首都医科大学学报 ›› 2022, Vol. 43 ›› Issue (1): 113-119.doi: 10.3969/j.issn.1006-7795.2022.01.019

• 基础研究 • 上一篇    下一篇

胶质母细胞瘤中差异甲基化增强子区域调控的蛋白编码基因识别研究

赵潇潇1,2, 于秋红3*, 嵇江淮4,5, 王世佳1,2, 王仁东1,2, 李冬果1,2*   

  1. 1.首都医科大学生物医学工程学院,北京 100069;
    2.首都医科大学临床生物力学基础研究北京市重点实验室,北京 100069;
    3.首都医科大学附属北京天坛医院高压氧科,北京 100070;
    4.浙江肿瘤医院放射物理科,杭州 310022;
    5.浙江省放射肿瘤学重点实验室,杭州 310022
  • 收稿日期:2021-07-06 出版日期:2022-02-21 发布日期:2022-01-27

Identification of protein-coding genes regulated by differential DNA methylation enhancer regions in glioblastoma

Zhao Xiaoxiao1,2, Yu Qiuhong3*, Ji Jianghuai4,5, Wang Shijia1,2, Wang Rendong1,2, Li Dongguo1,2*   

  1. 1. School of Biomedical Engineering, Capital Medical University, Beijing 100069, China;
    2. Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical, Capital Medical University, Beijing 100069, China;
    3. Department of Hyperbaric Oxygen, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China;
    4. Department of Radiation Physics Zhejiang Cancer Hospital, Hangzhou 310022,China;
    5. Zhejiang Key Laboratory of Radiation Oncology, Hangzhou 310022, China
  • Received:2021-07-06 Online:2022-02-21 Published:2022-01-27
  • Contact: * E-mail:yuqiuhong@bjtth.org;dg213@ccmu.edu.cn

摘要: 目的 通过对胶质母细胞瘤 (glioblastoma, GBM) 的 DNA甲基化数据和表达数据进行整合和分析,识别表达水平可能受差异甲基化增强子区域 (differential methylation enhancer regions, DMERs)调控的蛋白编码基因 (protein-coding genes,PCGs),预测其中受DMERs调控的PCGs在GBM中执行的功能,挖掘潜在的GBM预后相关的生物标志物。方法 基于公共疾病数据库中的甲基化数据和表达数据,运用一种计算策略构建全基因组增强子区域,筛选GBM中DMERs,识别表达可能受DMERs调控的PCGs并对其进行富集分析。基于患者的临床信息与对应样本的表达数据,对鉴别出的PCGs进行Kaplan-Meier预后分析。结果 筛选出16 287个DMERs,本研究鉴别出795对DMER-PCGs,其中包含有593个低甲基化增强子区域,82个高甲基化增强子区域和642个PCGs,挖掘出45个与GBM整体存活相关的PCGs。结论 本研究进一步加深了对GBM中增强子甲基化调控模式的理解,并为开发用于GBM诊疗的新型生物标志物和靶标提供帮助。

关键词: 多形性胶质母细胞瘤, DNA甲基化, 增强子区域, 表观遗传调控, 风险标志物

Abstract: Objective By integrating and analyzing DNA methylation data and expression data in glioblastoma (GBM), we identified protein-coding genes (PCGs) that might be regulated by differential DNA methylation enhancer regions (DMERs) in GBM, predicted the biological functions of PCGs regulated by DMERs in GBM, and explored the potential biomarkers related to the GBM prognosis. Methods Based on methylation data and expression data in public disease databases, we used a computational strategy to construct genome-wide enhancer regions and screened for DMERS in GBM. We identified PCGs whose expression might be regulated by DMERs and performed enrichment analysis on these PCGs. Based on the patient's clinical information and the expression data of the corresponding samples, we performed Kaplan-Meier prognostic analysis on the identified PCGs. Results We screened 16 287 DMERs and identified 795 pairs of DMER-PCGs in this study, including 593 hypomethylated enhancers, 82 hypermethylated enhancers and 642 PCGs. We excavated 45 PCGs that were significantly related to the overall survival of GBM. Conclusion This study has further deepened the understanding of the regulatory pattern of enhancer methylation in GBM, and provided assistance for the development of novel biomarkers and targets for diagnosis and treatment of GBM.

Key words: glioblastoma, DNA methylation, enhancer region, epigenetic regulation, risk markers

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