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 - Cai, Zehao; Yu, Chunna; Li, Shenglan; Wang, Can; Fan, Yaqiong; Ji, Qiang; Chen, Feng; Li, Wenbin (Frontiers in Oncology, 2021)
Purpose: Glioma is a classical type of primary brain tumors that is most common seen in adults, and its high heterogeneity used to be referential criteria for subgroup classification. Glioma was 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 presented a new subgroup classification for glioma based on the expression level of Gβ and Gγ genes to complement studies on glioma and Gβγ subunits, and to help clinicians 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 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 used to compare the OS of the patients between GNB2 group and non-GNB2 group. Univariate Cox regression analysis was used to investigate the prognostic role of each Gβ and Gγ gene. KEGG and ssGSEA analysis were used 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 have 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 predict patients prognosis. The unique relationships between subgroups and tumor-related pathways can be further investigated to identify therapeutic Gβγ heterodimer targets.