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      Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery

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          Abstract

          Breast cancer is the most common cancer in women worldwide, and the development of new technologies for better understanding of the molecular changes involved in breast cancer progression is essential. Metabolic changes precede overt phenotypic changes, because cellular regulation ultimately affects the use of small-molecule substrates for cell division, growth or environmental changes such as hypoxia. Differences in metabolism between normal cells and cancer cells have been identified. Because small alterations in enzyme concentrations or activities can cause large changes in overall metabolite levels, the metabolome can be regarded as the amplified output of a biological system. The metabolome coverage in human breast cancer tissues can be maximized by combining different technologies for metabolic profiling. Researchers are investigating alterations in the steady state concentrations of metabolites that reflect amplified changes in genetic control of metabolism. Metabolomic results can be used to classify breast cancer on the basis of tumor biology, to identify new prognostic and predictive markers and to discover new targets for future therapeutic interventions. Here, we examine recent results, including those from the European FP7 project METAcancer consortium, that show that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights. We predict an intensified use of metabolomic screens in clinical and preclinical studies focusing on the onset and progression of tumor development.

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          Most cited references49

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          Metabolomics--the link between genotypes and phenotypes.

          Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
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            Redox environment of the cell as viewed through the redox state of the glutathione disulfide/glutathione couple.

            Redox state is a term used widely in the research field of free radicals and oxidative stress. Unfortunately, it is used as a general term referring to relative changes that are not well defined or quantitated. In this review we provide a definition for the redox environment of biological fluids, cell organelles, cells, or tissue. We illustrate how the reduction potential of various redox couples can be estimated with the Nernst equation and show how pH and the concentrations of the species comprising different redox couples influence the reduction potential. We discuss how the redox state of the glutathione disulfide-glutathione couple (GSSG/2GSH) can serve as an important indicator of redox environment. There are many redox couples in a cell that work together to maintain the redox environment; the GSSG/2GSH couple is the most abundant redox couple in a cell. Changes of the half-cell reduction potential (E(hc)) of the GSSG/2GSH couple appear to correlate with the biological status of the cell: proliferation E(hc) approximately -240 mV; differentiation E(hc) approximately -200 mV; or apoptosis E(hc) approximately -170 mV. These estimates can be used to more fully understand the redox biochemistry that results from oxidative stress. These are the first steps toward a new quantitative biology, which hopefully will provide a rationale and understanding of the cellular mechanisms associated with cell growth and development, signaling, and reductive or oxidative stress.
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              Metabolomics by numbers: acquiring and understanding global metabolite data.

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                Author and article information

                Contributors
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central
                1756-994X
                2012
                30 April 2012
                30 April 2013
                : 4
                : 4
                : 37
                Affiliations
                [1 ]Institute of Pathology, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
                [2 ]Biotechnology for Health and Well-being, VTT Technical Research Centre of Finland, Espoo and Turku, PO Box 1000, FI-02044 VTT, Finland
                [3 ]Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
                [4 ]German Breast Group, GBG-Forschungs GmbH, 63263 Neu-Isenburg, Germany
                [5 ]Genome Center, University of California Davis, Davis, CA 95616, USA
                Article
                gm336
                10.1186/gm336
                3446265
                22546809
                3501d605-fdd8-45ba-8538-9103393e68f3
                Copyright ©2012 BioMed Central Ltd.
                History
                Categories
                Review

                Molecular medicine
                breast cancer,metabolomics,lipidomics,biomarker analysis
                Molecular medicine
                breast cancer, metabolomics, lipidomics, biomarker analysis

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