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Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

BACKGROUND: Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. METHODS: Gene signatures to predict the development of me...

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Autores principales: Yu, Jack X, Sieuwerts, Anieta M, Zhang, Yi, Martens, John WM, Smid, Marcel, Klijn, Jan GM, Wang, Yixin, Foekens, John A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2077336/
https://www.ncbi.nlm.nih.gov/pubmed/17894856
http://dx.doi.org/10.1186/1471-2407-7-182
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author Yu, Jack X
Sieuwerts, Anieta M
Zhang, Yi
Martens, John WM
Smid, Marcel
Klijn, Jan GM
Wang, Yixin
Foekens, John A
author_facet Yu, Jack X
Sieuwerts, Anieta M
Zhang, Yi
Martens, John WM
Smid, Marcel
Klijn, Jan GM
Wang, Yixin
Foekens, John A
author_sort Yu, Jack X
collection PubMed
description BACKGROUND: Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. METHODS: Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. RESULTS: The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. CONCLUSION: We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different.
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spelling pubmed-20773362007-11-14 Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer Yu, Jack X Sieuwerts, Anieta M Zhang, Yi Martens, John WM Smid, Marcel Klijn, Jan GM Wang, Yixin Foekens, John A BMC Cancer Research Article BACKGROUND: Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. METHODS: Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. RESULTS: The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. CONCLUSION: We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different. BioMed Central 2007-09-25 /pmc/articles/PMC2077336/ /pubmed/17894856 http://dx.doi.org/10.1186/1471-2407-7-182 Text en Copyright © 2007 Yu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yu, Jack X
Sieuwerts, Anieta M
Zhang, Yi
Martens, John WM
Smid, Marcel
Klijn, Jan GM
Wang, Yixin
Foekens, John A
Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
title Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
title_full Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
title_fullStr Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
title_full_unstemmed Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
title_short Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
title_sort pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2077336/
https://www.ncbi.nlm.nih.gov/pubmed/17894856
http://dx.doi.org/10.1186/1471-2407-7-182
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