打开文本图片集
[摘要] 目的 從分子层面分析EB病毒（EBV）相关伯基特淋巴瘤（BL）的差异表达基因（DEGs），为其诊疗提供新的思路。方法 从美国国立生物技术信息中心（NCBI）获取EBV阳性与阴性BL相关基因芯片数据集GSE100458。通过morpheus筛选出DEGs，分别将上调和下调基因输入Database for Annotation Visualizationand Integrated Discovery（DAVID）数据库，进行GO分析;进一步采用cytoscape筛选出10个TOP基因，实时荧光定量PCR方法验证其在EBV阳性与阴性BL细胞系中表达情况。结果 分析EBV阳性和EBV阴性的BL细胞系的前400个DEGs，包括200个上调基因和200个下调基因，上调基因主要集中在生长因子活性通路、肝素结合通路、细胞外间隙通路;下调基因主要集中在伽马微管蛋白结合通路、内质网膜通路、逆行性小囊泡运输通路。对DEGs进行蛋白质相互作用分析后，筛选出核心基因乙酰辅酶A羧化酶β（ACACB）等10个基因。实时荧光定量PCR检测结果显示，在EBV阳性与阴性BL组间，CDH1、PRPF19、UBE2N、F2、H6PD、VEGFA、YARS共7个基因的转录表达差异有统计学意义（t=3.878～32.601，P<0.05），ACACB、CTTN、IGF1共3个基因表达差异无统计学意义（P>0.05）。结论 EBV感染可导致宿主细胞基因表达谱的改变，生物信息学分析法可初步筛选出DEGs和相互作用的蛋白，为进一步深入研究提供有价值的信息和思路。
[关键词] 疱疹病毒4型;伯基特淋巴瘤;基因表达;生物信息学
[中图分类号] R373.9 [文献标志码] A [文章编号] 2096-5532（2019）04-0423-06
[ABSTRACT] Objective To analyze the differentially expressed genes （DEGs） in EB virus （EBV）-associated Burkitt lymphoma （BL） at molecular level， and to provide new ideas for its diagnosis and treatment. Methods The EBV-positive and-negative BL-related gene microarray data set GSE100458 was obtained from the National Center for Biotechnology Information （NCBI）. DEGs were screened out by Morpheus， and the up-regulated and down-regulated genes were entered into the Database for Annotation Visualization and Integrated Discovery （DAVID） for GO analysis. Ten top genes were identified by cytoscape， and their expression in EBV-positive and EBV-negative BL cell lines was verified by real-time fluorescence quantification PCR. Results The top 400 DEGs of EBV-positive and EBV-negative BL cell lines were analyzed， including 200 up-regulated genes and 200 down-regulated genes. The up-regulated genes were mainly concentrated in the growth factor activity pathway， heparin binding pathway， and extracellular gap pathway. The down-regulated genes were mainly concentrated in the gamma-tubulin binding pathway， endoplasmic reticulum pathway， and retrograde small vesicle transport pathway. After protein interaction analysis of DEGs，10 core genes such as acetyl coenzyme A carboxylase beta （ACACB） were screened out. Quantitative real-time PCR results showed that there were significant differences in transcriptional expression of 7 genes （CDH1， PRPF19， UBE2N， F2， H6PD， VEGFA， and YARS） between EBV-positive and EBV-negative BL groups （t=3.878-32.601，P<0.05）， and there were no significant diffe-rences in the expression of ACACB， CTTN， and IGF1 （P>0.05）. Conclusion EBV infection can lead to changes in the gene expression profiles of host cells. Bioinformatics analysis can preliminarily screen out DEGs and interacting proteins， providing valuable information and ideas for further research.