Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
BMC Cancer. 2010 Nov 2;10:599. doi: 10.1186/1471-2407-10-599.
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.
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.
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.
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.
前列腺癌发展的遗传控制机制尚不清楚。目前有大量关于前列腺肿瘤发生的不同方面的基因表达数据集。我们利用这些数据来识别和优先考虑与前列腺癌和骨转移发展相关的候选基因。我们的工作假设是，对前列腺肿瘤发生的不同但重叠步骤进行荟萃分析，将有助于识别与前列腺癌发展相关的基因。
采用基于 Z 分数的基因表达数据荟萃分析来识别与前列腺癌发展相关的候选基因。为了将不同的数据集组合在一起，我们按照前列腺癌发展的自然史进行了 3 个层次的荟萃分析。为了对候选基因进行实验验证，我们使用了计算机模拟验证和内部基因表达数据。
具有与前列腺癌发展相关的实验证据的基因在我们的候选基因中占很大比例。荟萃分析还确定了相当数量的新候选基因，这些基因在前列腺癌发展方面没有发表的证据。功能注释确定细胞骨架、细胞黏附、细胞外基质和细胞运动是与前列腺癌发展相关的前 5 个主要功能。我们确定了 10 个基因——CDC2、CCNA2、IGF1、EGR1、SRF、CTGF、CCL2、CAV1、SMAD4 和 AURKA——它们形成了相互作用网络的枢纽，因此可能是前列腺癌发展的主要驱动因素。
通过对基因表达数据进行大型 3 级荟萃分析来识别与前列腺癌发展相关的候选基因，我们生成了一个候选基因列表，这些基因可能是研究前列腺癌发展分子机制的研究人员的有用资源。
