Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer

<p>Abstract</p> <p>Background</p> <p>Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has b...

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Main Authors: Tan Qihua, Thomassen Mads, Kruse Torben A
Format: Article
Language:English
Published: BMC 2008-12-01
Series:BMC Cancer
Online Access:http://www.biomedcentral.com/1471-2407/8/394
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spelling doaj-d067828dd9f94793ae82d48fa6dfc0ad2020-11-24T21:52:49ZengBMCBMC Cancer1471-24072008-12-018139410.1186/1471-2407-8-394Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancerTan QihuaThomassen MadsKruse Torben A<p>Abstract</p> <p>Background</p> <p>Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has been used to predict outcome in recent studies. Besides classification of outcome, these global expression patterns may reflect biological mechanisms involved in metastasis of breast cancer. Our purpose has been to investigate pathways and transcription factors involved in metastasis by use of gene expression data sets.</p> <p>Methods</p> <p>We have analyzed 8 publicly available gene expression data sets. A global approach, "gene set enrichment analysis" as well as an approach focusing on a subset of significantly differently regulated genes, GenMAPP, has been applied to rank pathway gene sets according to differential regulation in metastasizing tumors compared to non-metastasizing tumors. Meta-analysis has been used to determine overrepresentation of pathways and transcription factors targets, concordant deregulated in metastasizing breast tumors, in several data sets.</p> <p>Results</p> <p>The major findings are up-regulation of cell cycle pathways and a metabolic shift towards glucose metabolism reflected in several pathways in metastasizing tumors. Growth factor pathways seem to play dual roles; EGF and PDGF pathways are decreased, while VEGF and sex-hormone pathways are increased in tumors that metastasize. Furthermore, migration, proteasome, immune system, angiogenesis, DNA repair and several signal transduction pathways are associated to metastasis. Finally several transcription factors e.g. E2F, NFY, and YY1 are identified as being involved in metastasis.</p> <p>Conclusion</p> <p>By pathway meta-analysis many biological mechanisms beyond major characteristics such as proliferation are identified. Transcription factor analysis identifies a number of key factors that support central pathways. Several previously proposed treatment targets are identified and several new pathways that may constitute new targets are identified.</p> http://www.biomedcentral.com/1471-2407/8/394
collection DOAJ
language English
format Article
sources DOAJ
author Tan Qihua
Thomassen Mads
Kruse Torben A
spellingShingle Tan Qihua
Thomassen Mads
Kruse Torben A
Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer
BMC Cancer
author_facet Tan Qihua
Thomassen Mads
Kruse Torben A
author_sort Tan Qihua
title Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer
title_short Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer
title_full Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer
title_fullStr Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer
title_full_unstemmed Gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer
title_sort gene expression meta-analysis identifies metastatic pathways and transcription factors in breast cancer
publisher BMC
series BMC Cancer
issn 1471-2407
publishDate 2008-12-01
description <p>Abstract</p> <p>Background</p> <p>Metastasis is believed to progress in several steps including different pathways but the determination and understanding of these mechanisms is still fragmentary. Microarray analysis of gene expression patterns in breast tumors has been used to predict outcome in recent studies. Besides classification of outcome, these global expression patterns may reflect biological mechanisms involved in metastasis of breast cancer. Our purpose has been to investigate pathways and transcription factors involved in metastasis by use of gene expression data sets.</p> <p>Methods</p> <p>We have analyzed 8 publicly available gene expression data sets. A global approach, "gene set enrichment analysis" as well as an approach focusing on a subset of significantly differently regulated genes, GenMAPP, has been applied to rank pathway gene sets according to differential regulation in metastasizing tumors compared to non-metastasizing tumors. Meta-analysis has been used to determine overrepresentation of pathways and transcription factors targets, concordant deregulated in metastasizing breast tumors, in several data sets.</p> <p>Results</p> <p>The major findings are up-regulation of cell cycle pathways and a metabolic shift towards glucose metabolism reflected in several pathways in metastasizing tumors. Growth factor pathways seem to play dual roles; EGF and PDGF pathways are decreased, while VEGF and sex-hormone pathways are increased in tumors that metastasize. Furthermore, migration, proteasome, immune system, angiogenesis, DNA repair and several signal transduction pathways are associated to metastasis. Finally several transcription factors e.g. E2F, NFY, and YY1 are identified as being involved in metastasis.</p> <p>Conclusion</p> <p>By pathway meta-analysis many biological mechanisms beyond major characteristics such as proliferation are identified. Transcription factor analysis identifies a number of key factors that support central pathways. Several previously proposed treatment targets are identified and several new pathways that may constitute new targets are identified.</p>
url http://www.biomedcentral.com/1471-2407/8/394
work_keys_str_mv AT tanqihua geneexpressionmetaanalysisidentifiesmetastaticpathwaysandtranscriptionfactorsinbreastcancer
AT thomassenmads geneexpressionmetaanalysisidentifiesmetastaticpathwaysandtranscriptionfactorsinbreastcancer
AT krusetorbena geneexpressionmetaanalysisidentifiesmetastaticpathwaysandtranscriptionfactorsinbreastcancer
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