BACKGROUND The prognosis of the glioblastoma (GBM) is dismal. This study aims to select an optimal RNA signature for prognostic prediction of GBM patients. METHODS For the training set, the long non-coding RNA (lncRNA) and mRNA expression profiles of 151 patients were downloaded from the TCGA. Differentially expressed mRNAs (DEGs) and lncRNAs (DE-lncRNAs) were identified between good prognosis and bad prognosis patients. Optimal prognostic mRNAs and lncRNAs were selected respectively, by using univariate Cox proportional-hazards (PH) regression model and LASSO Cox-PH model. Subsequently, four prognostic scoring models were built based on expression levels or expression status of the selected prognostic lncRNAs or mRNAs, separately. Each prognostic model was applied to the training set and an independent validation set. Function analysis was used to uncover the biological roles of these prognostic DEGs between different risk groups classified by the mRNA-based signature. RESULTS We obtained 261 DEGs and 33 DE-lncRNAs between good prognosis and bad prognosis patients. A panel of eight mRNAs and a combination of ten lncRNAs were determined as predictive RNAs by LASSO Cox-PH model. Among the four prognostic scoring models using the eight-mRNA signature or the ten-lncRNA signature, the one based on the expression levels of the eight mRNAs showed the greatest predictive power. The DEGs between different risk groups using the eight prognostic mRNAs were functionally involved in calcium signaling pathway, neuroactive ligand-receptor interaction pathway, and Wnt signaling pathway. CONCLUSION The eight-mRNA signature has greater prognostic value than the ten-lncRNA-based signature for GBM patients based on bioinformatics analysis.

中文翻译：

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在预测胶质母细胞瘤患者的预后方面，八-mRNA信号优于基于lncRNA的信号。

背景胶质母细胞瘤（GBM）的预后令人沮丧。这项研究的目的是为GBM患者的预后预测选择最佳的RNA标记。方法对于训练集，从TCGA下载了151位患者的长非编码RNA（lncRNA）和mRNA表达谱。在好预后和差预后患者之间鉴定出差异表达的mRNA（DEG）和lncRNA（DE-lncRNA）。使用单变量Cox比例风险回归模型和LASSO Cox-PH模型分别选择最佳的预后mRNA和lncRNA。随后，基于所选预后的lncRNA或mRNA的表达水平或表达状态分别建立了四个预后评分模型。每个预后模型都应用于训练集和独立的验证集。功能分析被用来揭示这些预后性DEG在基于mRNA的特征分类的不同风险组之间的生物学作用。结果我们获得了261个DEG和33个DE-lncRNAs，这些患者的预后良好。通过LASSO Cox-PH模型确定了一组8个mRNA和10个lncRNA的组合作为预测性RNA。在使用八-mRNA签名或十-lncRNA签名的四种预后评分模型中，一种基于八种mRNA的表达水平的模型显示出最大的预测能力。使用八个预后mRNA的不同风险组之间的DEG在功能上涉及钙信号通路，神经活性配体-受体相互作用通路和Wnt信号通路。