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Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas

Understanding metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Here, using transcriptomic data for 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we prese...

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Autores principales: Rosario, S. R., Long, M. D., Affronti, H. C., Rowsam, A. M., Eng, K. H., Smiraglia, D. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294258/
https://www.ncbi.nlm.nih.gov/pubmed/30552315
http://dx.doi.org/10.1038/s41467-018-07232-8
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author Rosario, S. R.
Long, M. D.
Affronti, H. C.
Rowsam, A. M.
Eng, K. H.
Smiraglia, D. J.
author_facet Rosario, S. R.
Long, M. D.
Affronti, H. C.
Rowsam, A. M.
Eng, K. H.
Smiraglia, D. J.
author_sort Rosario, S. R.
collection PubMed
description Understanding metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Here, using transcriptomic data for 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we present a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. We confirm pathway dysregulation in separate patient populations, demonstrating the robustness of our approach. Bootstrapping simulations were then applied to assess the biological significance of these alterations. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest, and patterns of metabolic flux, in both common and rare disease sites. Further, we show that Master Metabolic Transcriptional Regulators explain why metabolic differences exist, can segregate patient populations, and predict responders to different metabolism-targeted therapeutics.
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spelling pubmed-62942582018-12-17 Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas Rosario, S. R. Long, M. D. Affronti, H. C. Rowsam, A. M. Eng, K. H. Smiraglia, D. J. Nat Commun Article Understanding metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Here, using transcriptomic data for 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we present a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. We confirm pathway dysregulation in separate patient populations, demonstrating the robustness of our approach. Bootstrapping simulations were then applied to assess the biological significance of these alterations. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest, and patterns of metabolic flux, in both common and rare disease sites. Further, we show that Master Metabolic Transcriptional Regulators explain why metabolic differences exist, can segregate patient populations, and predict responders to different metabolism-targeted therapeutics. Nature Publishing Group UK 2018-12-14 /pmc/articles/PMC6294258/ /pubmed/30552315 http://dx.doi.org/10.1038/s41467-018-07232-8 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Rosario, S. R.
Long, M. D.
Affronti, H. C.
Rowsam, A. M.
Eng, K. H.
Smiraglia, D. J.
Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
title Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
title_full Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
title_fullStr Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
title_full_unstemmed Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
title_short Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
title_sort pan-cancer analysis of transcriptional metabolic dysregulation using the cancer genome atlas
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294258/
https://www.ncbi.nlm.nih.gov/pubmed/30552315
http://dx.doi.org/10.1038/s41467-018-07232-8
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