Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Specific glioblastoma multiforme prognostic-subtype distinctions based on DNA methylation patterns

Cancer Gene Therapy volume 27, pages 702–714 (2020)Cite this article

Subjects

Abstract

DNA methylation is an important regulator of gene expression, and plays a significant role in carcinogenesis in the brain. Here, we explored specific prognosis-subtypes based on DNA methylation status using 138 Glioblastoma Multiforme (GBM) samples from The Cancer Genome Atlas (TCGA) database. The methylation profiles of 11,637 CpG sites that significantly correlated with survival in the training set were employed for consensus clustering. We identified three GBM molecular subtypes, and their survival curves were distinct from each other. Furthermore, ten feature CpG sites were obtained on conducting a weighted gene co-expression network analysis (WGCNA) of the CpG sites. We were able to classify the samples into high- and low-methylation groups, and classified the prognosis information of the samples after cluster analysis of the training set samples using the hierarchical clustering algorithm. Similar results were obtained in the test set and clinical GBM specimens. Finally, we found that a positive relationship existed between methylation level and sensitivity to temozolomide (or radiotherapy) or anti-migration ability of GBM cells. Taken together, these results suggest that the model constructed in this study could help explain the heterogeneity of previous molecular subgroups in GBM and can provide guidance to clinicians regarding the prognosis of GBM.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Flowchart describing the schematic overview of the study design.
Fig. 2: Consensus matrix for DNA methylation classification with the corresponding heatmap.
Fig. 3: Survival, age distribution, demographic, and molecular characteristics of each sample in the three molecular subtypes.
Fig. 4: WGCNA analysis of CpG Sites.
Fig. 5: Relationship network of the feature methylation sites.
Fig. 6: Clustering and survival results of the ten CpG sites in the training and test sets.
Fig. 7: Clinical application of the final prognostic predictor in ten feature genes.

Similar content being viewed by others

References

  1. Friedmann-Morvinski D. Glioblastoma heterogeneity and cancer cell plasticity. Crit Rev Oncog. 2014;19:327–36.

    Article  PubMed  Google Scholar 

  2. Lee E, Yong RL, Paddison P, Zhu J. Comparison of glioblastoma (GBM) molecular classification methods. Semin Cancer Biol. 2018;53:201–11.

    Article  CAS  PubMed  Google Scholar 

  3. Crespo I, Vital AL, Gonzalez-Tablas M, Patino Mdel C, Otero A, Lopes MC, et al. Molecular and genomic alterations in glioblastoma multiforme. Am J Pathol. 2015;185:1820–33.

    Article  CAS  PubMed  Google Scholar 

  4. Hill C, Hunter SB, Brat DJ. Genetic markers in glioblastoma: prognostic significance and future therapeutic implications. Adv Anat Pathol. 2003;10:212–7.

    Article  CAS  PubMed  Google Scholar 

  5. Lieberman F. Glioblastoma update: molecular biology, diagnosis, treatment, response assessment, and translational clinical trials. F1000Res 2017;6:1892.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Sasmita AO, Wong YP, Ling APK. Biomarkers and therapeutic advances in glioblastoma multiforme. Asia Pac J Clin Oncol. 2018;14:40–51.

    Article  PubMed  Google Scholar 

  7. Han TS, Ban HS, Hur K, Cho HS. The epigenetic regulation of HCC metastasis. Int J Mol Sci. 2018;19:E3978.

    Article  PubMed  Google Scholar 

  8. Porten SP. Epigenetic alterations in bladder cancer. Curr Urol Rep. 2018;19:102.

    Article  PubMed  Google Scholar 

  9. Romani M, Pistillo MP, Banelli B. Epigenetic targeting of glioblastoma. Front Oncol. 2018;8:448.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Muhammad JS, Khan MR, Ghias K. DNA methylation as an epigenetic regulator of gallbladder cancer: an overview. Int J Surg. 2018;53:178–83.

    Article  PubMed  Google Scholar 

  11. Yamashita K, Hosoda K, Nishizawa N, Katoh H, Watanabe M. Epigenetic biomarkers of promoter DNA methylation in the new era of cancer treatment. Cancer Sci. 2018;109:3695–706.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V, et al. Author correction: analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol. 2018;15:467.

