Glioblastoma (GBM) is a most aggressive primary cancer in brain with poor prognosis. This study aimed to identify novel tumor biomarkers with independent prognostic values in GBMs. The DNA methylation profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. Differential methylated genes (DMGs) were screened from recurrent GBM samples using limma package in R software. Functional enrichment analysis was performed to identify major biological processes and signaling pathways. Furthermore, critical DMGs associated with the prognosis of GBM were screened according to univariate and multivariate cox regression analysis. A risk score-based prognostic model was constructed for these DMGs and prediction ability of this model was validated in training and validation data set. In total, 495 DMGs were identified between recurrent samples and disease-free samples, including 356 significantly hypermethylated and 139 hypomethylated genes. Functional and pathway items for these DMGs were mainly related to sensory organ development, neuroactive ligand–receptor interaction, pathways in cancer, etc. Five genes with abnormal methylation level were significantly correlated with prognosis according to survival analysis, such as ALX1, KANK1, NUDT12, SNED1, and SVOP. Finally, the risk model provided an effective ability for prognosis prediction both in training and validation data set. We constructed a novel prognostic model for survival prediction of GBMs. In addition, we identified five DMGs as critical prognostic biomarkers in GBM progression.

中文翻译：

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新型基于DNA甲基化的预测胶质母细胞瘤生存的预后模型。

胶质母细胞瘤（GBM）是脑部最具侵略性的原发癌，预后不良。这项研究旨在鉴定在GBM中具有独立预后价值的新型肿瘤生物标志物。DNA甲基化图谱可从《癌症基因组图谱》和《基因表达综合》数据库下载。使用R软件中的limma软件包从复发的GBM样本中筛选出差异甲基化基因（DMG）。进行功能富集分析以鉴定主要的生物学过程和信号传导途径。此外，根据单因素和多因素cox回归分析筛选了与GBM预后相关的重要DMG。针对这些DMG构建了基于风险评分的预后模型，并在训练和验证数据集中验证了该模型的预测能力。总共，在复发样本和无病样本之间鉴定出495个DMG，包括356个显着超甲基化的基因和139个低甲基化的基因。这些DMG的功能和通路项目主要与感觉器官发育，神经活性配体-受体相互作用，癌症通路等有关。根据存活分析，甲基化水平异常的5个基因与预后显着相关，例如ALX1，KANK1，NUDT12，SNED1和SVOP。最后，该风险模型为训练和验证数据集中的预后预测提供了有效的能力。我们构建了一种新型的GBMs生存预测模型。此外，我们确定了五种DMG作为GBM进展中的关键预后生物标志物。