Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures
- Autores
- Abba, Martín Carlos; Lacunza, Ezequiel; Butti, Matías; Aldaz, C. Marcelo
- Año de publicación
- 2010
- Idioma
- inglés
- Tipo de recurso
- reseña artículo
- Estado
- versión publicada
- Descripción
- In this review we provide a systematic analysis of transcriptomic signatures derived from 42 breast cancer gene expression studies, in an effort to identify the most relevant breast cancer biomarkers using a meta-analysis method. Meta-data revealed a set of 117 genes that were the most commonly affected ranging from 12% to 36% of overlap among breast cancer gene expression studies. Data mining analysis of transcripts and protein-protein interactions of these commonly modulated genes indicate three functional modules significantly affected among signatures, one module related with the response to steroid hormone stimulus, and two modules related to the cell cycle. Analysis of a publicly available gene expression data showed that the obtained meta-signature is capable of predicting overall survival (P < 0.0001) and relapse-free survival (P < 0.0001) in patients with early-stage breast carcinomas. In addition, the identified meta-signature improves breast cancer patient stratification independently of traditional prognostic factors in a multivariate Cox proportional-hazards analysis.
- Materia
-
Ciencias Médicas
breast cancer
biomarkers
gene expression signatures - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/6149
Ver los metadatos del registro completo
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Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression SignaturesAbba, Martín CarlosLacunza, EzequielButti, MatíasAldaz, C. MarceloCiencias Médicasbreast cancerbiomarkersgene expression signaturesIn this review we provide a systematic analysis of transcriptomic signatures derived from 42 breast cancer gene expression studies, in an effort to identify the most relevant breast cancer biomarkers using a meta-analysis method. Meta-data revealed a set of 117 genes that were the most commonly affected ranging from 12% to 36% of overlap among breast cancer gene expression studies. Data mining analysis of transcripts and protein-protein interactions of these commonly modulated genes indicate three functional modules significantly affected among signatures, one module related with the response to steroid hormone stimulus, and two modules related to the cell cycle. Analysis of a publicly available gene expression data showed that the obtained meta-signature is capable of predicting overall survival (P < 0.0001) and relapse-free survival (P < 0.0001) in patients with early-stage breast carcinomas. In addition, the identified meta-signature improves breast cancer patient stratification independently of traditional prognostic factors in a multivariate Cox proportional-hazards analysis.2010-10info:eu-repo/semantics/reviewinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_ba08info:ar-repo/semantics/revisionLiterariaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/6149enginfo:eu-repo/semantics/altIdentifier/doi/10.4137/BMI.S5740info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-04T09:43:12Zoai:digital.cic.gba.gob.ar:11746/6149Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-04 09:43:13.14CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse |
dc.title.none.fl_str_mv |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures |
title |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures |
spellingShingle |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures Abba, Martín Carlos Ciencias Médicas breast cancer biomarkers gene expression signatures |
title_short |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures |
title_full |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures |
title_fullStr |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures |
title_full_unstemmed |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures |
title_sort |
Breast Cancer Biomarker Discovery in the Functional Genomic Age: A Systematic Review of 42 Gene Expression Signatures |
dc.creator.none.fl_str_mv |
Abba, Martín Carlos Lacunza, Ezequiel Butti, Matías Aldaz, C. Marcelo |
author |
Abba, Martín Carlos |
author_facet |
Abba, Martín Carlos Lacunza, Ezequiel Butti, Matías Aldaz, C. Marcelo |
author_role |
author |
author2 |
Lacunza, Ezequiel Butti, Matías Aldaz, C. Marcelo |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Ciencias Médicas breast cancer biomarkers gene expression signatures |
topic |
Ciencias Médicas breast cancer biomarkers gene expression signatures |
dc.description.none.fl_txt_mv |
In this review we provide a systematic analysis of transcriptomic signatures derived from 42 breast cancer gene expression studies, in an effort to identify the most relevant breast cancer biomarkers using a meta-analysis method. Meta-data revealed a set of 117 genes that were the most commonly affected ranging from 12% to 36% of overlap among breast cancer gene expression studies. Data mining analysis of transcripts and protein-protein interactions of these commonly modulated genes indicate three functional modules significantly affected among signatures, one module related with the response to steroid hormone stimulus, and two modules related to the cell cycle. Analysis of a publicly available gene expression data showed that the obtained meta-signature is capable of predicting overall survival (P < 0.0001) and relapse-free survival (P < 0.0001) in patients with early-stage breast carcinomas. In addition, the identified meta-signature improves breast cancer patient stratification independently of traditional prognostic factors in a multivariate Cox proportional-hazards analysis. |
description |
In this review we provide a systematic analysis of transcriptomic signatures derived from 42 breast cancer gene expression studies, in an effort to identify the most relevant breast cancer biomarkers using a meta-analysis method. Meta-data revealed a set of 117 genes that were the most commonly affected ranging from 12% to 36% of overlap among breast cancer gene expression studies. Data mining analysis of transcripts and protein-protein interactions of these commonly modulated genes indicate three functional modules significantly affected among signatures, one module related with the response to steroid hormone stimulus, and two modules related to the cell cycle. Analysis of a publicly available gene expression data showed that the obtained meta-signature is capable of predicting overall survival (P < 0.0001) and relapse-free survival (P < 0.0001) in patients with early-stage breast carcinomas. In addition, the identified meta-signature improves breast cancer patient stratification independently of traditional prognostic factors in a multivariate Cox proportional-hazards analysis. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/review info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_ba08 info:ar-repo/semantics/revisionLiteraria |
format |
review |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://digital.cic.gba.gob.ar/handle/11746/6149 |
url |
https://digital.cic.gba.gob.ar/handle/11746/6149 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.4137/BMI.S5740 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
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CIC Digital (CICBA) |
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CIC Digital (CICBA) |
instname_str |
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
instacron_str |
CICBA |
institution |
CICBA |
repository.name.fl_str_mv |
CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
repository.mail.fl_str_mv |
marisa.degiusti@sedici.unlp.edu.ar |
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12.623145 |