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Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer

Cancer metastasis is the leading cause of cancer-related mortality and the process of the epithelial-to-mesenchymal transition (EMT) is crucial for cancer metastasis. Both partial and complete EMT have been reported to influence the metabolic plasticity of cancer cells in terms of switching among th...

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Autores principales: Muralidharan, Srinath, Sahoo, Sarthak, Saha, Aryamaan, Chandran, Sanjay, Majumdar, Sauma Suvra, Mandal, Susmita, Levine, Herbert, Jolly, Mohit Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961667/
https://www.ncbi.nlm.nih.gov/pubmed/35204797
http://dx.doi.org/10.3390/biom12020297
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author Muralidharan, Srinath
Sahoo, Sarthak
Saha, Aryamaan
Chandran, Sanjay
Majumdar, Sauma Suvra
Mandal, Susmita
Levine, Herbert
Jolly, Mohit Kumar
author_facet Muralidharan, Srinath
Sahoo, Sarthak
Saha, Aryamaan
Chandran, Sanjay
Majumdar, Sauma Suvra
Mandal, Susmita
Levine, Herbert
Jolly, Mohit Kumar
author_sort Muralidharan, Srinath
collection PubMed
description Cancer metastasis is the leading cause of cancer-related mortality and the process of the epithelial-to-mesenchymal transition (EMT) is crucial for cancer metastasis. Both partial and complete EMT have been reported to influence the metabolic plasticity of cancer cells in terms of switching among the oxidative phosphorylation, fatty acid oxidation and glycolysis pathways. However, a comprehensive analysis of these major metabolic pathways and their associations with EMT across different cancers is lacking. Here, we analyse more than 180 cancer cell datasets and show the diverse associations of these metabolic pathways with the EMT status of cancer cells. Our bulk data analysis shows that EMT generally positively correlates with glycolysis but negatively with oxidative phosphorylation and fatty acid metabolism. These correlations are also consistent at the level of their molecular master regulators, namely AMPK and HIF1α. Yet, these associations are shown to not be universal. The analysis of single-cell data for EMT induction shows dynamic changes along the different axes of metabolic pathways, consistent with general trends seen in bulk samples. Further, assessing the association of EMT and metabolic activity with patient survival shows that a higher extent of EMT and glycolysis predicts a worse prognosis in many cancers. Together, our results reveal the underlying patterns of metabolic plasticity and heterogeneity as cancer cells traverse through the epithelial–hybrid–mesenchymal spectrum of states.
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spelling pubmed-89616672022-03-30 Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer Muralidharan, Srinath Sahoo, Sarthak Saha, Aryamaan Chandran, Sanjay Majumdar, Sauma Suvra Mandal, Susmita Levine, Herbert Jolly, Mohit Kumar Biomolecules Article Cancer metastasis is the leading cause of cancer-related mortality and the process of the epithelial-to-mesenchymal transition (EMT) is crucial for cancer metastasis. Both partial and complete EMT have been reported to influence the metabolic plasticity of cancer cells in terms of switching among the oxidative phosphorylation, fatty acid oxidation and glycolysis pathways. However, a comprehensive analysis of these major metabolic pathways and their associations with EMT across different cancers is lacking. Here, we analyse more than 180 cancer cell datasets and show the diverse associations of these metabolic pathways with the EMT status of cancer cells. Our bulk data analysis shows that EMT generally positively correlates with glycolysis but negatively with oxidative phosphorylation and fatty acid metabolism. These correlations are also consistent at the level of their molecular master regulators, namely AMPK and HIF1α. Yet, these associations are shown to not be universal. The analysis of single-cell data for EMT induction shows dynamic changes along the different axes of metabolic pathways, consistent with general trends seen in bulk samples. Further, assessing the association of EMT and metabolic activity with patient survival shows that a higher extent of EMT and glycolysis predicts a worse prognosis in many cancers. Together, our results reveal the underlying patterns of metabolic plasticity and heterogeneity as cancer cells traverse through the epithelial–hybrid–mesenchymal spectrum of states. MDPI 2022-02-12 /pmc/articles/PMC8961667/ /pubmed/35204797 http://dx.doi.org/10.3390/biom12020297 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Muralidharan, Srinath
Sahoo, Sarthak
Saha, Aryamaan
Chandran, Sanjay
Majumdar, Sauma Suvra
Mandal, Susmita
Levine, Herbert
Jolly, Mohit Kumar
Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer
title Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer
title_full Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer
title_fullStr Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer
title_full_unstemmed Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer
title_short Quantifying the Patterns of Metabolic Plasticity and Heterogeneity along the Epithelial–Hybrid–Mesenchymal Spectrum in Cancer
title_sort quantifying the patterns of metabolic plasticity and heterogeneity along the epithelial–hybrid–mesenchymal spectrum in cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961667/
https://www.ncbi.nlm.nih.gov/pubmed/35204797
http://dx.doi.org/10.3390/biom12020297
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