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International Conference on Intelligent Computing

ICIC 2022: Intelligent Computing Theories and Application pp 116–125Cite as

Glioblastoma Subtyping by Immuogenomics

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

Abstract

Objective: To analyze and establish immunophenotyping of glioblastoma (GBM) by genomics study and further explore its clinical application value. Methods: RNA-seq and clinical data from TCGA (from onset to May 4, 2021) were retrieved. All patients were grouped into three subsets by ssGSEA, and ESTIMATE algorithm was used to estimate stromal and immune cells and CIBERSORT was used to evaluate tumor microenvironment composition and immune cells subtypes. Results: RNA-seq and related clinical data of 174 GBM patients were retrieved. Patients were divided into high, medium and low-immunity groups by ssGSEA. The expression of PD-L1, CTAL4, TIGIT, and VEGF in the high and medium immunity group was higher than in the low-immunity group, while EGFR and HER2 in the medium-immunity group were lower than high/low-immunity group. GO enrichment found terms around vascular endothelial cell proliferation, immunoglobulin, G protein-coupled opioid receptor, glucocorticoid metabolic and etc. KEGG enrichment found items around diabetes, arthritis and viral protein interaction with cytokine and cytokine receptor etc. Conclusion: It is demonstrated that GBM patients in the high/medium immune group showed higher immunogenicity and anti-tumor immune activity, which provides a reference for GBM immunotherapy research.

Y. Li and C. Gopalakrishnan—Contributed equally to this work.

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Acknowledgement

This study was supported by Provincial Science and Technology Grant of Shanxi Province (20210302124588), Science and technology innovation project of Shanxi province universities (2019L0683).

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Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China

    Yanran Li & Jian Wang

  2. Changzhi Medical College, Changzhi, 046000, China

    Caixia Xu & Pengyong Han

  3. Quantitative Biology Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, 632014, Tamil Nadu, India

    Chandrasekhar Gopalakrishnan & Rajasekaran Ramalingam

Authors
  1. Yanran Li
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  2. Chandrasekhar Gopalakrishnan
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  3. Jian Wang
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  4. Rajasekaran Ramalingam
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  5. Caixia Xu
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  6. Pengyong Han
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Corresponding authors

Correspondence to Caixia Xu or Pengyong Han .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Li, Y., Gopalakrishnan, C., Wang, J., Ramalingam, R., Xu, C., Han, P. (2022). Glioblastoma Subtyping by Immuogenomics. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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