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Cancers
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Cancer Metabolism
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Cancer Metabolism

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (20 December 2012) | Viewed by 9973

Special Issue Editor

Dr. Daniel Morvan

E-Mail Website
Guest Editor
UMR 1019, Université d'Auvergne, 58 Rue Montalembert, 63011 Clermont-Ferrand, France
Interests: Metabolomics, Tumors, Oxydative stress, DNA repair

Keywords

  • metabolomics
  • proteomics
  • genomics
  • bioenergetics
  • oxidative stress
  • DNA damage response and repair

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Published Papers (1 paper)

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Research

20 pages, 1122 KiB  
Article
Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data
by I-Lin Tsai, Tien-Chueh Kuo, Tsung-Jung Ho, Yeu-Chern Harn, San-Yuan Wang, Wen-Mei Fu, Ching-Hua Kuo and Yufeng Jane Tseng
Cancers 2013, 5(2), 491-510; https://doi.org/10.3390/cancers5020491 - 3 May 2013
Cited by 15 | Viewed by 9418
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Cancer Metabolism)
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