放射治疗
灵敏度(控制系统)
DNA甲基化
签名(拓扑)
胶质瘤
甲基化
计算生物学
计算机科学
生物信息学
生物
医学
癌症研究
内科学
基因
遗传学
基因表达
数学
工程类
电子工程
几何学
作者
Yuemei Feng,Guanzhang Li,Zhongcheng Shi,Xu Yan,Zhiliang Wang,Haoyu Jiang,Ye Chen,Renpeng Li,You Zhai,Yuanhao Chang,Wei Zhang,Fang Yuan
标识
DOI:10.1038/s41598-020-77259-9
摘要
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.
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