Glioblastoma (GBM) is the most common and malignant cancer of the central nervous system, and radiotherapy is widely applied in GBM treatment; however, the sensitivity to radiotherapy varies in different patients. To solve this clinical dilemma, a radiosensitivity prediction signature was constructed in the present study based on genomic methylation. In total, 1044 primary GBM samples with clinical and methylation microarray data were involved in this study. LASSO-COX, GSVA, Kaplan–Meier survival curve analysis, and COX regression were performed for the construction and verification of predictive models. The R programming language was used as the main tool for statistical analysis and graphical work. Via the integration analysis of methylation and the survival data of primary GBM, a novel prognostic and radiosensitivity prediction signature was constructed. This signature was found to be stable in prognosis prediction in the TCGA and CGGA databases. The possible mechanism was also explored, and it was found that this signature is closely related to DNA repair functions. Most importantly, this signature could predict whether GBM patients could benefit from radiotherapy. In summary, a radiosensitivity prediction signature for GBM patients based on five methylated probes was constructed, and presents great potential for clinical application.

胶质母细胞瘤（GBM）是中枢神经系统最常见，最恶性的癌症，放疗广泛应用于GBM。但是，放疗的敏感性因患者而异。为了解决这一临床难题，在本研究中基于基因组甲基化构建了放射敏感性预测特征​​。总共有1044例具有临床和甲基化微阵列数据的主要GBM样本参与了这项研究。进行了LASSO-COX，GSVA，Kaplan-Meier生存曲线分析和COX回归以建立和验证预测模型。R编程语言被用作统计分析和图形工作的主要工具。通过甲基化的整合分析和原发性GBM的生存数据，构建了新的预后和放射敏感性预测特征​​。在TCGA和CGGA数据库中，发现该特征在预后预测中是稳定的。还探讨了可能的机制，并发现该签名与DNA修复功能密切相关。最重要的是，该签名可以预测GBM患者是否可以从放疗中受益。总之，构建了基于五种甲基化探针的GBM患者的放射敏感性预测特征​​，具有很大的临床应用潜力。这个特征可以预测GBM患者是否可以从放射治疗中受益。总之，构建了基于五种甲基化探针的GBM患者的放射敏感性预测特征​​，具有很大的临床应用潜力。这个特征可以预测GBM患者是否可以从放射治疗中受益。总之，构建了基于五种甲基化探针的GBM患者的放射敏感性预测特征​​，具有很大的临床应用潜力。