Evidence map›Paper›PMID 42163039›Full record

ArticleAnnals of clinical and translational neurology2026

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

Jun Yang, Dongxu Zhang, Dongyuan Liu, Da Lin, Hao Wang

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In one paragraph

Article in Annals of clinical and translational neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jun YangDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Dongxu ZhangDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Dongyuan LiuDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Da LinDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Hao WangDepartment of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0009-0002-8320-289X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLow-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.

methodsBased 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.

resultsLGG 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.

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

Indexed as

GPCR index score modelG protein‐coupled receptors (GPCR)low‐grade gliomas

Identifiers

PMID42163039
PMCPMC13394093

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