Jing Jing-Jing, Wang Ze-Yang, Li Hao, Sun Li-Ping, Yuan Yuan
Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Key Laboratory of Cancer Etiology and Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang, Liaoning China.
Cancer Cell Int. 2018 Sep 21;18:146. doi: 10.1186/s12935-018-0637-5. eCollection 2018.
The molecular mechanism of Epstein-Barr virus (EBV)-associated gastric cancer (EBVaGC) remains elusive. A collection of molecular regulators including transcription factor and noncoding RNA (ncRNAs) may affect the carcinogenesis of EBVaGC by regulating the expression and function of key genes. In this study, integration of multi-level expression data and bioinformatics approach was used to identify key elements and their interactions involved in mechanism of EBVaGC and their network regulation.
Data of the gene expression profiling data sets (GSE51575) was downloaded from GEO database. Differentially expressed genes between EBVaGC and normal samples were identified by GEO2R. Gene ontology and pathway enrichment analyses were performed using R packages Cluster profiler. STRING database was used to find interacting proteins between different genes. Transcription factors in differentially expressed genes were obtained from TF Checkpoint database. Using Cytoscape, we built transcription factor regulation network. miRNAs involved in the gene-interacting proteins and the miRNA-targeted lncRNA were predicted through miRWalk. Using ViRBase, EBV related miRNA regulation network was built. Overlapping genes and regulators of the above three networks were further identified, and the cross network was constructed using Cytoscape software. Moreover, the differential expressions of the target genes and transcription factors in the cross network were explored in different molecular subtypes of GC using cBioPortal. By histological verification, the expression of two main target genes in the cross network were further analyzed.
A total of 104 genes showed differential expressions between EBVaGC and normal tissues, which were associated with digestion, G-protein coupled receptor binding, gastric acid secretion, etc. Pathway analysis showed that the differentially expressed genes were mainly enriched in gastric acid secretion and protein digestion and absorption. Using STRING dataset, a total of 54 proteins interacted with each other. Based on the transcription factor network, the hub transcription factors IRX3, NKX6-2, PTGER3 and SMAD5 were identified to regulate their target genes SST and GDF5, etc. After screening and matching in miRwalk datasets, a ceRNA network was established, in which the top five miRNAs were hsa-miR-4446-3p, hsa-miR-5787, hsa-miR-1915-3p, hsa-miR-335-3p and hsa-miR-6877-3p, and the top two lncRNAs were RP5-1039K5.19 and TP73-AS1. According to the EBV related miRNA regulation network, CXCL10 and SMAD5 were found to be regulated by EBV-miR-BART1-3p and EBV-mir-BART22, respectively. By overlapping the three networks, CXCL10, GDF5, PTGER3, SMAD5, miR-6877-3p, RP5-1039K5.19, TP73-AS1, EBV-miR-BART1-3p and EBV-mir-BART22 were found to be key elements of regulation mechanism of EBVaGC. CXCL10, GDF5, PTGER3 and SMAD5 were also differentially expressed among the four molecular subtypes of GC. The histological verification experiment showed differential expressions of the two main target genes GDF5 and CXCL10 between EBVaGC and non-tumor tissues as well as EBVnGC.
In the current study, our results revealed key elements and their interactions involved in EBVaGC. Some hub transcription factors, miRNAs, lncRNAs and EBV related miRNAs were observed to regulate their target genes. Overlapping genes and regulators were observed in diverse regulation networks, such as CXCL10, GDF5, PTGER3, SMAD5, miR-6877-3p, RP5-1039K5.19, TP73-AS1, EBV-miR-BART1-3p and EBV-mir-BART22. Moreover, CXCL10, GDF5, PTGER3 and SMAD5 were also differentially expressed among the four molecular subtypes of GC. The histological verification experiment showed differential expressions of the two main target genes GDF5 and CXCL10 between EBVaGC and non-tumor tissues as well as EBVnGC. Therefore, the identified key elements and their network regulation may be specifically involved in EBVaGC mechanisms.
爱泼斯坦-巴尔病毒（EBV）相关胃癌（EBVaGC）的分子机制仍不清楚。包括转录因子和非编码RNA（ncRNAs）在内的一系列分子调节因子可能通过调节关键基因的表达和功能来影响EBVaGC的致癌作用。在本研究中，利用多层次表达数据整合和生物信息学方法来识别参与EBVaGC机制及其网络调控的关键元件及其相互作用。
从基因表达综合数据库（GEO）下载基因表达谱数据集（GSE51575）的数据。利用GEO2R识别EBVaGC与正常样本之间的差异表达基因。使用R包Cluster profiler进行基因本体论和通路富集分析。利用STRING数据库查找不同基因之间相互作用的蛋白质。从TF Checkpoint数据库获取差异表达基因中的转录因子。使用Cytoscape构建转录因子调控网络。通过miRWalk预测参与基因相互作用蛋白的miRNA以及miRNA靶向的lncRNA。利用ViRBase构建EBV相关miRNA调控网络。进一步识别上述三个网络的重叠基因和调节因子，并使用Cytoscape软件构建交叉网络。此外，利用cBioPortal在胃癌的不同分子亚型中探索交叉网络中靶基因和转录因子的差异表达。通过组织学验证，进一步分析交叉网络中两个主要靶基因的表达。
共有104个基因在EBVaGC与正常组织之间表现出差异表达，这些基因与消化、G蛋白偶联受体结合、胃酸分泌等相关。通路分析表明，差异表达基因主要富集于胃酸分泌以及蛋白质消化和吸收。利用STRING数据集，共有54种蛋白质相互作用。基于转录因子网络，确定了关键转录因子IRX3、NKX6-2、PTGER3和SMAD5来调节其靶基因SST和GDF5等。在miRwalk数据集中进行筛选和匹配后，建立了一个ceRNA网络，其中排名前五的miRNA是hsa-miR-4446-3p、hsa-miR-5787、hsa-miR-1915-3p、hsa-miR-335-3p和hsa-miR-6877-3p，排名前两位的lncRNA是RP5-1039K5.19和TP73-AS1。根据EBV相关miRNA调控网络，发现CXCL10和SMAD5分别受EBV-miR-BART1-3p和EBV-mir-BART22调控。通过重叠这三个网络，发现CXCL10、GDF5、PTGER3、SMAD5、miR-6877-3p、RP5-1039K5.19、TP73-AS1、EBV-miR-BART1-3p和EBV-mir-BART22是EBVaGC调控机制的关键元件。CXCL10、GDF5、PTGER3和SMAD5在胃癌的四种分子亚型中也存在差异表达。组织学验证实验表明，两个主要靶基因GDF5和CXCL10在EBVaGC与非肿瘤组织以及EBV阴性胃癌之间存在差异表达。
在本研究中，我们的结果揭示了参与EBVaGC的关键元件及其相互作用。观察到一些关键转录因子、miRNA、lncRNA和EBV相关miRNA调节其靶基因。在不同的调控网络中观察到重叠基因和调节因子，如CXCL10、GDF5、PTGER3、SMAD5、miR-6877-3p、RP5-1039K5.19、TP73-AS1、EBV-miR-BART1-3p和EBV-mir-BART22。此外，CXCL10、GDF5、PTGER3和SMAD5在胃癌的四种分子亚型中也存在差异表达。组织学验证实验表明，两个主要靶基因GDF5和CXCL10在EBVaGC与非肿瘤组织以及EBV阴性胃癌之间存在差异表达。因此，所确定的关键元件及其网络调控可能特异性参与EBVaGC机制。
