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BMC Cancer
Published in: BMC Cancer 1/2008

Open Access 01-12-2008 | Research article

A resampling-based meta-analysis for detection of differential gene expression in breast cancer

Authors: Bala Gur-Dedeoglu, Ozlen Konu, Serkan Kir, Ahmet Rasit Ozturk, Betul Bozkurt, Gulusan Ergul, Isik G Yulug

Published in: BMC Cancer | Issue 1/2008

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Abstract

Background

Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures.

Methods

A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis.

Results

The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively). The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results.

Conclusion

The proposed meta-analysis approach has the ability to detect a set of differentially expressed genes with the least amount of within-group variability, thus providing highly stable gene lists for class prediction. Increased statistical power and stringent filtering criteria used in the present study also make identification of novel candidate genes possible and may provide further insight to improve our understanding of breast cancer development.
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Metadata
Title
A resampling-based meta-analysis for detection of differential gene expression in breast cancer
Authors
Bala Gur-Dedeoglu
Ozlen Konu
Serkan Kir
Ahmet Rasit Ozturk
Betul Bozkurt
Gulusan Ergul
Isik G Yulug
Publication date
01-12-2008
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2008
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/1471-2407-8-396

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