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Proteogenomics of diffuse gliomas reveal molecular subtypes associated with specific therapeutic targets and immune-evasion mechanisms

Author

Listed:
  • Yunzhi Wang

    (Fudan University)

  • Rongkui Luo

    (Fudan University)

  • Xuan Zhang

    (Fudan University Shanghai Cancer Center
    Fudan University)

  • Hang Xiang

    (Fudan University)

  • Bing Yang

    (Fudan University)

  • Jinwen Feng

    (Fudan University)

  • Mengjie Deng

    (Fudan University)

  • Peng Ran

    (Fudan University)

  • Akesu Sujie

    (Fudan University)

  • Fan Zhang

    (Fudan University)

  • Jiajun Zhu

    (Fudan University)

  • Subei Tan

    (Fudan University)

  • Tao Xie

    (Fudan University)

  • Pin Chen

    (Fudan University)

  • Zixiang Yu

    (Fudan University)

  • Yan Li

    (Fudan University)

  • Dongxian Jiang

    (Fudan University)

  • Xiaobiao Zhang

    (Fudan University)

  • Jian-Yuan Zhao

    (Shanghai Jiao Tong University School of Medicine
    Zhengzhou University)

  • Yingyong Hou

    (Fudan University)

  • Chen Ding

    (Fudan University)

Abstract

Diffuse gliomas are devastating brain tumors. Here, we perform a proteogenomic profiling of 213 retrospectively collected glioma tumors. Proteogenomic analysis reveals the downstream biological events leading by EGFR-, IDH1-, TP53-mutations. The comparative analysis illustrates the distinctive features of GBMs and LGGs, indicating CDK2 inhibitor might serve as a promising drug target for GBMs. Further proteogenomic integrative analysis combined with functional experiments highlight the cis-effect of EGFR alterations might lead to glioma tumor cell proliferation through ERK5 medicates nucleotide synthesis process. Proteome-based stratification of gliomas defines 3 proteomic subgroups (S-Ne, S-Pf, S-Im), which could serve as a complement to WHO subtypes, and would provide the essential framework for the utilization of specific targeted therapies for particular glioma subtypes. Immune clustering identifies three immune subtypes with distinctive immune cell types. Further analysis reveals higher EGFR alteration frequencies accounts for elevation of immune check point protein: PD-L1 and CD70 in T-cell infiltrated tumors.

Suggested Citation

  • Yunzhi Wang & Rongkui Luo & Xuan Zhang & Hang Xiang & Bing Yang & Jinwen Feng & Mengjie Deng & Peng Ran & Akesu Sujie & Fan Zhang & Jiajun Zhu & Subei Tan & Tao Xie & Pin Chen & Zixiang Yu & Yan Li & , 2023. "Proteogenomics of diffuse gliomas reveal molecular subtypes associated with specific therapeutic targets and immune-evasion mechanisms," Nature Communications, Nature, vol. 14(1), pages 1-32, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36005-1
    DOI: 10.1038/s41467-023-36005-1
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    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Björn Schwanhäusser & Dorothea Busse & Na Li & Gunnar Dittmar & Johannes Schuchhardt & Jana Wolf & Wei Chen & Matthias Selbach, 2011. "Global quantification of mammalian gene expression control," Nature, Nature, vol. 473(7347), pages 337-342, May.
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