胶质母细胞瘤中异常甲基化调控的蛋白编码基因识别研究

doi:10.3969/j.issn.1006-7795.2021.01.012

首都医科大学学报 ›› 2021, Vol. 42 ›› Issue (1): 71-76.doi: 10.3969/j.issn.1006-7795.2021.01.012

胶质母细胞瘤中异常甲基化调控的蛋白编码基因识别研究

嵇江淮¹, 赵潇潇¹, 李乾鹏¹, 安奕^2,3, 赵磊^2,3, 李冬果^1*

1.首都医科大学生物医学工程学院生物医学信息学系,北京 100069;
2.首都医科大学宣武医院麻醉手术科,北京 100053;
3.国家老年疾病临床研究中心,北京 100053

收稿日期:2019-07-12 出版日期:2021-02-21 发布日期:2021-02-02
基金资助:
北京市教育委员会科技发展计划一般项目 (KM201710025010), 首都医科大学基础临床合作项目[17JL61, 17JL(TTZX)10]。

Identification of protein-coding genes regulated by aberrant methylation in glioblastoma

Ji Jianghuai¹, Zhao Xiaoxiao¹, Li Qianpeng¹, An Yi^2,3, Zhao Lei^2,3, Li Dongguo^1*

1. Department of Biomedical Information, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China;
2. Department of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China;
3. National Clinical Research Center for Geriatric Disorders, Beijing 100053, China

Received:2019-07-12 Online:2021-02-21 Published:2021-02-02
Contact: ^*E-mail:ldg213@ccmu.edu.cn
Supported by:
Scientific Research Common Program of Beijing Municipal Commission of Education(KM201710025010), Basic Clinical Cooperation Project of Capital Medical University[17JL61, 17JL(TTZX)10].

摘要/Abstract

摘要： 目的通过整合多组学数据,识别胶质母细胞瘤(glioblastoma, GBM)中表达受DNA甲基化调控的蛋白编码基因(protein-coding genes,PCGs),评估不同甲基化模式下PCGs的生物学功能,挖掘与GBM预后相关的风险标志物。方法基于多组学数据,构建GBM中PCGs的DNA甲基化谱,筛选表达受异常甲基化调控的PCGs并进行功能富集分析。同时结合GBM临床数据,对筛选出的PCGs进行生存分析。结果识别出表达受异常甲基化调控的PCGs 630个,挖掘出51个与GBM预后相关的PCGs。结论系统识别GBM中潜在的表达受异常甲基化调控的PCGs,并对识别GBM风险标志物和潜在的治疗靶点提出了新的认识。

关键词: 胶质母细胞瘤, 蛋白编码基因, DNA甲基化, 调控, 生物标志物

Abstract: Objective By integrating multi-omics data, we identified protein-coding genes (PCGs) regulated by DNA methylation in glioblastoma (GBM), evaluated the biological functions of PCGs under different methylation patterns, and explored the risk markers associated with GBM prognosis. Methods Based on multi-omics data, we constructed the DNA methylation profiles of PCGs in GBM, screened PCGs whose expression was regulated by aberrant methylation and functional enrichment analysis. Meanwhile, we performed survival analysis of the selected PCGs by combining with GBM clinical data. Results 630 PCGs whose expression was regulated by aberrant methylation were identified, and 51 PCGs associated with GBM prognosis were excavated. Conclusions The potential PCGs whose expression were regulated by aberrant methylation were identified systematically, and we provide a novel insight for identifying GBM risk markers and potential therapeutic targets.

Key words: glioblastoma, protein-coding genes, DNA methylation, regulation, biomarkers

中图分类号:

R318

嵇江淮, 赵潇潇, 李乾鹏, 安奕, 赵磊, 李冬果. 胶质母细胞瘤中异常甲基化调控的蛋白编码基因识别研究[J]. 首都医科大学学报, 2021, 42(1): 71-76.

Ji Jianghuai, Zhao Xiaoxiao, Li Qianpeng, An Yi, Zhao Lei, Li Dongguo. Identification of protein-coding genes regulated by aberrant methylation in glioblastoma[J]. Journal of Capital Medical University, 2021, 42(1): 71-76.

