Identification of a 5-microRNA signature and hub miRNA-mRNA interactions associated with pancreatic cancer
Identification of a 5-microRNA signature and hub miRNA-mRNA interactions associated with pancreatic cancer
复制标题
鉴定与胰腺癌相关的 5-microRNA 特征和中枢 miRNA-mRNA 相互作用
DOI：
10.3892/or.2018.6820
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发表时间：
2019-01-01
期刊：
影响因子：
4.2
通讯作者：
Tao, Kaixiong
中科院分区：
文献类型：
--
作者：
Ma, Xianxiong;Tao, Ruikang;Tao, Kaixiong
据报道，miRNA基因轴在胰腺癌(PC)的发生发展中起着重要作用。本研究的目的是系统地鉴定微小RNA信号和HUB分子，以及HUB miRNA-基因轴，并探索与PC致癌相关的潜在生物标志物和机制。从美国国家生物技术信息中心(NCBI)的基因表达总览(GEO)和ArrayExpress数据库中获得11个microRNA图谱数据集，并进行Meta分析以确定肿瘤组织和正常组织之间差异表达的miRNAs(Dem)。随后，基于癌症基因组图谱(TCGA)的miRNA序列数据，利用最小绝对收缩和选择算子(LASSO)方法构建了用于识别PC的诊断回归模型。另外，下载GSE41368，并进行加权基因共表达网络分析(WGCNA)，分别使用TCGAbiolinks和WGCNA程序包获得与癌变相关的基因模块。最后，构建了miRNA-基因网络，并利用Cytoscape软件进行可视化，然后基于注释、可视化和集成发现数据库(David)进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)分析。共鉴定出14个DEM，套索回归模型生成的基于5-microRNA的分数为识别PC提供了高精度[曲线下面积(AUC)=0.918]。此外，基于上述生物信息学工具和数据库，构建了44个miRNA-mRNA相互作用，筛选出4个HUB基因。此外，通过基因集浓缩分析(GSEA)，鉴定了14个生物过程(BP)功能和6个KEGG途径。总之，本研究应用集成的生物信息学方法来生成PC的整体视图，从而为进一步临床应用5-miRNA特征和识别的HUB分子以及miRNA基因轴提供基础，这些分子可以作为诊断标记和潜在的治疗靶点。
miRNA-gene axes have been reported to serve an important role in the carcinogenesis of pancreatic cancer (PC). The aim of the present study was to systematically identity the microRNA signature and hub molecules, as well as hub miRNA-gene axes, and to explore the potential biomarkers and mechanisms associated with the carcinogenesis of PC. Eleven microRNA profile datasets were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) and ArrayExpress databases, and a meta-analysis was performed to identify the differentially expressed miRNAs (DEMs) between tumor tissue and normal tissue. Subsequently, a diagnostic regression model was constructed to identify PC based on The Cancer Genome Atlas (TCGA) miRNA sequence data by using the least absolute shrinkage and selection operator (LASSO) method. In addition, GSE41368 was downloaded, and a weighted gene co-expression network analysis (WGCNA) was performed to obtain the gene module associated with carcinogenesis by using the TCGAbiolinks and WGCNA packages, respectively. Finally, miRNA-gene networks were constructed and visualized using Cytoscape software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A total of 14 DEMs were identified, and a 5-microRNA-based score generated by the LASSO regression model provided a high accuracy for identifying PC [area under the curve (AUC)=0.918]. In addition, 44 miRNA-mRNA interactions were constructed, and 4 hub genes were screened on the basis of the above bioinformatic tools and databases. Furthermore, 14 biological process (BP) functions and 6 KEGG pathways were identified according to gene set enrichment analysis (GSEA). In summary, the present study applied integrated bioinformatics approaches to generate a holistic view of PC, thereby providing a basis for further clinical application of the 5-miRNA signature and the identified hub molecules, as well as the miRNA-gene axes, which could serve as diagnostic markers and potential treatment targets.