Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, No,36, Sanhao Street, Heping District, Shenyang, Liaoning Province 110004, China.
J Ovarian Res. 2013 Dec 6;6(1):88. doi: 10.1186/1757-2215-6-88.
The Cancer Genome Atlas (TCGA) Data portal provides a platform for researchers to search, download, and analysis data generated by TCGA. The objective of this study was to explore the molecular mechanism of ovarian cancer pathogenesis.
Microarray data of ovarian cancer were downloaded from TCGA database, and Limma package in R language was used to identify the differentially expressed genes (DEGs) between ovarian cancer and normal samples, followed by the function and pathway annotations of the DEGs. Next, NetBox software was used to for the gene-gene interaction (GGI) network construction and the corresponding modules identification, and functions of genes in the modules were screened using DAVID.
Our studies identified 332 DEGs, including 146 up-regulated genes which mainly involved in the cell cycle related functions and cell cycle pathway, and 186 down-regulated genes which were enriched in extracellular region par function, and Ether lipid metabolism pathway. GGI network was constructed by 127 DEGs and their significantly interacted 209 genes (LINKERs). In the top 10 nodes ranked by degrees in the network, 5 were LINKERs. Totally, 7 functional modules in the network were selected, and they were enriched in different functions and pathways, such as mitosis process, DNA replication and DNA double-strand synthesis, lipid synthesis processes and metabolic pathways. AR, BRCA1, TFDP1, FOXM1, CDK2, and DBF4 were identified as the transcript factors of the 7 modules.
our data provides a comprehensive bioinformatics analysis of genes, functions, and pathways which may be involved in the pathogenesis of ovarian cancer.
癌症基因组图谱（TCGA）数据门户为研究人员提供了一个搜索、下载和分析 TCGA 生成的数据的平台。本研究旨在探索卵巢癌发病机制的分子机制。
从 TCGA 数据库下载卵巢癌的微阵列数据，使用 R 语言中的 Limma 包识别卵巢癌和正常样本之间的差异表达基因（DEGs），然后对 DEGs 进行功能和途径注释。接下来，使用 NetBox 软件构建基因-基因相互作用（GGI）网络并识别相应的模块，并使用 DAVID 筛选模块中基因的功能。
我们的研究确定了 332 个 DEGs，其中 146 个上调基因主要涉及细胞周期相关功能和细胞周期途径，186 个下调基因富集在外细胞区功能和醚脂代谢途径。通过 127 个 DEG 和它们显著相互作用的 209 个基因（LINKERs）构建了 GGI 网络。在网络中按度排名前 10 的节点中，有 5 个是 LINKERs。总共从网络中选择了 7 个功能模块，它们富集在不同的功能和途径中，如有丝分裂过程、DNA 复制和 DNA 双链合成、脂质合成过程和代谢途径。AR、BRCA1、TFDP1、FOXM1、CDK2 和 DBF4 被确定为 7 个模块的转录因子。
我们的数据提供了一个全面的基因、功能和途径的生物信息学分析，这些基因、功能和途径可能参与了卵巢癌的发病机制。