参考文献

[1] 康恩铭,章薇,章翔. 胶质母细胞瘤DNA甲基化研究进展[J]. 中华神经外科疾病研究杂志,2017,16(3):279-281.
[2] Gallego O. Nonsurgical treatment of recurrent glioblastoma[J]. Curr Oncol, 2015, 22(4): e273-281.
[3] Xiong J, Bing Z, Su Y, et al. An integrated mRNA and microRNA expression signature for glioblastoma multiforme prognosis[J]. PLoS One, 2014, 9(5): e98419.
[4] Liu Z, Niu Y, Xie M, et al. Gene expression profiling analysis reveals that DLG3 is down-regulated in glioblastoma[J]. J Neurooncol, 2014, 116(3): 465-476.
[5] Xing Z Y, Sun L G, Guo W J. Elevated expression of Notch-1 and EGFR induced apoptosis in glioblastoma multiforme patients[J]. Clin Neurol Neurosurg, 2015, 131: 54-58.
[6] Meng D, Chen Y, Zhao Y, et al. Expression and prognostic significance of TCTN1 in human glioblastoma[J]. J Transl Med, 2014, 12: 288.
[7] Jones P A. Functions of DNA methylation: islands, start sites, gene bodies and beyond[J]. Nat Revi Genet, 2012, 13(7): 484-492.
[8] Zhang C, Zhao H, Li J, et al. The Identification of specific methylation patterns across different cancers[J].PLoS One, 2015, 10(3): e0120361.
[9] Shah N, Lin B, Sibenaller Z, et al. Comprehensive analysis of MGMT promoter methylation: correlation with MGMT expression and clinical response in GBM[J]. PLoS One, 2011, 6(1): e16146.
[10]Sandoval J, Heyn H, Moran S, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome[J]. Epigenetics, 2011, 6(6): 692-702.
[11]Guintivano J, Aryec M J, kaminsky Z A. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression[J]. Epigenetics, 2013, 8(3): 290-302.
[12]Yang X, Gao L, Zhang S. Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns[J]. Briefings Bioinform, 2017,18(5):761-773.
[13]Harrow J, Frankish A, Gonzalez J M, et al. GENCODE: the reference human genome annotation for The ENCODE Project[J]. Genome Res, 2012, 22(9): 1760-1774.
[14]Robinson M D, McCarthy D J, Smyth G K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data[J]. Bioinformatics, 2009, 26(1): 139-140.
[15]Di Lena P, Sala C, Prodi A, et al. Missing value estimation methods for DNA methylation data[J]. Bioinformatics, 2019, 35(19): 3786-3793.
[16]Zhi H, Ning S, Li X, et al. A novel reannotation strategy for dissecting DNA methylation patterns of human long intergenic non-coding RNAs in cancers[J]. Nucleic Acids Res, 2014, 42(13): 8258-8270.
[17]Xiao W, Cao Y, Long H, et al. Genome-wide DNA methylation patterns analysis of noncoding RNAs in temporal lobe epilepsy patients[J]. Mol Neurobiol, 2018, 55(1): 793-803.
[18]Ritchie M E, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7): e47.
[19]Yu G, Wang L, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
[20]王瑞娴,徐建红. 基因组DNA甲基化及组蛋白甲基化[J]. 遗传,2014,36(3): 191-199.
[21]Etcheverry A, Aubry M, de Tayrac M, et al. DNA methylation in glioblastoma: impact on gene expression and clinical outcome[J]. BMC Genomics, 2010, 11: 701.
[22]Ventero M P, Fuentes-Baile M, Quereda C, et al. Radiotherapy resistance acquisition in Glioblastoma. Role of SOCS1 and SOCS3[J]. PLoS One, 2019, 14(2): e212581.
[23]Ladha J, Sinha S, Bhat V, et al. Identification of genomic targets of transcription factor AEBP1 and its role in survival of glioma cells[J]. Mol Cancer Res, 2012, 10(8): 1039-1051.

胶质母细胞瘤中异常甲基化调控的蛋白编码基因识别研究

Identification of protein-coding genes regulated by aberrant methylation in glioblastoma

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