Department of Endocrinology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Med Sci Monit. 2019 Feb 12;25:1140-1154. doi: 10.12659/MSM.912450.
BACKGROUND The aims of this study were to use RNA expression profile bioinformatics data from cases of thyroid cancer from the Cancer Genome Atlas (TCGA), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Gene Ontology (GO) databases to construct a competing endogenous RNA (ceRNA) network of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs). MATERIAL AND METHODS TCGA provided RNA profiles from 515 thyroid cancer tissues and 56 normal thyroid tissues. The DESeq R package analyzed high-throughput sequencing data on differentially expressed RNAs. GO and KEGG pathway analysis used the DAVID 6.8 and the ClusterProfile R package. Kaplan-Meier survival statistics and Cox regression analysis were performed. The thyroid cancer ceRNA network was constructed based on the miRDB, miRTarBase, and TargetScan databases. RESULTS There were 1,098 mRNAs associated with thyroid cancer; 101 mRNAs were associated with overall survival (OS). Multivariate analysis developed a risk scoring system that identified seven signature mRNAs, with a discriminative value of 0.88, determined by receiver operating characteristic (ROC) curve analysis. A ceRNA network included 13 mRNAs, 31 lncRNAs, and seven miRNAs. Four out of the 31 lncRNAs and all miRNAs were down-regulated, and the remaining RNAs were upregulated. Two lncRNAs (MIR1281A2HG and OPCML-IT1) and one miRNA (miR-184) were significantly associated with OS in patients with thyroid cancer. CONCLUSIONS Differential RNA expression profiling in thyroid cancer was used to construct a ceRNA network of mRNAs, lncRNAs, and miRNAs that showed potential in evaluating prognosis.
本研究旨在利用癌症基因组图谱（TCGA）、京都基因与基因组百科全书（KEGG）和基因本体论（GO）数据库中甲状腺癌病例的 RNA 表达谱生物信息学数据，构建 mRNA、长链非编码 RNA（lncRNA）和 microRNA（miRNA）的竞争性内源 RNA（ceRNA）网络。
TCGA 提供了 515 例甲状腺癌组织和 56 例正常甲状腺组织的 RNA 谱。DESeq R 包分析了高通量测序数据中差异表达的 RNA。GO 和 KEGG 通路分析使用了 DAVID 6.8 和 ClusterProfile R 包。进行 Kaplan-Meier 生存统计和 Cox 回归分析。基于 miRDB、miRTarBase 和 TargetScan 数据库构建了甲状腺癌 ceRNA 网络。
有 1098 个 mRNA 与甲状腺癌相关；101 个 mRNA 与总生存期（OS）相关。多变量分析开发了一个风险评分系统，通过接受者操作特征（ROC）曲线分析，确定了具有 0.88 判别值的七个特征性 mRNA。ceRNA 网络包括 13 个 mRNA、31 个 lncRNA 和 7 个 miRNA。31 个 lncRNA 中有 4 个和所有 miRNA 下调，其余 RNA 上调。两个 lncRNA（MIR1281A2HG 和 OPCML-IT1）和一个 miRNA（miR-184）与甲状腺癌患者的 OS 显著相关。
甲状腺癌差异 RNA 表达谱分析用于构建 mRNA、lncRNA 和 miRNA 的 ceRNA 网络，该网络在评估预后方面具有潜力。