    Article  PubMed  Google Scholar 

  13. Gustafsson JR, Katsioudi G, Degn M, Ejlerskov P, Issazadeh-Navikas S, Kornum BR. DNMT1 regulates expression of MHC class I in post-mitotic neurons. Mol Brain. 2018;11:36.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Li Z, Takenobu H, Setyawati AN, Akita N, Haruta M, Satoh S, et al. EZH2 regulates neuroblastoma cell differentiation via NTRK1 promoter epigenetic modifications. Oncogene. 2018;37:2714–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Perez RF, Tejedor JR, Bayon GF, Fernandez AF, Fraga MF. Distinct chromatin signatures of DNA hypomethylation in aging and cancer. Aging Cell. 2018;17:e12744.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Kim DS, Lee WK, Park JY. Promoter methylation of Wrap53alpha, an antisense transcript ofp53, is associated with the poor prognosis of patients with non-small cell lung cancer. Oncol Lett. 2018;16:5823–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Li Y, Gong Y, Ning X, Peng D, Liu L, He S, et al. Downregulation of CLDN7 due to promoter hypermethylation is associated with human clear cell renal cell carcinoma progression and poor prognosis. J Exp Clin Cancer Res. 2018;37:276.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hao X, Luo H, Krawczyk M, Wei W, Wang W, Wang J, et al. DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci USA. 2017;114:7414–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bell EH, Zhang P, Fisher BJ, Macdonald DR, McElroy JP, Lesser GJ, et al. Association of MGMT promoter methylation status with survival outcomes in patients with high-risk glioma treated with radiotherapy and temozolomide: an analysis from the NRG oncology/RTOG 0424 Trial. JAMA Oncol. 2018;4:1405–9.

    Article  PubMed  Google Scholar 

  20. Johannessen LE, Brandal P, Myklebust TA, Heim S, Micci F, Panagopoulos I. MGMT gene promoter methylation status—assessment of two pyrosequencing kits and three methylation-specific PCR methods for their predictive capacity in glioblastomas. Cancer Genomics Proteom. 2018;15:437–46.

    Article  CAS  Google Scholar 

  21. Kyrochristos ID, Ziogas DE, Roukos DH. Dynamic genome and transcriptional network-based biomarkers and drugs: precision in breast cancer therapy. Med Res Rev. 2019;39:1205–27.

    Article  CAS  PubMed  Google Scholar 

  22. Liu S, Xu C, Zhang Y, Liu J, Yu B, Liu X, et al. Feature selection of gene expression data for Cancer classification using double RBF-kernels. BMC Bioinf. 2018;19:396.

    Article  Google Scholar 

  23. Xiao M, Liu L, Zhang S, Yang X, Wang Y. Cancer stem cell biomarkers for head and neck squamous cell carcinoma: a bioinformatic analysis. Oncol Rep. 2018;40:3843–51.

    CAS  PubMed  Google Scholar 

  24. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The SVA package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012;28:882–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010;26:1572–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 2008;9:559.

    Article  CAS  Google Scholar 

  29. Aquilanti E, Miller J, Santagata S, Cahill DP, Brastianos PK. Updates in prognostic markers for gliomas. Neuro Oncol 2018;20:vii17–vii26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kumar R, Liu APY, Orr BA, Northcott PA, Robinson GW. Advances in the classification of pediatric brain tumors through DNA methylation profiling: from research tool to frontline diagnostic. Cancer 2018;124:4168–80.

    Article  PubMed  Google Scholar 

  31. Villanueva A, Portela A, Sayols S, Battiston C, Hoshida Y, Mendez-Gonzalez J, et al. DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology 2015;61:1945–56.

    Article  CAS  PubMed  Google Scholar 

  32. Stefansson OA, Moran S, Gomez A, Sayols S, Arribas-Jorba C, Sandoval J, et al. A DNA methylation-based definition of biologically distinct breast cancer subtypes. Mol Oncol. 2015;9:555–68.

    Article  CAS  PubMed  Google Scholar 

  33. Exner R, Pulverer W, Diem M, Spaller L, Woltering L, Schreiber M, et al. Potential of DNA methylation in rectal cancer as diagnostic and prognostic biomarkers. Br J Cancer 2015;113:1035–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA, et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 2016;164:550–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. de Souza CF, Sabedot TS, Malta TM, Stetson L, Morozova O, Sokolov A, et al. A distinct DNA methylation shift in a subset of glioma CpG island methylator phenotypes during tumor recurrence. Cell Rep. 2018;23:637–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Leonhardt H, Cardoso MC. DNA methylation, nuclear structure, gene expression and cancer. J Cell Biochem Suppl. 2000;Suppl 35:78–83.

    Article  CAS  PubMed  Google Scholar 

  37. Rountree MR, Bachman KE, Herman JG, Baylin SB. DNA methylation, chromatin inheritance, and cancer. Oncogene 2001;20:3156–65.

    Article  CAS  PubMed  Google Scholar 

  38. Kawano H, Saeki H, Kitao H, Tsuda Y, Otsu H, Ando K, et al. Chromosomal instability associated with global DNA hypomethylation is associated with the initiation and progression of esophageal squamous cell carcinoma. Ann Surg Oncol. 2014;21(Suppl 4):S696–702.

    Article  PubMed  Google Scholar 

  39. El-Osta A. The rise and fall of genomic methylation in cancer. Leukemia. 2004;18:233–7.

    Article  CAS  PubMed  Google Scholar 

  40. Issa JP. DNA methylation as a therapeutic target in cancer. Clin Cancer Res. 2007;13:1634–7.

    Article  CAS  PubMed  Google Scholar 

  41. Flavahan WA, Drier Y, Liau BB, Gillespie SM, Venteicher AS, Stemmer-Rachamimov AO, et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature. 2016;529:110–4.

