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      Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data

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          Abstract

          Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis.

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          Most cited references54

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          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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            DAVID: Database for Annotation, Visualization, and Integrated Discovery.

            Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.
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              Defining the role of hypoxia-inducible factor 1 in cancer biology and therapeutics.

              Adaptation of cancer cells to their microenvironment is an important driving force in the clonal selection that leads to invasive and metastatic disease. O2 concentrations are markedly reduced in many human cancers compared with normal tissue, and a major mechanism mediating adaptive responses to reduced O2 availability (hypoxia) is the regulation of transcription by hypoxia-inducible factor 1 (HIF-1). This review summarizes the current state of knowledge regarding the molecular mechanisms by which HIF-1 contributes to cancer progression, focusing on (1) clinical data associating increased HIF-1 levels with patient mortality; (2) preclinical data linking HIF-1 activity with tumor growth; (3) molecular data linking specific HIF-1 target gene products to critical aspects of cancer biology and (4) pharmacological data showing anticancer effects of HIF-1 inhibitors in mouse models of human cancer.
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                Author and article information

                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                03 May 2013
                June 2013
                : 5
                : 2
                : 491-510
                Affiliations
                [1 ]Department of Pharmacy, National Taiwan University, No. 1, Jen-Ai Road, Section 1 Taipei 10051, Taiwan; E-Mail: f95423009@ 123456ntu.edu.tw
                [2 ]The Metabolomics Group, National Taiwan University, Taipei 106, Taiwan; E-Mails: cot@ 123456cmdm.csie.ntu.edu.tw (T.-C.K.); pipisteve2@ 123456gmail.com (T.-J.H.); duke3d.harn@ 123456gmail.com (Y.-C.H.); sanyuan731@ 123456gmail.com (S.-Y.W.)
                [3 ]Center for Genomic Medicine, National Taiwan University, Taipei 10051, Taiwan
                [4 ]Graduate Institute of Biomedical Electronic and Bioinformatics, National Taiwan University, Room 410 BL Building, No. 1, Roosevelt Road, Sec. 4, Taipei 106, Taiwan
                [5 ]Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
                [6 ]Graduate Institute of Networking and Multimedia, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
                [7 ]Department of Pharmacology, National Taiwan University, 11 F No. 1 Sec. 1, Ren-ai Rd., Taipei 10051, Taiwan; E-Mail: wenmei@ 123456ntu.edu.tw
                Author notes
                [†]

                These authors contributed equally to this work.

                [* ] Authors to whom correspondence should be addressed; E-Mails: kuoch@ 123456ntu.edu.tw (C.-H.K.); yjtseng@ 123456csie.ntu.edu.tw (Y.J.T.); Tel: +886-2-3366-4888 (Y.J.T.); Fax: +886-2-2362-8167 (Y.J.T.).
                Article
                cancers-05-00491
                10.3390/cancers5020491
                3730319
                24216987
                7a709313-8d31-4bbf-8a5c-d8d85b836c3c
                © 2013 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 12 April 2013
                : 24 April 2013
                : 24 April 2013
                Categories
                Article

                1h-nmr spectroscopy,metabolic network,metabolomics,multivariate analysis,tumor hypoxia

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