A Novel Classification of Glioma Subgroup, Which Is Highly Correlated With the Clinical Characteristics and Tumor Tissue Characteristics, Based on the Expression Levels of Gβ and Gγ Genes

Zehao Cai; Chunna Yu; Shenglan Li; Can Wang; Yaqiong Fan; Qiang Ji; Feng Chen; Wenbin Li

doi:10.3389/fonc.2021.685823

A Novel Classification of Glioma Subgroup, Which Is Highly Correlated With the Clinical Characteristics and Tumor Tissue Characteristics, Based on the Expression Levels of Gβ and Gγ Genes

Front Oncol. 2021 Jun 18:11:685823. doi: 10.3389/fonc.2021.685823. eCollection 2021.

Authors

Zehao Cai¹, Chunna Yu¹, Shenglan Li¹, Can Wang¹, Yaqiong Fan¹, Qiang Ji¹, Feng Chen¹, Wenbin Li¹

Affiliation

¹ Department of Neuro-oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical Unversity, Beijing, China.

Abstract

Purpose: Glioma is a classical type of primary brain tumors that is most common seen in adults, and its high heterogeneity used to be a reference standard for subgroup classification. Glioma has been diagnosed based on histopathology, grade, and molecular markers including IDH mutation, chromosome 1p/19q loss, and H3K27M mutation. This subgroup classification cannot fully meet the current needs of clinicians and researchers. We, therefore, present a new subgroup classification for glioma based on the expression levels of Gβ and Gγ genes to complement studies on glioma and Gβγ subunits, and to support clinicians to assess a patient's tumor status.

Methods: Glioma samples retrieved from the CGGA database and the TCGA database. We clustered the gliomas into different groups by using expression values of Gβ and Gγ genes extracted from RNA sequencing data. The Kaplan-Meier method with a two-sided log-rank test was adopted to compare the OS of the patients between GNB2 group and non-GNB2 group. Univariate Cox regression analysis was referred to in order to investigate the prognostic role of each Gβ and Gγ genes. KEGG and ssGSEA analysis were applied to identify highly activated pathways. The "estimate" package, "GSVA" package, and the online analytical tools CIBERSORTx were employed to evaluate immune cell infiltration in glioma samples.

Results: Three subgroups were identified. Each subgroup had its own specific pathway activation pattern and other biological characteristics. High M2 cell infiltration was observed in the GNB2 subgroup. Different subgroups displayed different sensitivities to chemotherapeutics. GNB2 subgroup predicted poor survival in patients with gliomas, especially in patients with LGG with mutation IDH and non-codeleted 1p19q.

Conclusion: The subgroup classification we proposed has great application value. It can be used to select chemotherapeutic drugs and the prognosis of patients with target gliomas. The unique relationships between subgroups and tumor-related pathways are worthy of further investigation to identify therapeutic Gβγ heterodimer targets.

Keywords: G protein subunit; RNA sequencing data; glioma; prognosis; subgroup classification.