基于CNV区域网络识别神经胶质瘤的重要功能区域

目的：基于基因拷贝数变异（CNV）区域网络识别神经胶质瘤的重要功能区域。方法：运用独特的计算样本的共相关性值的方法，使CNV数据与基因数据产生联系；基于蛋白质互作关系，在CNV区域与基因之间搭建桥梁，构建CNV区域网络；分析网络拓扑性质，识别出神经胶质瘤的重要功能CNV区域。结果：本文共识别出了11个与神经胶质瘤相关的候选重要功能CNV区域，通过功能注释和通路分析，确认了识别到的区域与神经胶质瘤有重要联系。结论：通过基因与表型之间的联系，利用已知表型基因在同源、功能、互作、结构域上的特征将CNV区域与基因联系起来，通过基因的功能可以了解到CNV区域的功能，对于疾病的预测和诊断有重要的意义。

Identify Glioma Important Functional Areas Based on the CNV Area Network

Objective： To identify the significant functional regions in glioma by using copy number variation region networks. Methods： The link between CNV data and genetic data was created by applying a unique approach which calculated correlationship values between samples. Based on protein-protein interactions, a bridge that connected CNV regions and genes was built, and a CNVR network was constructed. After analyzing topological properties of the network, significant functional CNV regions in glioma were identified. Results： 11 candidate functional CNV regions that are related to glioma were identified, and their significant correlation with glioma were verified by functional annotation and pathway analysis. Conclusions： Through the association of genes and phenotypes, characteristics of genes with known phenotypes,such as homolog, function, interaction, structural domains, could be used to associate CNV regions and genes. CNV regions＇ function can then be inferred from gene functions, which is purposeful for disease prediction and diagnosis.