Uncovering G Protein-Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis

Low-grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein-coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR-based outcome-predicting model for LGG.

Based on TCGA LGG data, the enrichment scores of GPCR genes were calculated via GSVA, and molecular subtypes were recognized through consensus clustering. Hub GPCR genes were screened via weighted gene co-expression network analysis and LASSO regression, enabling the construction of the GPCR index score (GPCRS) model, validated in an independent CGGA cohort. Patients were stratified into high- and low-risk groups accurately for mutation analysis, immune microenvironment assessment, and functional enrichment analysis, comprehensively elucidating the clinical significance of the GPCRS model.

LGG was classified into two subtypes based on GPCR-related gene expression. Using WGCNA and machine learning, nine hub GPCR genes (SSTR2, GPR61, SSTR1, OR2H2, CHRM4, P2RY2, GPR63, OPN3, GRPR) were identified. The GPCRS model effectively predicted prognosis, with area under the curve values for 1-, 3-, and 5-year overall survival, respectively, above 0.78, 0.72, and 0.71. Functional enrichment and immune analyses showed that the high-risk group is enriched in embryonic development and cytokine-related pathways, with a more complex immune microenvironment. Whereas the low-risk group is focused on GPCR signaling and neuro-related processes. Mutation analysis revealed differences in mutation profiles and co-mutation patterns between risk groups.

The GPCRS model serves as a reliable prognostic biomarker, providing insights into LGG molecular subtyping and potential targets for precision therapy.