis on these 15 metabolites, to determine whether they have predictive or prognostic value.

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          Most cited references36

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          Microbiota-Modulated Metabolites Shape the Intestinal Microenvironment by Regulating NLRP6 Inflammasome Signaling.

          Host-microbiome co-evolution drives homeostasis and disease susceptibility, yet regulatory principles governing the integrated intestinal host-commensal microenvironment remain obscure. While inflammasome signaling participates in these interactions, its activators and microbiome-modulating mechanisms are unknown. Here, we demonstrate that the microbiota-associated metabolites taurine, histamine, and spermine shape the host-microbiome interface by co-modulating NLRP6 inflammasome signaling, epithelial IL-18 secretion, and downstream anti-microbial peptide (AMP) profiles. Distortion of this balanced AMP landscape by inflammasome deficiency drives dysbiosis development. Upon fecal transfer, colitis-inducing microbiota hijacks this microenvironment-orchestrating machinery through metabolite-mediated inflammasome suppression, leading to distorted AMP balance favoring its preferential colonization. Restoration of the metabolite-inflammasome-AMP axis reinstates a normal microbiota and ameliorates colitis. Together, we identify microbial modulators of the NLRP6 inflammasome and highlight mechanisms by which microbiome-host interactions cooperatively drive microbial community stability through metabolite-mediated innate immune modulation. Therefore, targeted "postbiotic" metabolomic intervention may restore a normal microenvironment as treatment or prevention of dysbiosis-driven diseases.
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            Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry.

            Most cancer cells predominantly produce energy by glycolysis rather than oxidative phosphorylation via the tricarboxylic acid (TCA) cycle, even in the presence of an adequate oxygen supply (Warburg effect). However, little has been reported regarding the direct measurements of global metabolites in clinical tumor tissues. Here, we applied capillary electrophoresis time-of-flight mass spectrometry, which enables comprehensive and quantitative analysis of charged metabolites, to simultaneously measure their levels in tumor and grossly normal tissues obtained from 16 colon and 12 stomach cancer patients. Quantification of 94 metabolites in colon and 95 metabolites in stomach involved in glycolysis, the pentose phosphate pathway, the TCA and urea cycles, and amino acid and nucleotide metabolisms resulted in the identification of several cancer-specific metabolic traits. Extremely low glucose and high lactate and glycolytic intermediate concentrations were found in both colon and stomach tumor tissues, which indicated enhanced glycolysis and thus confirmed the Warburg effect. Significant accumulation of all amino acids except glutamine in the tumors implied autophagic degradation of proteins and active glutamine breakdown for energy production, i.e., glutaminolysis. In addition, significant organ-specific differences were found in the levels of TCA cycle intermediates, which reflected the dependency of each tissue on aerobic respiration according to oxygen availability. The results uncovered unexpectedly poor nutritional conditions in the actual tumor microenvironment and showed that capillary electrophoresis coupled to mass spectrometry-based metabolomics, which is capable of quantifying the levels of energy metabolites in tissues, could be a powerful tool for the development of novel anticancer agents that target cancer-specific metabolism.
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              Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

              Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.
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                Author and article information

                Contributors
                andrea.gsur@meduniwien.ac.at
                Journal
                Int J Cancer
                Int. J. Cancer
                10.1002/(ISSN)1097-0215
                IJC
                International Journal of Cancer
                John Wiley & Sons, Inc. (Hoboken, USA )
                0020-7136
                1097-0215
                14 February 2019
                01 September 2019
                : 145
                : 5 ( doiID: 10.1002/ijc.v145.5 )
                : 1221-1231
                Affiliations
                [ 1 ] Division of Human Nutrition and Health Wageningen University & Research Wageningen The Netherlands
                [ 2 ] Institute of Cancer Research, Department of Medicine I Medical University of Vienna Austria
                [ 3 ] Biomarkers Group International Agency for Research on Cancer Lyon France
                [ 4 ] Department of Statistics and Operations Research University of Vienna Austria
                [ 5 ] Department of Surgery Medical University Vienna Austria
                [ 6 ] Huntsman Cancer Institute Salt Lake City UT
                [ 7 ] Department of Population Health Sciences University of Utah Salt Lake City UT
                [ 8 ] Division of Preventive Oncology National Center for Tumor Diseases and German Cancer Research Center Heidelberg Germany
                [ 9 ] Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany
                [ 10 ] German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany
                [ 11 ] Division of Cancer Epidemiology German Cancer Research Center Heidelberg Germany
                [ 12 ] Department of General, Visceral and Transplantation Surgery University of Heidelberg Germany
                [ 13 ] Hospital Oberpullendorf Burgenland Austria
                [ 14 ] BEVITAL Bergen Norway
                [ 15 ] Genome Biology European Molecular Biology Laboratory (EMBL) Heidelberg Germany
                Author notes
                [*] [* ] Correspondence to: Andrea Gsur, Institute of Cancer Research, Department of Medicine I, Medical University of Vienna Borschkegasse 8a, Vienna, 1090, Austria, Tel.: 0043‐1‐40‐160‐57541, E‐mail: andrea.gsur@ 123456meduniwien.ac.at
                [†]

                A.J.M.R.G. and S.B. are the shared co‐first authors. N.H., A.G., and C.M.U. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-9272-7703
                https://orcid.org/0000-0001-5238-6900
                https://orcid.org/0000-0001-8001-8793
                https://orcid.org/0000-0002-9795-1528
                https://orcid.org/0000-0001-7641-059X
                Article
                IJC32146
                10.1002/ijc.32146
                6614008
                30665271
                d005cb14-f601-48e1-a1c7-4021dfc8cdcf
                © 2019 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 November 2018
                : 08 January 2019
                Page count
                Figures: 2, Tables: 3, Pages: 11, Words: 7345
                Funding
                Funded by: Austrian Science Fund
                Award ID: I1578‐B19
                Funded by: Bundesministerium für Bildung und Forschung
                Award ID: 01KT1512
                Funded by: Huntsman Cancer Foundation
                Funded by: KWF Kankerbestrijding
                Award ID: UW2013‐6397
                Funded by: National Cancer Institute
                Award ID: R01 CA189184
                Award ID: R01 CA207371
                Award ID: U01 CA206110
                Funded by: National Cancer Institute, INCa, France
                Award ID: 2014‐007
                Funded by: National Human Genome Research Institute
                Award ID: Ruth L. Kirschstein National Research Service Awar
                Funded by: Netherlands Organization for Health Research and Development
                Funded by: The Research Council of Norway
                Award ID: 236564/H10
                Categories
                Cancer Epidemiology
                Cancer Epidemiology
                Custom metadata
                2.0
                ijc32146
                1 September 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.9 mode:remove_FC converted:30.09.2019

                Oncology & Radiotherapy
                colorectal cancer,metabolomics,discovery‐replication approach,uhplc‐qtof‐ms

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