Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mustacchi, Giorgio, Sormani, Maria Pia, Bruzzi, Paolo, Gennari, Alessandra, Zanconati, Fabrizio, Bonifacio, Daniela, Monzoni, Adriana, Morandi, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2013
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
_version_ 1782272670047404032
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
work_keys_str_mv AT mustacchigiorgio identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer
AT sormanimariapia identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer
AT bruzzipaolo identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer
AT gennarialessandra identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer
AT zanconatifabrizio identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer
AT bonifaciodaniela identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer
AT monzoniadriana identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer
AT morandiluca identificationandvalidationofanewsetoffivegenesforpredictionofriskinearlybreastcancer