Differential C3NET reveals disease networks of direct physical interactions.
Differential C3NET reveals disease networks of direct physical interactions.
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DOI：
10.1186/1471-2105-12-296
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发表时间：
2011-07-21
影响因子：
3
通讯作者：
Neal DE
中科院分区：
文献类型：
--
作者：
Altay G;Asim M;Markowetz F;Neal DE
基因在不同的细胞条件下可能有不同的基因相互作用，这可能会映射到不同的网络中。基因网络的差异分析允许发现特定条件的相互作用，例如，如果条件是疾病，如癌症，则形成疾病网络。这可能有助于开发更好的和微妙的靶向药物来治疗癌症。直接物理基因相互作用的差异网络分析需要在这方面的努力进行探索。C3NET是最近推出的基于信息论的基因网络推理算法，它从表达数据中推断直接的物理基因相互作用，在各种网络上的推理性能始终高于其竞争对手。在本文中，我们提出，DC3net，采用C3NET在推断疾病网络的方法。我们将DC3net应用于合成和真实的前列腺癌数据集，显示出有希望的结果。在宽松的截止值下，我们预测了肿瘤和正常样品总共18583种相互作用。虽然在文献中没有针对我们样品的特定条件的参考相互作用数据库，但我们发现仅从四个生物相互作用数据库中验证了我们预测的54个直接物理相互作用。作为一个例子，我们预测RAD50与TRF 2具有前列腺癌特异性相互作用，结果证明具有来自文献的验证。已知RAD50复合物在细胞周期的S期与TRF2结合，这表明这种预测的相互作用可能促进肿瘤细胞中的端粒维持，以允许肿瘤细胞无限分裂。我们的富集分析表明，所确定的肿瘤特异性基因相互作用可能在驱动前列腺癌生长方面具有潜在的重要性。此外，我们发现我们预测的肿瘤特异性网络的最高连接子网络富含所有增殖基因，这进一步表明该网络中的基因可能在肿瘤发生过程中发挥作用。我们的方法揭示了疾病特异性相互作用。通过优先考虑全球基因网络的疾病相关部分，这可能有助于使实验性后续研究更具成本和时间效率。
Genes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour. C3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis. Our approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network.
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影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
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