胶质母细胞瘤驱动基因相关的竞争性内源RNA调控网络

目的 探讨胶质母细胞瘤（GBM）驱动基因相关的竞争性内源RNA（ceRNA）调控网络。方法 从癌症基因组图谱（TCGA）中下载169例GBM及5例正常组织长链非编码RNA（lncRNA）表达数据，从UCSC Xena数据库下载509例GBM及10例正常脑组织微小RNA（miRNA）表达数据。对获取的lncRNA及miRNA表达数据进行差异表达分析。GBM的17个驱动基因是从文献（PMID: 30096302）中获得。miRcode，TargetScan，miRTarBase和miRDB数据库预测lncRNA、miRNA和GBM驱动基因之间的相互作用。结果 GBM组织TP53及PTEN突变率最高，达30%，且TP53错义突变最常见。筛选出差异表达lncRNA共2 445个，表达上调1 052个，下调1 393个；差异表达miRNA共56 个，表达上调28个，下调28个。共有5 个GBM驱动基因、6 个miRNA 及297个lncRNA筛选出用于构建ceRNA网络，包括HOX转录反义RNA在内的8个lncRNA与GBM病人的生存相关。结论 采用生信分析方法构建ceRNA网络有助于深化GBM发生、发展机制的认识

Competing endogenous RNA regulatory network related to glioblastoma driver genes

Objective To explore the competing endogenous RNA (ceRNA) regulatory network related to glioblastoma driver genes. Methods The long non-coding RNA (lncRNA) expression data of 169 patients with glioblastoma and 5 normal brain tissues were downloaded from the Cancer Genome Atlas (TCGA), and the microRNA (miRNA) expression data of 509 patients with glioblastoma tissues and 10 normal brain tissues were downloaded from the UCSC Xena database. Differential expression analysis was performed on the lncRNA and miRNA expression data. The 17 driver genes of glioblastoma were obtained from the literature (PMID: 30096302). The miRcode, TargetScan, miRTarBase and miRDB databases were used to predict the interactions among lncRNA, miRNA and glioblastoma driver genes. Results The TP53 and PTEN mutation rates of glioblastoma tissues were the highest, which were up to 30%, and TP53 missense mutation was the most common. A total of 2 445 differentially expressed lncRNA was screened, with 1 052 up-regulation and 1 393 down-regulation. A total of 56 differentially expressed miRNA was screened, with 28 up-regulation and 28 down-regulation. A total of 5 driver genes of glioblastoma, 6 miRNA and 297 lncRNA was screened to construct the ceRNA network. Eight lncRNAs, including HOX transcrit antisense RNA, were related to the survival of glioblastoma patients. Conclusion The construction of ceRNA network with bio-information analysis method helps to further elucidate the mechanism of glioblastoma development