Cuproplasia-related gene signature: Prognostic insights for glioma therapy.

IF 3.7 Q1 CLINICAL NEUROLOGY
Neuro-oncology advances Pub Date : 2024-12-23 eCollection Date: 2025-01-01 DOI:10.1093/noajnl/vdae233
Toni Rose Jue, Joseph Descallar, Vu Viet Hoang Pham, Jessica Lilian Bell, Tyler Shai-Hee, Riccardo Cazzolli, Sumanth Nagabushan, Eng-Siew Koh, Orazio Vittorio
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引用次数: 0

Abstract

Background: Adult-type diffuse gliomas encompass nearly a quarter of all primary tumors found in the CNS, including astrocytoma, oligodendroglioma, and glioblastoma. Histopathological tumor grade and molecular profile distinctly impact patient survival. Despite treatment advancements, patients with recurrent glioma have a very poor clinical outcome, warranting improved risk stratification to determine therapeutic interventions. Various studies have shown that copper is a notable trace element that is crucial for biological processes and has been shown to display pro-tumorigenic functions in cancer, particularly gliomas.

Methods: Differential gene expression, Cox regression, and least absolute shrinkage and selection operator regression were used to identify 19 copper-homeostasis-related gene signatures using TCGA lower-grade glioma and glioblastoma (GBM) cohorts. The GLASS Consortium dataset was used as an independent validation cohort. Enrichment analysis revealed the involvement of the signature in various cancer-related pathways and biological processes. Using this CHRG signature, a risk score model and a nomogram were developed to predict survival in glioma patients.

Results: Our prognostic CHRG signature stratified patients into high- and low-risk groups, demonstrating robust predictive performance. High-risk groups showed poorer survival outcomes. The nomogram model integrating CHRG signature and clinical features accurately predicted 1-, 3-, and 5-year survival rates in both training and test sets.

Conclusions: The identified 19-gene CHRG signature holds promise as a prognostic tool, enabling accurate risk stratification and survival prediction in glioma patients. Integrating this signature with clinical characteristics enhances prognostic accuracy, underscoring its potential clinical utility in optimizing therapeutic strategies and patient care in glioma management.

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