Open Access Highly Accessed Research article

Prioritizing genes associated with prostate cancer development

Ivan P Gorlov1*, Kanishka Sircar2, Hongya Zhao15, Sankar N Maity1, Nora M Navone1, Olga Y Gorlova3, Patricia Troncoso2, Curtis A Pettaway4, Jin Young Byun1 and Christopher J Logothetis1

Author Affiliations

1 Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas

2 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas

3 Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas

4 Department of Urology, The University of Texas MD Anderson Cancer Center, Houston, Texas

5 Institute of Advanced Computing and Digital Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

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BMC Cancer 2010, 10:599  doi:10.1186/1471-2407-10-599

Published: 2 November 2010

Abstract

Background

The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.

Methods

A Z score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.

Results

Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4, and AURKA--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.

Conclusions

By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.