摘要
目的识别与前列腺癌不良预后相关的差异甲基化基因,为寻找治疗靶点提供数据支持。方法利用GEO数据库的4个前列腺癌基因芯片数据集GSE46602、GSE69223、GSE6919和GSE32269进行差异基因的筛选,并与TCGA数据相比对。通过David数据库对其进行功能富集分析。采用String数据库构建了基因编码蛋白之间PPI网络,随后利用Cytoscape软件进行分析并实现可视化。通过TCGA甲基化数据,考量基因的甲基化水平,并利用临床数据观察其差异表达对预后的影响。结果 GEO数据库筛选得到差异基因600个,与TCGA数据比对后,得到差异基因301个。激活了癌症、p I3K-Akt和cGMP-PKG信号通路。构建PPI网络,分析出10个网络关键节点,进一步做差异甲基化分析,发现过表达基因EZH2、TOP2A、GTSE1和HOXC6存在启动子区低甲基化情况,低表达基因CAV1启动子区高甲基化。其中基因EZH2、GTSE1和HOXC6的过表达与前列腺癌的不良预后相关。结论选取不同平台的前列腺癌数据,通过生物信息学分析,筛选出与不良预后相关的差异甲基化基因,为前列腺癌治疗提供新的分子靶点。
Objective The aim of this study was to identify differentially methylated genes associated with poor prognosis of prostate cancer and provide data support for finding therapeutic targets. Methods In this experiment, four prostate cancer gene chip data sets of GSE46602, GSE69223, GSE6919 and GSE32269 from GEO database were used to screen differential genes, and compared with TCGA data. The functional enrichment analysis was carried out by David database. The PPI network between gene-encoded proteins was constructed by using String database, and then analyzed and visualized by Cytoscape software. Through the methylation data of TCGA, the methylation level of the gene was investigated, and the clinical data were used to observe the effect of differential expression on the prognosis. Results 600 differential genes were screened by GEO database. After overlapping analysis with TCGA data, 301 differential genes were obtained. These genes activate pI3 K-Akt and cGMP-PKG signaling pathways. After building the PPI network, 10 key nodes of the network were analyzed. Further differential methylation analysis of these core genes showed that the over-expressed genes EZH2, TOP2 A, GTSE1 and HOXC6 had low methylation in the promoter region, while the low-expressed gene CAV1 had hypermethylation in the promoter region. Over-expression of EZH2, GTSE1 and HOXC6 genes is associated with poor prognosis of prostate cancer. Conclusions Prostate cancer data from different platforms are selected, and the differential methylation state of genes related to poor prognosis is analyzed. The finding may provide potential molecular targets for the treatment of prostate cancer.
作者
李猷
柳东辉
张国勇
LI You;LIU Dong-hui;ZHANG Guo-yong(Department of Urology,Jinqiu Hospital of Liaoning Province,Shenyang 110016,China)
出处
《中国医药生物技术》
2020年第4期411-417,共7页
Chinese Medicinal Biotechnology
关键词
前列腺癌
甲基化
生物信息学分析
Prostate cancer
Methylation
Bioinformatics analysis
作者简介
通信作者:李猷,Email:liyoutgyx@126.com。