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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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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