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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
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.
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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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