Yang Xiao, Zhu Shaoming, Li Li, Zhang Li, Xian Shu, Wang Yanqing, Cheng Yanxiang
Department of Obstetrics and Gynecology.
Department of Urology, Renmin Hospital of Wuhan University.
Onco Targets Ther. 2018 Mar 15;11:1457-1474. doi: 10.2147/OTT.S152238. eCollection 2018.
The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.
The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network.
This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.
卵巢癌相关死亡率在妇科恶性肿瘤中位居首位。然而，卵巢癌的病因及潜在分子事件尚不清楚。在此，我们应用整合生物信息学来鉴定参与卵巢癌的关键致病基因，并揭示潜在分子机制。
从基因表达综合数据库（GEO）下载了GDS3592、GSE54388和GSE66957的表达谱，其中包含115个样本，包括85例卵巢癌样本和30例正常卵巢样本。整合这三个微阵列数据集以获得差异表达基因（DEG），并通过生物信息学方法进行深入分析。分别通过DAVID在线分析和KOBAS在线分析对DEG进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。从STRING数据库构建DEG的蛋白质-蛋白质相互作用（PPI）网络。在这三个GEO数据集中共鉴定出190个DEG，其中99个基因上调，91个基因下调。GO分析表明，DEG的生物学功能主要集中在调节细胞增殖、黏附、分化以及细胞内信号级联反应。主要细胞成分包括细胞膜、外泌体、细胞骨架和细胞外基质。分子功能包括生长因子活性、蛋白激酶调节、DNA结合和氧转运活性。KEGG通路分析表明，这些DEG主要参与Wnt信号通路、氨基酸代谢和肿瘤信号通路。从PPI网络中鉴定出DEG中17个最密切相关的基因。
本研究表明，利用整合生物信息学分析筛查卵巢癌中的DEG和通路有助于我们了解卵巢癌发生发展的分子机制，对卵巢癌的早期诊断和预防具有临床意义，并为卵巢癌治疗提供有效靶点。
