Discovering cancer genes by integrating network and functional properties.
Discovering cancer genes by integrating network and functional properties.
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DOI：
10.1186/1755-8794-2-61
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发表时间：
2009-09-19
影响因子：
2.7
通讯作者：
Tang Z
中科院分区：
文献类型：
--
作者：
Li L;Zhang K;Lee J;Cordes S;Davis DP;Tang Z
发现新的致癌基因是癌症研究的主要目标之一。人类全基因组蛋白质相互作用（PPI）数据的快速积累为研究细胞网络中癌症基因的拓扑特征提供了新的基础。整合多个基因组数据源，包括PPI网络，蛋白质结构域和基因本体（GO）注释，以促进癌症基因的识别非常重要。提取了PPI网络的拓扑特征、蛋白质结构域组成、基因本体类别的丰富度、序列和进化保守性特征，并在癌基因和其他基因之间进行了比较。通过交叉验证评估了各种分类器用于识别癌症基因的预测能力。在人结肠癌细胞系DLD-1中使用siRNA敲低和活力测定对预测结果的子集进行实验验证。交叉验证证明了基于支持向量机（SVM）的分类器的优越性能，包括从PPI网络，蛋白质结构域组合物和GO注释的拓扑特征。然后，我们将训练好的SVM分类器应用于人类基因，以优先考虑推定的癌症基因。几个SVM预测的癌症基因的siRNA敲低在人结肠癌细胞系DLD-1中显示出大大降低的细胞活力。PPI网络的拓扑特征、蛋白质结构域组成和GO注释是癌症基因的良好预测因子。SVM分类器集成了多个特征，因此对于优先考虑候选癌症基因进行实验验证是有用的。
Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO) annotations, to facilitate the identification of cancer genes. Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1. Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs) with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1. Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.
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Pfam：氏族、网络工具和服务。
影响因子：
14.9
作者：
Finn, Robert D.;Mistry, Jaina;Schuster-Bockler, Benjamin;Griffiths-Jones, Sam;Hollich, Volker;Lassmann, Timo;Moxon, Simon;Marshall, Mhairi;Khanna, Ajay;Durbin, Richard;Eddy, Sean R.;Sonnhammer, Erik L. L.;Bateman, Alex
通讯作者：
Bateman, Alex
DOI：
10.1038/nrc1299
发表时间：
2004-03
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
通讯作者：
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影响因子：
12.3
作者：
Fraser HB;Plotkin JB
通讯作者：
Plotkin JB
影响因子：
14.9
作者：
Harris, MA;Clark, J;White, R
通讯作者：
White, R
影响因子：
9.9
作者：
通讯作者：
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