IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36005-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36005-1
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-36005-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lingling Li & Dongxian Jiang & Qiao Zhang & Hui Liu & Fujiang Xu & Chunmei Guo & Zhaoyu Qin & Haixing Wang & Jinwen Feng & Yang Liu & Weijie Chen & Xue Zhang & Lin Bai & Sha Tian & Subei Tan & Chen Xu, 2023. "Integrative proteogenomic characterization of early esophageal cancer," Nature Communications, Nature, vol. 14(1), pages 1-28, December.
    2. Lingling Li & Dongxian Jiang & Hui Liu & Chunmei Guo & Rui Zhao & Qiao Zhang & Chen Xu & Zhaoyu Qin & Jinwen Feng & Yang Liu & Haixing Wang & Weijie Chen & Xue Zhang & Bin Li & Lin Bai & Sha Tian & Su, 2023. "Comprehensive proteogenomic characterization of early duodenal cancer reveals the carcinogenesis tracks of different subtypes," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    3. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    4. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    5. Spelta, A. & Pecora, N. & Rovira Kaltwasser, P., 2019. "Identifying Systemically Important Banks: A temporal approach for macroprudential policies," Journal of Policy Modeling, Elsevier, vol. 41(1), pages 197-218.
    6. Paul Fogel & Yann Gaston-Mathé & Douglas Hawkins & Fajwel Fogel & George Luta & S. Stanley Young, 2016. "Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health," IJERPH, MDPI, vol. 13(5), pages 1-14, May.
    7. Le Thi Khanh Hien & Duy Nhat Phan & Nicolas Gillis, 2022. "Inertial alternating direction method of multipliers for non-convex non-smooth optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 247-285, September.
    8. Yuping Chen & Jo-Hsi Huang & Connie Phong & James E. Ferrell, 2024. "Viscosity-dependent control of protein synthesis and degradation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    9. Jingfeng Guo & Chao Zheng & Shanshan Li & Yutong Jia & Bin Liu, 2022. "BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    10. Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    11. Zhang, Lifeng & Chao, Xiangrui & Qian, Qian & Jing, Fuying, 2022. "Credit evaluation solutions for social groups with poor services in financial inclusion: A technical forecasting method," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    12. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
    13. Gábor Csárdi & Alexander Franks & David S Choi & Edoardo M Airoldi & D Allan Drummond, 2015. "Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast," PLOS Genetics, Public Library of Science, vol. 11(5), pages 1-32, May.
    14. Anna Luiza Silva Almeida Vicente & Alexei Novoloaca & Vincent Cahais & Zainab Awada & Cyrille Cuenin & Natália Spitz & André Lopes Carvalho & Adriane Feijó Evangelista & Camila Souza Crovador & Rui Ma, 2022. "Cutaneous and acral melanoma cross-OMICs reveals prognostic cancer drivers associated with pathobiology and ultraviolet exposure," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    15. Takehiro Sano & Tsuyoshi Migita & Norikazu Takahashi, 2022. "A novel update rule of HALS algorithm for nonnegative matrix factorization and Zangwill’s global convergence," Journal of Global Optimization, Springer, vol. 84(3), pages 755-781, November.
    16. Adam R. Pines & Bart Larsen & Zaixu Cui & Valerie J. Sydnor & Maxwell A. Bertolero & Azeez Adebimpe & Aaron F. Alexander-Bloch & Christos Davatzikos & Damien A. Fair & Ruben C. Gur & Raquel E. Gur & H, 2022. "Dissociable multi-scale patterns of development in personalized brain networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    17. Xiangli Li & Hongwei Liu & Xiuyun Zheng, 2012. "Non-monotone projection gradient method for non-negative matrix factorization," Computational Optimization and Applications, Springer, vol. 51(3), pages 1163-1171, April.
    18. Ding, Chris & Li, Tao & Peng, Wei, 2008. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3913-3927, April.
    19. Dominik P. Koller & Michael Schirner & Petra Ritter, 2024. "Human connectome topology directs cortical traveling waves and shapes frequency gradients," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    20. Abdul Suleman, 2017. "On ill-conceived initialization in archetypal analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 785-808, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36005-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.