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A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition

Patient-specific analysis of molecular networks is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Although systems biology allows the gene network of a cell to be reconstructed from clinical gene expression data, traditional methods, such as Ba...

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Autores principales: Shimamura, Teppei, Imoto, Seiya, Shimada, Yukako, Hosono, Yasuyuki, Niida, Atsushi, Nagasaki, Masao, Yamaguchi, Rui, Takahashi, Takashi, Miyano, Satoru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3110206/
https://www.ncbi.nlm.nih.gov/pubmed/21687740
http://dx.doi.org/10.1371/journal.pone.0020804
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author Shimamura, Teppei
Imoto, Seiya
Shimada, Yukako
Hosono, Yasuyuki
Niida, Atsushi
Nagasaki, Masao
Yamaguchi, Rui
Takahashi, Takashi
Miyano, Satoru
author_facet Shimamura, Teppei
Imoto, Seiya
Shimada, Yukako
Hosono, Yasuyuki
Niida, Atsushi
Nagasaki, Masao
Yamaguchi, Rui
Takahashi, Takashi
Miyano, Satoru
author_sort Shimamura, Teppei
collection PubMed
description Patient-specific analysis of molecular networks is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Although systems biology allows the gene network of a cell to be reconstructed from clinical gene expression data, traditional methods, such as Bayesian networks, only provide an averaged network for all samples. Therefore, these methods cannot reveal patient-specific differences in molecular networks during cancer progression. In this study, we developed a novel statistical method called NetworkProfiler, which infers patient-specific gene regulatory networks for a specific clinical characteristic, such as cancer progression, from gene expression data of cancer patients. We applied NetworkProfiler to microarray gene expression data from 762 cancer cell lines and extracted the system changes that were related to the epithelial-mesenchymal transition (EMT). Out of 1732 possible regulators of E-cadherin, a cell adhesion molecule that modulates the EMT, NetworkProfiler, identified 25 candidate regulators, of which about half have been experimentally verified in the literature. In addition, we used NetworkProfiler to predict EMT-dependent master regulators that enhanced cell adhesion, migration, invasion, and metastasis. In order to further evaluate the performance of NetworkProfiler, we selected Krueppel-like factor 5 (KLF5) from a list of the remaining candidate regulators of E-cadherin and conducted in vitro validation experiments. As a result, we found that knockdown of KLF5 by siRNA significantly decreased E-cadherin expression and induced morphological changes characteristic of EMT. In addition, in vitro experiments of a novel candidate EMT-related microRNA, miR-100, confirmed the involvement of miR-100 in several EMT-related aspects, which was consistent with the predictions obtained by NetworkProfiler.
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spelling pubmed-31102062011-06-16 A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition Shimamura, Teppei Imoto, Seiya Shimada, Yukako Hosono, Yasuyuki Niida, Atsushi Nagasaki, Masao Yamaguchi, Rui Takahashi, Takashi Miyano, Satoru PLoS One Research Article Patient-specific analysis of molecular networks is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Although systems biology allows the gene network of a cell to be reconstructed from clinical gene expression data, traditional methods, such as Bayesian networks, only provide an averaged network for all samples. Therefore, these methods cannot reveal patient-specific differences in molecular networks during cancer progression. In this study, we developed a novel statistical method called NetworkProfiler, which infers patient-specific gene regulatory networks for a specific clinical characteristic, such as cancer progression, from gene expression data of cancer patients. We applied NetworkProfiler to microarray gene expression data from 762 cancer cell lines and extracted the system changes that were related to the epithelial-mesenchymal transition (EMT). Out of 1732 possible regulators of E-cadherin, a cell adhesion molecule that modulates the EMT, NetworkProfiler, identified 25 candidate regulators, of which about half have been experimentally verified in the literature. In addition, we used NetworkProfiler to predict EMT-dependent master regulators that enhanced cell adhesion, migration, invasion, and metastasis. In order to further evaluate the performance of NetworkProfiler, we selected Krueppel-like factor 5 (KLF5) from a list of the remaining candidate regulators of E-cadherin and conducted in vitro validation experiments. As a result, we found that knockdown of KLF5 by siRNA significantly decreased E-cadherin expression and induced morphological changes characteristic of EMT. In addition, in vitro experiments of a novel candidate EMT-related microRNA, miR-100, confirmed the involvement of miR-100 in several EMT-related aspects, which was consistent with the predictions obtained by NetworkProfiler. Public Library of Science 2011-06-07 /pmc/articles/PMC3110206/ /pubmed/21687740 http://dx.doi.org/10.1371/journal.pone.0020804 Text en Shimamura et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Shimamura, Teppei
Imoto, Seiya
Shimada, Yukako
Hosono, Yasuyuki
Niida, Atsushi
Nagasaki, Masao
Yamaguchi, Rui
Takahashi, Takashi
Miyano, Satoru
A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition
title A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition
title_full A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition
title_fullStr A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition
title_full_unstemmed A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition
title_short A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition
title_sort novel network profiling analysis reveals system changes in epithelial-mesenchymal transition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3110206/
https://www.ncbi.nlm.nih.gov/pubmed/21687740
http://dx.doi.org/10.1371/journal.pone.0020804
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