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甲状腺乳头状癌差异表达基因整合的网络分析,以鉴定特征基因。

Network Analyses of Integrated Differentially Expressed Genes in Papillary Thyroid Carcinoma to Identify Characteristic Genes.

机构信息

School of Statistics, Qufu Normal University, Qufu 273165, China.

School of Information Science and Engineering, Qufu Normal University, Rizhao 276800, China.

出版信息

Genes (Basel). 2019 Jan 14;10(1):45. doi: 10.3390/genes10010045.

Abstract

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC.

摘要

甲状腺乳头状癌(PTC)是最常见的甲状腺癌类型。鉴定 PTC 的特征基因对于揭示其潜在的遗传机制具有重要意义。在本文中,我们提出了一个框架以及一种名为归一化中心度度量(NCM)的度量方法,用于鉴定 PTC 的特征基因。该框架由四个步骤组成。首先,从四个数据集 GSE3467、GSE3678、GSE33630 和 GSE58545 中筛选和整合上调基因和下调基因(统称为差异表达基因(DEGs));其次,构建了一个 DEGs 的相互作用网络,其中每个节点代表一个基因,每个边代表连接节点之间的相互作用;第三,使用传统度量和 NCM 度量来分析网络中每个节点的拓扑性质。与传统度量相比,NCM 度量方法识别出更多与 PTC 相关的基因;最后,通过挖掘该网络的高密度子图,并进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析,捕获了几个有意义的结果,其中大多数被证明与 PTC 相关。实验结果证明,该网络框架和 NCM 度量方法可用于鉴定更多的 PTC 特征基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00bc/6356810/01a98f93385e/genes-10-00045-g001.jpg

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