Identifying Cancer Specific Driver Modules Using a Network-Based Method.
Identifying Cancer Specific Driver Modules Using a Network-Based Method.
复制标题
使用基于网络的方法识别癌症特异性驱动模块
DOI：
10.3390/molecules23051114
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发表时间：
2018-05-08
期刊：
影响因子：
--
通讯作者：
Hu Y
中科院分区：
文献类型：
--
作者：
Li F;Gao L;Wang P;Hu Y
检测驱动模块是在通路水平上理解致癌机制的关键挑战。识别癌症特异性驱动模块有助于解释不同癌症类型的不同原理。然而，大多数方法被提出来识别一种癌症中的驱动模块，但很少有方法被引入来检测癌症特异性驱动模块。我们提出了一种基于网络的方法来检测癌症特异性驱动模块（CSDM）在某种癌症类型到其他癌症类型。我们通过结合所有癌症类型的特异性覆盖和互斥性来构建癌症的特异性网络，以在通路水平上捕获癌症的特异性。为了说明该方法的性能，我们将CSDM应用于12种TCGA癌症类型。当我们将CSDM与SpeMDP和HotNet2在特定覆盖率和GO术语和KEGG途径的富集方面进行比较时，CSDM更准确。我们发现两种不同癌症的特异性驱动模块几乎没有重叠，这表明CSDM检测到的驱动模块是特异性的。最后，我们还分析了BRCA，BLCA和LAML与已知途径交叉的三个特定驱动模块。CSDM的源代码可以在https://github.com/fengli28/CSDM.git上免费访问。
Detecting driver modules is a key challenge for understanding the mechanisms of carcinogenesis at the pathway level. Identifying cancer specific driver modules is helpful for interpreting the different principles of different cancer types. However, most methods are proposed to identify driver modules in one cancer, but few methods are introduced to detect cancer specific driver modules. We propose a network-based method to detect cancer specific driver modules (CSDM) in a certain cancer type to other cancer types. We construct the specific network of a cancer by combining specific coverage and mutual exclusivity in all cancer types, to catch the specificity of the cancer at the pathway level. To illustrate the performance of the method, we apply CSDM on 12 TCGA cancer types. When we compare CSDM with SpeMDP and HotNet2 with regard to specific coverage and the enrichment of GO terms and KEGG pathways, CSDM is more accurate. We find that the specific driver modules of two different cancers have little overlap, which indicates that the driver modules detected by CSDM are specific. Finally, we also analyze three specific driver modules of BRCA, BLCA, and LAML intersecting with well-known pathways. The source code of CSDM is freely accessible at https://github.com/fengli28/CSDM.git.
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影响因子：
8.8
作者：
Ge Z;Leighton JS;Wang Y;Peng X;Chen Z;Chen H;Sun Y;Yao F;Li J;Zhang H;Liu J;Shriver CD;Hu H;Cancer Genome Atlas Research Network;Piwnica-Worms H;Ma L;Liang H
通讯作者：
Liang H
影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
64.5
作者：
Davoli T;Xu AW;Mengwasser KE;Sack LM;Yoon JC;Park PJ;Elledge SJ
通讯作者：
Elledge SJ
影响因子：
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作者：
Alaimo S;Giugno R;Acunzo M;Veneziano D;Ferro A;Pulvirenti A
通讯作者：
Pulvirenti A
影响因子：
4.3
作者：
Alaimo S;Marceca GP;Ferro A;Pulvirenti A
通讯作者：
Pulvirenti A