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BMC Cancer
Published in: BMC Cancer 1/2015

Open Access 01-12-2015 | Research article

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

Authors: Yan Li, Anatoliy A. Melnikov, Victor Levenson, Emanuela Guerra, Pasquale Simeone, Saverio Alberti, Youping Deng

Published in: BMC Cancer | Issue 1/2015

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Abstract

Background

DNA 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.

Methods

CpG-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.

Results

Seven 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.

Conclusions

Our findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings.
Appendix
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Metadata
Title
A seven-gene CpG-island methylation panel predicts breast cancer progression
Authors
Yan Li
Anatoliy A. Melnikov
Victor Levenson
Emanuela Guerra
Pasquale Simeone
Saverio Alberti
Youping Deng
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2015
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-015-1412-9

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