Du Lei, Qian Xiaolei, Dai Chenyang, Wang Lihua, Huang Ding, Wang Shuying, Shen Xiaowei
Department of Gynecology, the International Peace Maternity and Child Health Hospital of China Welfare Institute, Shanghai - China.
Tumori. 2015 Jul-Aug;101(4):384-9. doi: 10.5301/tj.5000319. Epub 2015 Jul 2.
Ovarian cancer (OC) is the most lethal gynecologic malignancy. This study aims to explore the molecular mechanisms of OC and identify potential molecular targets for OC treatment.
Microarray gene expression data (GSE14407) including 12 normal ovarian surface epithelia samples and 12 OC epithelia samples were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) between 2 kinds of ovarian tissue were identified by using limma package in R language (|log2 fold change| gt;1 and false discovery rate [FDR] lt;0.05). Protein-protein interactions (PPIs) and known OC-related genes were screened from COXPRESdb and GenBank database, respectively. Furthermore, PPI network of top 10 upregulated DEGs and top 10 downregulated DEGs was constructed and visualized through Cytoscape software. Finally, for the genes involved in PPI network, functional enrichment analysis was performed by using DAVID (FDR lt;0.05).
In total, 1136 DEGs were identified, including 544 downregulated and 592 upregulated DEGs. Then, PPI network was constructed, and DEGs CDKN2A, MUC1, OGN, ZIC1, SOX17, and TFAP2A interacted with known OC-related genes CDK4, EGFR/JUN, SRC, CLI1, CTNNB1, and TP53, respectively. Moreover, functions about oxygen transport and embryonic development were enriched by the genes involved in the network of downregulated DEGs.
We propose that 4 DEGs (OGN, ZIC1, SOX17, and TFAP2A) and 2 functions (oxygen transport and embryonic development) might play a role in the development of OC. These 4 DEGs and known OC-related genes might serve as therapeutic targets for OC. Further studies are required to validate these predictions.
卵巢癌（OC）是最致命的妇科恶性肿瘤。本研究旨在探讨卵巢癌的分子机制，并确定卵巢癌治疗的潜在分子靶点。
从基因表达综合数据库下载了包含12个正常卵巢表面上皮样本和12个卵巢癌上皮样本的微阵列基因表达数据（GSE14407）。使用R语言中的limma软件包识别两种卵巢组织之间的差异表达基因（DEGs）（|log2倍数变化|>1且错误发现率[FDR]<0.05）。分别从COXPRESdb和GenBank数据库中筛选蛋白质-蛋白质相互作用（PPIs）和已知的卵巢癌相关基因。此外，通过Cytoscape软件构建并可视化前10个上调DEGs和前10个下调DEGs的PPI网络。最后，对参与PPI网络的基因进行DAVID功能富集分析（FDR<0.05）。
共鉴定出1136个DEGs，其中544个下调，592个上调。然后构建了PPI网络，DEGs CDKN2A、MUC1、OGN、ZIC1、SOX17和TFAP2A分别与已知的卵巢癌相关基因CDK4、EGFR/JUN、SRC、CLI1、CTNNB1和TP53相互作用。此外，下调DEGs网络中的基因富集了与氧运输和胚胎发育相关的功能。
我们提出4个DEGs（OGN、ZIC1、SOX17和TFAP2A）和2种功能（氧运输和胚胎发育）可能在卵巢癌的发生发展中起作用。这4个DEGs和已知的卵巢癌相关基因可能作为卵巢癌的治疗靶点。需要进一步研究来验证这些预测。
