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Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer
Gastric cancer (GC) is the third leading cause of cancer deaths and the fourth most prevalent malignancy worldwide. The high incidence and mortality rates of gastric cancer result from multiple factors such as ineffective screening, diagnosis, and limited treatment options. In our study, we sought t...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004118/ https://www.ncbi.nlm.nih.gov/pubmed/31463974 http://dx.doi.org/10.1002/ijc.32643 |
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author | Song, Won‐Min Lin, Xiandong Liao, Xuehong Hu, Dan Lin, Jieqiong Sarpel, Umut Ye, Yunbin Feferman, Yael Labow, Daniel M. Walsh, Martin J. Zheng, Xiongwei Zhang, Bin |
author_facet | Song, Won‐Min Lin, Xiandong Liao, Xuehong Hu, Dan Lin, Jieqiong Sarpel, Umut Ye, Yunbin Feferman, Yael Labow, Daniel M. Walsh, Martin J. Zheng, Xiongwei Zhang, Bin |
author_sort | Song, Won‐Min |
collection | PubMed |
description | Gastric cancer (GC) is the third leading cause of cancer deaths and the fourth most prevalent malignancy worldwide. The high incidence and mortality rates of gastric cancer result from multiple factors such as ineffective screening, diagnosis, and limited treatment options. In our study, we sought to systematically identify predictive molecular networks and key regulators to elucidate complex interacting signaling pathways in GC. We performed an integrative network analysis of the transcriptomic data in The Cancer Genome Atlas (TCGA) gastric cancer cohort and then comprehensively characterized the predictive subnetworks and key regulators by the matched genetic and epigenetic data. We identified 221 gene subnetworks (modules) in GC. The most prognostic subnetworks captured multiple aspects of the tumor microenvironment in GC involving interactions among stromal, epithelial and immune cells. We revealed the genetic and epigenetic underpinnings of those subnetworks and their key transcriptional regulators. We computationally predicted and experimentally validated specific mechanisms of anticancer effects of GKN2 in gastric cancer proliferation and invasion in vitro. The network models and the key regulators of the tumor microenvironment in GC identified here pave a way for developing novel therapeutic strategies for GC. |
format | Online Article Text |
id | pubmed-7004118 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70041182020-02-11 Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer Song, Won‐Min Lin, Xiandong Liao, Xuehong Hu, Dan Lin, Jieqiong Sarpel, Umut Ye, Yunbin Feferman, Yael Labow, Daniel M. Walsh, Martin J. Zheng, Xiongwei Zhang, Bin Int J Cancer Cancer Genetics and Epigenetics Gastric cancer (GC) is the third leading cause of cancer deaths and the fourth most prevalent malignancy worldwide. The high incidence and mortality rates of gastric cancer result from multiple factors such as ineffective screening, diagnosis, and limited treatment options. In our study, we sought to systematically identify predictive molecular networks and key regulators to elucidate complex interacting signaling pathways in GC. We performed an integrative network analysis of the transcriptomic data in The Cancer Genome Atlas (TCGA) gastric cancer cohort and then comprehensively characterized the predictive subnetworks and key regulators by the matched genetic and epigenetic data. We identified 221 gene subnetworks (modules) in GC. The most prognostic subnetworks captured multiple aspects of the tumor microenvironment in GC involving interactions among stromal, epithelial and immune cells. We revealed the genetic and epigenetic underpinnings of those subnetworks and their key transcriptional regulators. We computationally predicted and experimentally validated specific mechanisms of anticancer effects of GKN2 in gastric cancer proliferation and invasion in vitro. The network models and the key regulators of the tumor microenvironment in GC identified here pave a way for developing novel therapeutic strategies for GC. John Wiley & Sons, Inc. 2019-10-11 2020-03-01 /pmc/articles/PMC7004118/ /pubmed/31463974 http://dx.doi.org/10.1002/ijc.32643 Text en © 2019 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Cancer Genetics and Epigenetics Song, Won‐Min Lin, Xiandong Liao, Xuehong Hu, Dan Lin, Jieqiong Sarpel, Umut Ye, Yunbin Feferman, Yael Labow, Daniel M. Walsh, Martin J. Zheng, Xiongwei Zhang, Bin Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer |
title | Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer |
title_full | Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer |
title_fullStr | Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer |
title_full_unstemmed | Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer |
title_short | Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer |
title_sort | multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer |
topic | Cancer Genetics and Epigenetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004118/ https://www.ncbi.nlm.nih.gov/pubmed/31463974 http://dx.doi.org/10.1002/ijc.32643 |
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