A seven-gene CpG-island methylation panel predicts breast cancer progression

BMC Cancer

Research Article

Youping Deng¹  Yan Li¹  Anatoliy A. Melnikov²  Victor Levenson³  Pasquale Simeone⁴  Emanuela Guerra⁴  Saverio Alberti⁵ 

[1] Rush University Medical Center, 653 W Congress Pkwy, 60612, Chicago, IL, USA;US Biomarkers, Inc, 29 Buckingham Ln., 60089, Buffalo Grove, IL, USA;US Biomarkers, Inc, 29 Buckingham Ln., 60089, Buffalo Grove, IL, USA;Currently at Center for Translational Research, Catholic Health Initiatives, Englewood, USA;Unit of Cancer Pathology, CeSI, ‘G. d’Annunzio’ University Foundation, Via L. Polacchi 11, 66100, Chieti, Italy;Unit of Cancer Pathology, CeSI, ‘G. d’Annunzio’ University Foundation, Via L. Polacchi 11, 66100, Chieti, Italy;Department of Neuroscience, Imaging and Clinical Sciences, Unit of Physiology and Physiopathology, ‘G. d’Annunzio’ University, Via dei Vestini, 66100, Chieti, Italy;

关键词: Breast cancer;    DNA methylation;    Microarray;    Metastatic relapse;    Prognostic indicators;   

DOI  :  10.1186/s12885-015-1412-9

 received in 2014-12-19, accepted in 2015-05-01,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundDNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors.MethodsCpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes.ResultsSeven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression.ConclusionsOur findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings.

【 授权许可】

Unknown   
© Li et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.