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Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer
Molecular tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after consideration of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing severa...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3676806/ https://www.ncbi.nlm.nih.gov/pubmed/23648477 http://dx.doi.org/10.3390/ijms14059686 |
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author | Mustacchi, Giorgio Sormani, Maria Pia Bruzzi, Paolo Gennari, Alessandra Zanconati, Fabrizio Bonifacio, Daniela Monzoni, Adriana Morandi, Luca |
author_facet | Mustacchi, Giorgio Sormani, Maria Pia Bruzzi, Paolo Gennari, Alessandra Zanconati, Fabrizio Bonifacio, Daniela Monzoni, Adriana Morandi, Luca |
author_sort | Mustacchi, Giorgio |
collection | PubMed |
description | Molecular tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after consideration of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing several publicly available array gene expression data using R/Bioconductor package. Using RTqPCR we evaluate 261 consecutive invasive breast cancer cases not selected for age, adjuvant treatment, nodal and estrogen receptor status from paraffin embedded sections. The biological samples dataset was split into a training (137 cases) and a validation set (124 cases). The gene signature was developed on the training set and a multivariate stepwise Cox analysis selected five genes independently associated with DFS: FGF18 (HR = 1.13, p = 0.05), BCL2 (HR = 0.57, p = 0.001), PRC1 (HR = 1.51, p = 0.001), MMP9 (HR = 1.11, p = 0.08), SERF1a (HR = 0.83, p = 0.007). These five genes were combined into a linear score (signature) weighted according to the coefficients of the Cox model, as: 0.125FGF18 − 0.560BCL2 + 0.409PRC1 + 0.104MMP9 − 0.188SERF1A (HR = 2.7, 95% CI = 1.9–4.0, p < 0.001). The signature was then evaluated on the validation set assessing the discrimination ability by a Kaplan Meier analysis, using the same cut offs classifying patients at low, intermediate or high risk of disease relapse as defined on the training set (p < 0.001). Our signature, after a further clinical validation, could be proposed as prognostic signature for disease free survival in breast cancer patients where the indication for adjuvant chemotherapy added to endocrine treatment is uncertain. |
format | Online Article Text |
id | pubmed-3676806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-36768062013-07-02 Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer Mustacchi, Giorgio Sormani, Maria Pia Bruzzi, Paolo Gennari, Alessandra Zanconati, Fabrizio Bonifacio, Daniela Monzoni, Adriana Morandi, Luca Int J Mol Sci Article Molecular tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after consideration of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing several publicly available array gene expression data using R/Bioconductor package. Using RTqPCR we evaluate 261 consecutive invasive breast cancer cases not selected for age, adjuvant treatment, nodal and estrogen receptor status from paraffin embedded sections. The biological samples dataset was split into a training (137 cases) and a validation set (124 cases). The gene signature was developed on the training set and a multivariate stepwise Cox analysis selected five genes independently associated with DFS: FGF18 (HR = 1.13, p = 0.05), BCL2 (HR = 0.57, p = 0.001), PRC1 (HR = 1.51, p = 0.001), MMP9 (HR = 1.11, p = 0.08), SERF1a (HR = 0.83, p = 0.007). These five genes were combined into a linear score (signature) weighted according to the coefficients of the Cox model, as: 0.125FGF18 − 0.560BCL2 + 0.409PRC1 + 0.104MMP9 − 0.188SERF1A (HR = 2.7, 95% CI = 1.9–4.0, p < 0.001). The signature was then evaluated on the validation set assessing the discrimination ability by a Kaplan Meier analysis, using the same cut offs classifying patients at low, intermediate or high risk of disease relapse as defined on the training set (p < 0.001). Our signature, after a further clinical validation, could be proposed as prognostic signature for disease free survival in breast cancer patients where the indication for adjuvant chemotherapy added to endocrine treatment is uncertain. Molecular Diversity Preservation International (MDPI) 2013-05-06 /pmc/articles/PMC3676806/ /pubmed/23648477 http://dx.doi.org/10.3390/ijms14059686 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland http://creativecommons.org/licenses/by/3.0 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Mustacchi, Giorgio Sormani, Maria Pia Bruzzi, Paolo Gennari, Alessandra Zanconati, Fabrizio Bonifacio, Daniela Monzoni, Adriana Morandi, Luca Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer |
title | Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer |
title_full | Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer |
title_fullStr | Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer |
title_full_unstemmed | Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer |
title_short | Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer |
title_sort | identification and validation of a new set of five genes for prediction of risk in early breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3676806/ https://www.ncbi.nlm.nih.gov/pubmed/23648477 http://dx.doi.org/10.3390/ijms14059686 |
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