Prioritizing genes associated with prostate cancer development - PubMed (original) (raw)

Meta-Analysis

Prioritizing genes associated with prostate cancer development

Ivan P Gorlov et al. BMC Cancer. 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.

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Figures

Figure 1

Figure 1

Scatter plot of bone- and non-bone metastasizing cancers. The blue line is a linear regression; the red line is a moving average computed for 100 adjacent genes.

Figure 2

Figure 2

Relative Z score for genes with published evidence of an association with the development of bone metastases based on the KnowledgeNet approach (KN) and for genes from candidate pathways (CP). Relative Z scores were computed as the ratio between the Z score for candidate genes and the overall average Z score. Vertical bars represent standard error (SE).

Figure 3

Figure 3

Distributions of Z scores from the third-level analysis. (A) The green line represents the distribution of all Z scores; the blue line, the distribution of Z scores for the genes found to be down-regulated in the study by Chandran et al. [20]; and the red line, the distribution of genes found to be up-regulated in Chandran's study. (B) Distribution of Z scores of genes found to be significantly up- and down-regulated between primary prostate tumor and bone metastases; study by Stanbrough et al., 2006 [21].

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