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Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models
Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219783/ https://www.ncbi.nlm.nih.gov/pubmed/25368990 http://dx.doi.org/10.1371/journal.pone.0111813 |
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author | Geiger, Thomas R. Ha, Ngoc-Han Faraji, Farhoud Michael, Helen T. Rodriguez, Loren Walker, Renard C. Green, Jeffery E. Simpson, R. Mark Hunter, Kent W. |
author_facet | Geiger, Thomas R. Ha, Ngoc-Han Faraji, Farhoud Michael, Helen T. Rodriguez, Loren Walker, Renard C. Green, Jeffery E. Simpson, R. Mark Hunter, Kent W. |
author_sort | Geiger, Thomas R. |
collection | PubMed |
description | Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+) breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease. |
format | Online Article Text |
id | pubmed-4219783 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-42197832014-11-12 Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models Geiger, Thomas R. Ha, Ngoc-Han Faraji, Farhoud Michael, Helen T. Rodriguez, Loren Walker, Renard C. Green, Jeffery E. Simpson, R. Mark Hunter, Kent W. PLoS One Research Article Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+) breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease. Public Library of Science 2014-11-04 /pmc/articles/PMC4219783/ /pubmed/25368990 http://dx.doi.org/10.1371/journal.pone.0111813 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Geiger, Thomas R. Ha, Ngoc-Han Faraji, Farhoud Michael, Helen T. Rodriguez, Loren Walker, Renard C. Green, Jeffery E. Simpson, R. Mark Hunter, Kent W. Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models |
title | Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models |
title_full | Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models |
title_fullStr | Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models |
title_full_unstemmed | Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models |
title_short | Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models |
title_sort | functional analysis of prognostic gene expression network genes in metastatic breast cancer models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219783/ https://www.ncbi.nlm.nih.gov/pubmed/25368990 http://dx.doi.org/10.1371/journal.pone.0111813 |
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