Department of Computer Engineering, Inha University, Incheon, 22212, South Korea.
Department of Computer Engineering, Inha University, Incheon, 22212, South Korea. Electronic address: http://biocomputing.inha.ac.kr.
Comput Methods Programs Biomed. 2021 Nov;212:106465. doi: 10.1016/j.cmpb.2021.106465. Epub 2021 Oct 20.
Most prognostic gene signatures that have been known for cancer are either individual genes or combination of genes. Both individual genes and combination of genes do not provide information on gene-gene relations, and often have less prognostic significance than random genes associated with cell proliferation. Several methods for generating sample-specific gene networks have been proposed, but programs implementing the methods are not publicly available.
We have developed a method that builds gene correlation networks specific to individual cancer patients and derives prognostic gene correlations from the networks. A gene correlation network specific to a patient is constructed by identifying gene-gene relations that are significantly different from normal samples. Prognostic gene pairs are obtained by carrying out the Cox proportional hazards regression and the log-rank test for every gene pair.
We built a web application server called GeneCoNet with thousands of tumor samples in TCGA. Given a tumor sample ID of TCGA, GeneCoNet dynamically constructs a gene correlation network specific to the sample as output. As an additional output, it provides information on prognostic gene correlations in the network. GeneCoNet found several prognostic gene correlations for six types of cancer, but there were no prognostic gene pairs common to multiple cancer types.
Extensive analysis of patient-specific gene correlation networks suggests that patients with a larger subnetwork of prognostic gene pairs have shorter survival time than the others and that patients with a subnetwork that contains more genes participating in prognostic gene pairs have shorter survival time than the others. GeneCoNet can be used as a valuable resource for generating gene correlation networks specific to individual patients and for identifying prognostic gene correlations. It is freely accessible at http://geneconet.inha.ac.kr.
已知的大多数癌症预后基因特征要么是单个基因，要么是基因组合。单个基因和基因组合都不能提供基因-基因关系的信息，而且通常比与细胞增殖相关的随机基因的预后意义更小。已经提出了几种生成样本特异性基因网络的方法，但是实现这些方法的程序并没有公开。
我们开发了一种方法，该方法为每个患者构建特定于基因相关性网络，并从网络中得出预后基因相关性。通过识别与正常样本显著不同的基因-基因关系来构建特定于患者的基因相关性网络。通过对每个基因对进行 Cox 比例风险回归和对数秩检验来获得预后基因对。
我们构建了一个名为 GeneCoNet 的网络应用程序服务器，其中包含 TCGA 中的数千个肿瘤样本。给定 TCGA 的肿瘤样本 ID，GeneCoNet 会动态构建特定于样本的基因相关性网络作为输出。作为附加输出，它提供网络中预后基因相关性的信息。GeneCoNet 为六种癌症找到了几个预后基因相关性，但没有一种预后基因对在多种癌症中是共同的。
对患者特异性基因相关性网络的广泛分析表明，具有更大预后基因对子网的患者比其他患者的生存时间更短，而包含更多参与预后基因对的基因的子网的患者比其他患者的生存时间更短。GeneCoNet 可作为为每个患者生成特定基因相关性网络和识别预后基因相关性的有价值资源。它可以在 http://geneconet.inha.ac.kr 上免费访问。
