Department of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Neoplasma. 2019 May 23;66(3):459-469. doi: 10.4149/neo_2018_181008N746.
We aim to identify novel molecular signatures for prognosis prediction of glioblastoma multiforme (GBM). The expression and microarray data of GBM were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Differentially expressed mRNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) between GBM and normal samples were identified by differential expression analysis using Bayesian T-test. Functional enrichment analysis was performed to identify GBM associated functions and pathways. A subset of signature mRNAs was selected from differentially expressed mRNAs and used to build a risk model for GBM using random survival forest (RSF) method. The performance of the model in prognosis prediction was validated using an independent validation dataset. A competing endogenous RNA (ceRNA) network was then constructed and key prognostic markers were identified from the network by survival analysis. In total, 905 mRNAs, 24 miRNAs and 403 lncRNAs were identified to be differentially expressed between GBM and normal samples. Functional and pathway items such as p53 signaling and PI3K/Akt signaling were significantly enriched by differentially expressed mRNAs. The RSF risk model showed a high performance in prognosis prediction for both training and validation dataset. The ceRNA network provided a comprehensive view of the interplays between differentially expressed mRNAs, miRNAs and lncRNAs. Among the ceRNA network, p21 (RAC1) activated kinase 1 (PAK1) and synaptic vesicle glycoprotein 2B (SV2B) were identified as key prognosis associated markers. The RSF risk model and key prognostic markers may contribute to GBM diagnosis in future clinical practice.
我们旨在确定胶质母细胞瘤（GBM）预后预测的新分子特征。从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载了 GBM 的表达和微阵列数据。通过贝叶斯 T 检验进行差异表达分析，确定了 GBM 与正常样本之间差异表达的信使 RNA（mRNA）、微小 RNA（miRNA）和长链非编码 RNA（lncRNA）。进行功能富集分析，以确定与 GBM 相关的功能和途径。从差异表达的 mRNAs 中选择一组特征 mRNAs，使用随机生存森林（RSF）方法构建 GBM 的风险模型。使用独立验证数据集验证模型在预后预测中的性能。然后构建竞争性内源性 RNA（ceRNA）网络，并通过生存分析从网络中确定关键预后标志物。总共鉴定出 905 个 mRNAs、24 个 miRNA 和 403 个 lncRNA 在 GBM 和正常样本之间差异表达。差异表达的 mRNAs 显著富集了 p53 信号和 PI3K/Akt 信号等功能和途径项目。RSF 风险模型在训练和验证数据集的预后预测中均表现出较高的性能。ceRNA 网络提供了差异表达的 mRNAs、miRNA 和 lncRNA 之间相互作用的全面视图。在 ceRNA 网络中，鉴定出 p21（RAC1）激活激酶 1（PAK1）和突触小泡糖蛋白 2B（SV2B）作为关键预后相关标志物。RSF 风险模型和关键预后标志物可能有助于未来的临床实践中的 GBM 诊断。
