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MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers

Altered cellular metabolism is a fundamental adaptation of cancer during rapid proliferation as a result of growth factor overstimulation. We review different pathways involving metabolic alterations in cancers including aerobic glycolysis, pentose phosphate pathway, de novo fatty acid synthesis, an...

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Autores principales: Pinweha, Pannapa, Rattanapornsompong, Khanti, Charoensawan, Varodom, Jitrapakdee, Sarawut
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4915959/
https://www.ncbi.nlm.nih.gov/pubmed/27358718
http://dx.doi.org/10.1016/j.csbj.2016.05.005
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author Pinweha, Pannapa
Rattanapornsompong, Khanti
Charoensawan, Varodom
Jitrapakdee, Sarawut
author_facet Pinweha, Pannapa
Rattanapornsompong, Khanti
Charoensawan, Varodom
Jitrapakdee, Sarawut
author_sort Pinweha, Pannapa
collection PubMed
description Altered cellular metabolism is a fundamental adaptation of cancer during rapid proliferation as a result of growth factor overstimulation. We review different pathways involving metabolic alterations in cancers including aerobic glycolysis, pentose phosphate pathway, de novo fatty acid synthesis, and serine and glycine metabolism. Although oncoproteins, c-MYC, HIF1α and p53 are the major drivers of this metabolic reprogramming, post-transcriptional regulation by microRNAs (miR) also plays an important role in finely adjusting the requirement of the key metabolic enzymes underlying this metabolic reprogramming. We also combine the literature data on the miRNAs that potentially regulate 40 metabolic enzymes responsible for metabolic reprogramming in cancers, with additional miRs from computational prediction. Our analyses show that: (1) a metabolic enzyme is frequently regulated by multiple miRs, (2) confidence scores from prediction algorithms might be useful to help narrow down functional miR-mRNA interaction, which might be worth further experimental validation. By combining known and predicted interactions of oncogenic transcription factors (TFs) (c-MYC, HIF1α and p53), sterol regulatory element binding protein 1 (SREBP1), 40 metabolic enzymes, and regulatory miRs we have established one of the first reference maps for miRs and oncogenic TFs that regulate metabolic reprogramming in cancers. The combined network shows that glycolytic enzymes are linked to miRs via p53, c-MYC, HIF1α, whereas the genes in serine, glycine and one carbon metabolism are regulated via the c-MYC, as well as other regulatory organization that cannot be observed by investigating individual miRs, TFs, and target genes.
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spelling pubmed-49159592016-06-29 MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers Pinweha, Pannapa Rattanapornsompong, Khanti Charoensawan, Varodom Jitrapakdee, Sarawut Comput Struct Biotechnol J Short Survey Altered cellular metabolism is a fundamental adaptation of cancer during rapid proliferation as a result of growth factor overstimulation. We review different pathways involving metabolic alterations in cancers including aerobic glycolysis, pentose phosphate pathway, de novo fatty acid synthesis, and serine and glycine metabolism. Although oncoproteins, c-MYC, HIF1α and p53 are the major drivers of this metabolic reprogramming, post-transcriptional regulation by microRNAs (miR) also plays an important role in finely adjusting the requirement of the key metabolic enzymes underlying this metabolic reprogramming. We also combine the literature data on the miRNAs that potentially regulate 40 metabolic enzymes responsible for metabolic reprogramming in cancers, with additional miRs from computational prediction. Our analyses show that: (1) a metabolic enzyme is frequently regulated by multiple miRs, (2) confidence scores from prediction algorithms might be useful to help narrow down functional miR-mRNA interaction, which might be worth further experimental validation. By combining known and predicted interactions of oncogenic transcription factors (TFs) (c-MYC, HIF1α and p53), sterol regulatory element binding protein 1 (SREBP1), 40 metabolic enzymes, and regulatory miRs we have established one of the first reference maps for miRs and oncogenic TFs that regulate metabolic reprogramming in cancers. The combined network shows that glycolytic enzymes are linked to miRs via p53, c-MYC, HIF1α, whereas the genes in serine, glycine and one carbon metabolism are regulated via the c-MYC, as well as other regulatory organization that cannot be observed by investigating individual miRs, TFs, and target genes. Research Network of Computational and Structural Biotechnology 2016-06-04 /pmc/articles/PMC4915959/ /pubmed/27358718 http://dx.doi.org/10.1016/j.csbj.2016.05.005 Text en © 2016 Natrix Separations http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Short Survey
Pinweha, Pannapa
Rattanapornsompong, Khanti
Charoensawan, Varodom
Jitrapakdee, Sarawut
MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers
title MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers
title_full MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers
title_fullStr MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers
title_full_unstemmed MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers
title_short MicroRNAs and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers
title_sort micrornas and oncogenic transcriptional regulatory networks controlling metabolic reprogramming in cancers
topic Short Survey
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4915959/
https://www.ncbi.nlm.nih.gov/pubmed/27358718
http://dx.doi.org/10.1016/j.csbj.2016.05.005
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