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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| Veröffentlicht in: | PloS one 2014-11, Vol.9 (11), p.e111813-e111813 |
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| creator | 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 |
| 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. |
| doi_str_mv | 10.1371/journal.pone.0111813 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0111813</identifier><identifier>PMID: 25368990</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Animals ; Apoptosis ; Biology ; Biology and Life Sciences ; Breast cancer ; Breast Neoplasms - genetics ; Breast Neoplasms - metabolism ; Breast Neoplasms - pathology ; Cancer ; Cell cycle ; Cell Cycle Proteins - genetics ; Cell Cycle Proteins - metabolism ; Cell growth ; Cell Line, Tumor ; Cell Movement ; Cell Proliferation ; Cellular communication ; Clustering ; Epithelial-Mesenchymal Transition ; Estrogen receptors ; Estrogens ; Etiology ; Female ; Functional analysis ; Gene expression ; Gene Knockdown Techniques ; Gene Regulatory Networks ; Genes ; Genomes ; HEK293 Cells ; Humans ; Kinases ; Laboratories ; Lung Neoplasms - genetics ; Lung Neoplasms - metabolism ; Lung Neoplasms - secondary ; Medical research ; Medicine and Health Sciences ; Metastases ; Metastasis ; Mice ; Microtubule-Associated Proteins - genetics ; Microtubule-Associated Proteins - metabolism ; Neoplasm Transplantation ; Network analysis ; Nuclear Proteins - genetics ; Nuclear Proteins - metabolism ; Prognosis ; Proteins ; Transcriptome ; Tumor Burden ; Tumor cells ; Tumors</subject><ispartof>PloS one, 2014-11, Vol.9 (11), p.e111813-e111813</ispartof><rights>2014. This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-b10ea46727121b085071522d7a9f04f92fdb1d1a7088d284c28d47c46a86ef543</citedby><cites>FETCH-LOGICAL-c526t-b10ea46727121b085071522d7a9f04f92fdb1d1a7088d284c28d47c46a86ef543</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219783/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219783/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23864,27922,27923,53789,53791,79370,79371</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25368990$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Samant, Rajeev</contributor><creatorcontrib>Geiger, Thomas R</creatorcontrib><creatorcontrib>Ha, Ngoc-Han</creatorcontrib><creatorcontrib>Faraji, Farhoud</creatorcontrib><creatorcontrib>Michael, Helen T</creatorcontrib><creatorcontrib>Rodriguez, Loren</creatorcontrib><creatorcontrib>Walker, Renard C</creatorcontrib><creatorcontrib>Green, Jeffery E</creatorcontrib><creatorcontrib>Simpson, R Mark</creatorcontrib><creatorcontrib>Hunter, Kent W</creatorcontrib><title>Functional analysis of prognostic gene expression network genes in metastatic breast cancer models</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. 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| subjects | Animals Apoptosis Biology Biology and Life Sciences Breast cancer Breast Neoplasms - genetics Breast Neoplasms - metabolism Breast Neoplasms - pathology Cancer Cell cycle Cell Cycle Proteins - genetics Cell Cycle Proteins - metabolism Cell growth Cell Line, Tumor Cell Movement Cell Proliferation Cellular communication Clustering Epithelial-Mesenchymal Transition Estrogen receptors Estrogens Etiology Female Functional analysis Gene expression Gene Knockdown Techniques Gene Regulatory Networks Genes Genomes HEK293 Cells Humans Kinases Laboratories Lung Neoplasms - genetics Lung Neoplasms - metabolism Lung Neoplasms - secondary Medical research Medicine and Health Sciences Metastases Metastasis Mice Microtubule-Associated Proteins - genetics Microtubule-Associated Proteins - metabolism Neoplasm Transplantation Network analysis Nuclear Proteins - genetics Nuclear Proteins - metabolism Prognosis Proteins Transcriptome Tumor Burden Tumor cells Tumors |
| title | Functional analysis of prognostic gene expression network genes in metastatic breast cancer models |
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