Abstract
BackgroundThe 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.MethodsA 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.ResultsGenes 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.ConclusionsBy 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.
Highlights
The genetic control of prostate cancer development is poorly understood
We have assumed that combining meta-analyses of different steps of prostate carcinogenesis and assigning greater weight to more-specific data might improve identification of genes associated with prostate tumorigenesis, especially those involved in the development of bone metastases; we have up-weighted the third level of meta-analysis
We found that the Z scores derived from the comparison of normal and localized tumor tissue of bone-metastasizing cancers were much more strongly correlated with those of the prostate cancers than they were with those of the non-bone metastasizing cancers: Pearson’s correlation coefficients for breast vs. prostate cancer were 0.29, n = 9,824; for lung vs. prostate cancer, 0.36, n = 10,824; for colon vs. prostate cancer, 0.21, n = 12,756; and for ovarian vs. prostate cancer, 0.22, n = 11,984
Summary
Large numbers of geneexpression 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. Gene-expression profiling has been extensively used to classify cancers by gene-expression signatures [1,2,3]. It has been used for predicting response to treatment [4,5,6] and prognosis [7,8,9]. Version 3.6, as accessed on January 5, 2010, comprised
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