Department of Pathophysiololgy, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Sci Rep. 2020 Nov 23;10(1):20406. doi: 10.1038/s41598-020-77259-9.
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和最恶性的癌症,放疗广泛应用于 GBM 的治疗;然而,不同患者对放疗的敏感性存在差异。为了解决这一临床难题,本研究基于基因组甲基化构建了一个放射敏感性预测标志物。本研究共纳入了 1044 例具有临床和甲基化微阵列数据的原发性 GBM 样本。使用 LASSO-COX、GSVA、Kaplan-Meier 生存曲线分析和 COX 回归进行预测模型的构建和验证。统计学分析和图形工作主要使用 R 编程语言。通过对甲基化和原发性 GBM 生存数据的综合分析,构建了一个新的预后和放射敏感性预测标志物。该标志物在 TCGA 和 CGGA 数据库中的预后预测中表现稳定。还探索了可能的机制,发现该标志物与 DNA 修复功能密切相关。最重要的是,该标志物可以预测 GBM 患者是否能从放疗中获益。总之,构建了一个基于五个甲基化探针的 GBM 患者放射敏感性预测标志物,具有很大的临床应用潜力。