Li R, Jiang W J, Jin S L, Zhao R H, Cao X G, Zong H
Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
Department of Oncology, The Zhengzhou Central Hospital Affiliated of Zhengzhou University, Zhengzhou 450007, China.
Zhonghua Zhong Liu Za Zhi. 2020 Feb 23;42(2):115-121. doi: 10.3760/cma.j.issn.0253-3766.2020.02.006.
To construct the competitive endogenous RNA (ceRNA) network related to gastric cancer and explore the molecular mechanism. The expression profiles of lncRNA, miRNA and mRNA in gastric cancer and paracancer tissues were analyzed by biochip technology, edgeR package in R software was used to filtrate differential expression genes (multiple change of >1.5 times, <0.05) and volcano map was drawn. Based on the online miRNA-lncRNA prediction tool lncBase database and the miRNA Target gene prediction database (miRTarBase, target-scan, miRDB, starBase), the relationship between miRNA, lncRNA and mRNA was predicted. Cytoscape software was used to construct lncRNA-miRNA-mRNA ceRNA network and key genes (hub genes) were identified based on cytohubba calculation of degree score of each node. Then Hub genes related to the prognosis of gastric cancer were verified in the TCGA database. The GO and KEGG enrichment analysis of differentially expressed mRNA was performed using the online biological information annotation database DAVID, <0.05 and false discovery rate (FDR)<0.05 were used as cut-off criteria. R software was used to download the RNA sequencing data and mirna-seq data of gastric cancer and adjacent tissues in TCGA database, edgeR package was used to screen out differentially expressed mRNA, miRNA and lncRNA, and some differentially expressed genes in our data were verified. In OncoLnc database, STAD project of TCGA data was selected and hub gene was input. Patients were divided into two groups based on the median value for hub genes and Kaplan-meier analysis was performed. The differentially expressed 766 mRNA, 110 lncRNA and 10 miRNA were screened out, among them 90 mRNA, 4 lncRNA and 6 miRNA were used to construct the ceRNA network, and 2 of the 20 hub genes were related to the prognosis of patients. MLK7-AS1, SPP1, SULF1, hsa-miR-1307-3p were upregulated in gastric cancer tissues from our biochip, while MT2A, MT1X were downregulated, which were consistent with the results of TCGA gastric cancer database. The differentially expressed mRNAs were significantly enriched in the biological process (BP) and the mineral absorption pathway. CHST1 was negatively correlated while miR-183-5p was positively corelated with the survival of patients. The establishment of ceRNA network for gastric cancer is conducive to further understanding of the molecular biological mechanism. CHST1 and miR-183-5p can be used as prognostic factors of gastric cancer.
构建与胃癌相关的竞争性内源性RNA（ceRNA）网络并探索其分子机制。采用生物芯片技术分析胃癌及癌旁组织中lncRNA、miRNA和mRNA的表达谱，使用R软件中的edgeR包筛选差异表达基因（变化倍数>1.5倍且P<0.05）并绘制火山图。基于在线miRNA-lncRNA预测工具lncBase数据库以及miRNA靶基因预测数据库（miRTarBase、target-scan、miRDB、starBase）预测miRNA、lncRNA和mRNA之间的关系。利用Cytoscape软件构建lncRNA-miRNA-mRNA ceRNA网络，并基于cytohubba计算每个节点的度得分来识别关键基因（枢纽基因）。然后在TCGA数据库中验证与胃癌预后相关的枢纽基因。使用在线生物信息注释数据库DAVID对差异表达的mRNA进行GO和KEGG富集分析，以P<0.05和错误发现率（FDR）<0.05作为截断标准。利用R软件下载TCGA数据库中胃癌及癌旁组织的RNA测序数据和mirna-seq数据，使用edgeR包筛选差异表达的mRNA、miRNA和lncRNA，并对我们数据中的一些差异表达基因进行了验证。在OncoLnc数据库中，选择TCGA数据的STAD项目并输入枢纽基因。根据枢纽基因的中位数将患者分为两组并进行Kaplan-meier分析。筛选出差异表达的766个mRNA、110个lncRNA和10个miRNA，其中90个mRNA、4个lncRNA和6个miRNA用于构建ceRNA网络，20个枢纽基因中有2个与患者预后相关。在我们的生物芯片中，MLK7-AS1、SPP1、SULF1、hsa-miR-1307-3p在胃癌组织中上调，而MT2A、MT1X下调，这与TCGA胃癌数据库的结果一致。差异表达的mRNA在生物学过程（BP）和矿物质吸收途径中显著富集。CHST1与患者生存率呈负相关，而miR-183-5p与患者生存率呈正相关。胃癌ceRNA网络的建立有助于进一步了解分子生物学机制。CHST1和miR-183-5p可作为胃癌的预后因素。
