Xu Zhanzhan, Zhou Yu, Shi Fang, Cao Yexuan, Dinh Thi Lan Anh, Wan Jing, Zhao Min
Department of Biomedical Engineering, School of Basic Medical Sciences, Wuhan University, Wuhan, Hubei 430071, P.R. China.
Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China.
Oncol Lett. 2017 Apr;13(4):2784-2790. doi: 10.3892/ol.2017.5766. Epub 2017 Feb 22.
Cervical cancer is one of the most common types of cancer among women worldwide. In order to identify the microRNAs (miRNAs/miRs) and mRNAs associated with the carcinogenesis of cervical cancer, and to investigate the molecular mechanisms of cervical cancer, an miRNA microarray, GSE30656, and 3 mRNA microarrays, GSE63514, GSE39001 and GSE9750, for cervical cancer were retrieved from Gene Expression Omnibus. These datasets were analyzed in order to obtain differentially-expressed genes (DEGs) and miRNAs using the GEO2R tool. Gene Ontology (GO) and pathway enrichment analysis for DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) analysis for DEGs was conducted using The Search Tool for the Retrieval of Interacting Genes software and visualized using Cytoscape, followed by hub gene identification, and biological process and pathway enrichment analysis of the module selected from the PPI network using the Molecular Complex Detection plugin. In addition, miRecords was applied to predict the targets of differentially-expressed miRNAs. A total of 44 DEGs and 15 differentially-expressed miRNAs were identified. These DEGs were mainly enriched in GO terms associated with the cell cycle. In the PPI network, cyclin-dependent kinase 1, topoisomerase DNA IIα, aurora kinase A () and minichromosome maintenance complex component 2 () had higher degrees of connectivity. A significant module was detected from the PPI network. , and kinesin family member 20A exhibited higher degrees in this module, while the genes in the module were mainly involved in the cell cycle and the DNA replication pathway. In addition, estrogen receptor 1 was predicted as the potential target of 13 miRNAs. A total of 10 DEGs were identified as potential targets of miR-203. In conclusion, the results indicated that microarray dataset analysis may provide a useful method for the identification of key genes and patterns to successfully identify determinants of the carcinogenesis of cervical cancer. The functional studies of candidate genes and miRNAs from these databases may lead to an increased understanding of the development of cervical cancer.
宫颈癌是全球女性中最常见的癌症类型之一。为了鉴定与宫颈癌发生相关的微小RNA（miRNA/miR）和信使核糖核酸（mRNA），并探究宫颈癌的分子机制，从基因表达综合数据库（Gene Expression Omnibus）中检索了一个用于宫颈癌研究的miRNA微阵列（GSE30656）以及三个mRNA微阵列（GSE63514、GSE39001和GSE9750）。使用GEO2R工具对这些数据集进行分析，以获得差异表达基因（DEG）和miRNA。使用注释、可视化和综合发现数据库（Database for Annotation, Visualization and Integrated Discovery）对DEG进行基因本体论（GO）和通路富集分析。使用检索相互作用基因的搜索工具（The Search Tool for the Retrieval of Interacting Genes）软件对DEG进行蛋白质-蛋白质相互作用（PPI）分析，并使用Cytoscape进行可视化，随后鉴定枢纽基因，并使用分子复合物检测插件对从PPI网络中选择的模块进行生物学过程和通路富集分析。此外，应用miRecords预测差异表达miRNA的靶标。共鉴定出44个DEG和15个差异表达的miRNA。这些DEG主要富集在与细胞周期相关的GO术语中。在PPI网络中，细胞周期蛋白依赖性激酶1、拓扑异构酶DNA IIα、极光激酶A（）和微小染色体维持复合物组分2（）具有较高的连接度。从PPI网络中检测到一个显著模块。 、 和驱动蛋白家族成员20A在该模块中具有较高的连接度，而该模块中的基因主要参与细胞周期和DNA复制通路。此外，雌激素受体1被预测为13个miRNA的潜在靶标。共鉴定出10个DEG作为miR-203的潜在靶标。总之，结果表明微阵列数据集分析可能为鉴定关键基因和模式提供一种有用的方法，从而成功识别宫颈癌发生的决定因素。对这些数据库中候选基因和miRNA的功能研究可能会增进对宫颈癌发展的理解。
