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Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development

Recurrence and metastasis have been regarded as two of the greatest obstacles to cancer therapy. Cancer stem cells (CSCs) contribute to cancer development, with the distinctive features of recurrence and resistance to popular treatments such as drugs and chemotherapy. In addition, recent discoveries...

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Detalles Bibliográficos
Autores principales: Yu, Chong, Liu, Qiong, Chen, Cong, Wang, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061528/
https://www.ncbi.nlm.nih.gov/pubmed/32194614
http://dx.doi.org/10.3389/fgene.2019.01388
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author Yu, Chong
Liu, Qiong
Chen, Cong
Wang, Jin
author_facet Yu, Chong
Liu, Qiong
Chen, Cong
Wang, Jin
author_sort Yu, Chong
collection PubMed
description Recurrence and metastasis have been regarded as two of the greatest obstacles to cancer therapy. Cancer stem cells (CSCs) contribute to cancer development, with the distinctive features of recurrence and resistance to popular treatments such as drugs and chemotherapy. In addition, recent discoveries suggest that the epithelial mesenchymal transition (EMT) is an essential process in normal embryogenesis and tissue repair, as well as being a required step in cancer metastasis. Although there are many indications of the connections between metastasis and stem cells, these have often been studied separately or at most bi-laterally, not in an integrated way. In this study, we aimed to explore the global mechanisms and interrelationships among cancer, development, and metastasis, which are currently poorly understood. First, we constructed a core gene regulatory network containing specific genes and microRNAs of CSCs, EMT, and cancer. We uncovered seven distinct states emerging from the underlying landscape, denoted normal, premalignant, cancer, stem cell, CSC, lesion, and hyperplasia. Given the biological definition of each state, we also discuss the metastasis ability of each state. We show how and which types of cells can be transformed to a cancer state, and the connections among cancer, CSCs, and EMT. The barrier height and flux of the kinetic paths are explored to quantify how and which cells switch stochastically between the states. Our landscape model provides a quantitative approach to reveal the global mechanisms of cancer, development, and metastasis.
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spelling pubmed-70615282020-03-19 Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development Yu, Chong Liu, Qiong Chen, Cong Wang, Jin Front Genet Genetics Recurrence and metastasis have been regarded as two of the greatest obstacles to cancer therapy. Cancer stem cells (CSCs) contribute to cancer development, with the distinctive features of recurrence and resistance to popular treatments such as drugs and chemotherapy. In addition, recent discoveries suggest that the epithelial mesenchymal transition (EMT) is an essential process in normal embryogenesis and tissue repair, as well as being a required step in cancer metastasis. Although there are many indications of the connections between metastasis and stem cells, these have often been studied separately or at most bi-laterally, not in an integrated way. In this study, we aimed to explore the global mechanisms and interrelationships among cancer, development, and metastasis, which are currently poorly understood. First, we constructed a core gene regulatory network containing specific genes and microRNAs of CSCs, EMT, and cancer. We uncovered seven distinct states emerging from the underlying landscape, denoted normal, premalignant, cancer, stem cell, CSC, lesion, and hyperplasia. Given the biological definition of each state, we also discuss the metastasis ability of each state. We show how and which types of cells can be transformed to a cancer state, and the connections among cancer, CSCs, and EMT. The barrier height and flux of the kinetic paths are explored to quantify how and which cells switch stochastically between the states. Our landscape model provides a quantitative approach to reveal the global mechanisms of cancer, development, and metastasis. Frontiers Media S.A. 2020-03-02 /pmc/articles/PMC7061528/ /pubmed/32194614 http://dx.doi.org/10.3389/fgene.2019.01388 Text en Copyright © 2020 Yu, Liu, Chen and Wang http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Yu, Chong
Liu, Qiong
Chen, Cong
Wang, Jin
Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development
title Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development
title_full Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development
title_fullStr Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development
title_full_unstemmed Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development
title_short Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development
title_sort quantification of the underlying mechanisms and relationships among cancer, metastasis, and differentiation and development
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061528/
https://www.ncbi.nlm.nih.gov/pubmed/32194614
http://dx.doi.org/10.3389/fgene.2019.01388
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