    Article  CAS  PubMed  Google Scholar 

  42. Hashimoto Y, Zumwalt TJ, Goel A. DNA methylation patterns as noninvasive biomarkers and targets of epigenetic therapies in colorectal cancer. Epigenomics. 2016;8:685–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kaminska K, Nalejska E, Kubiak M, Wojtysiak J, Zolna L, Kowalewski J, et al. Prognostic and predictive epigenetic biomarkers in oncology. Mol Diagn Ther. 2019;23:83–95.

    Article  CAS  PubMed  Google Scholar 

  44. Zhu X, Wang Y, Tan L, Fu X. The pivotal role of DNA methylation in the radio-sensitivity of tumor radiotherapy. Cancer Med. 2018;7:3812–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Paul Y, Mondal B, Patil V, Somasundaram K. DNA methylation signatures for 2016 WHO classification subtypes of diffuse gliomas. Clin Epigenetics. 2017;9:32.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yin AA, Lu N, Etcheverry A, Aubry M, Barnholtz-Sloan J, Zhang LH, et al. A novel prognostic six-CpG signature in glioblastomas. CNS Neurosci Ther. 2018;24:167–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kloosterhof NK, de Rooi JJ, Kros M, Eilers PH, Sillevis Smitt PA, van den Bent MJ, et al. Molecular subtypes of glioma identified by genome-wide methylation profiling. Genes Chromosomes Cancer. 2013;52:665–74.

    CAS  PubMed  Google Scholar 

  48. Wu X, Rauch TA, Zhong X, Bennett WP, Latif F, Krex D, et al. CpG island hypermethylation in human astrocytomas. Cancer Res. 2010;70:2718–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yu ZQ, Zhang BL, Ren QX, Wang JC, Yu RT, Qu DW, et al. Changes in transcriptional factor binding capacity resulting from promoter region methylation induce aberrantly high GDNF expression in human glioma. Mol Neurobiol. 2013;48:571–80.

    Article  CAS  PubMed  Google Scholar 

  50. Pangeni RP, Zhang Z, Alvarez AA, Wan X, Sastry N, Lu S, et al. Genome-wide methylomic and transcriptomic analyses identify subtype-specific epigenetic signatures commonly dysregulated in glioma stem cells and glioblastoma. Epigenetics. 2018;13:432–48.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wenger A, Ferreyra Vega S, Kling T, Bontell TO, Jakola AS, Caren H. Intratumor DNA methylation heterogeneity in glioblastoma: implications for DNA methylation-based classification. Neuro Oncol. 2019;21:616–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (81872066, 31571433, 81773131, and 81972635), Innovative Program of Development Foundation of Hefei Center for Physical Science and Technology (2018CXFX004 and 2017FXCX008), the CASHIPS Director’s Fund (YZJJ201704) and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2018487).

Author information

Authors and Affiliations

  1. Anhui Province Key Laboratory of Medical Physics and Technology; Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, Hefei, Anhui, 230031, China

    Huihui Ma, Chenggang Zhao, Zhiyang Zhao, Lizhu Hu, Hongzhi Wang, Zhiyou Fang & Xueran Chen

  2. University of Science and Technology of China, No. 96, Jin Zhai Road, Hefei, Anhui, 230026, China

    Huihui Ma, Chenggang Zhao, Zhiyang Zhao, Lizhu Hu & Yuejin Wu

  3. Department of Molecular Pathology, Hefei Cancer Hospital, Chinese Academy of Sciences, No. 350, Shushan Hu Road, Hefei, Anhui, 230031, China

    Fang Ye, Hongzhi Wang, Zhiyou Fang & Xueran Chen

  4. Institute of Technical Biology Agriculture Engineering, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, Hefei, Anhui, 230031, China

    Yuejin Wu

Authors
  1. Huihui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenggang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiyang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lizhu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongzhi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhiyou Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yuejin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xueran Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XRC and LJW conceived and designed the experiments. HHM, CGZ, ZYZ, and LZH collected the data. HHM, CGZ, and FY performed the analysis. XRC, LJW, HZW, and ZYF participated in the discussion of the algorithm. HHM and XRC prepared and edited the paper. All authors have read and approved the final paper.

Corresponding authors

Correspondence to Yuejin Wu or Xueran Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics statement

The protocol for this article was approved by the Institutional Review Board of Hefei Institutes of Physical Science, CAS.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, H., Zhao, C., Zhao, Z. et al. Specific glioblastoma multiforme prognostic-subtype distinctions based on DNA methylation patterns. Cancer Gene Ther 27, 702–714 (2020). https://doi.org/10.1038/s41417-019-0142-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41417-019-0142-6

This article is cited by

Search

Advanced search

Quick links