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

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

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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.

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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.

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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).

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  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

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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.

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Correspondence to Yuejin Wu or Xueran Chen.

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

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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

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