A cuproptosis-based prognostic model for predicting survival in low-grade glioma

Area of Science:

• Oncology
• Molecular Biology
• Genetics

Background:

• Low-grade gliomas (LGG) lack defined contributing variables for their formation and multiplication.
• Cuproptosis is an emerging cell death process with an unknown role in LGG.
• Understanding cuproptosis-related genes is crucial for advancing LGG therapeutic options.

Purpose of the Study:

• To investigate the role of cuproptosis-related genes in LGG.
• To develop a prognostic risk model for LGG based on cuproptosis-associated genes.
• To enhance therapeutic strategies for LGG patients.

Main Methods:

• Utilized TCGA and CGGA databases for risk model development and external validation.
• Employed Cox analysis (univariate, multivariate, LASSO) and ROC curves for predictive ability assessment.
• Performed immunohistochemistry, gene mutation analysis, functional enrichment, and Western blot to validate findings.

Main Results:

• A five-gene cuproptosis signature was developed, demonstrating significant differences in overall survival (OS) between high- and low-risk groups.
• The risk model showed high predictive performance and correlated with clinical characteristics and molecular subgroups.
• Immunological analysis indicated distinct immune microenvironments between risk groups, with implications for immunotherapy and chemotherapy.

Conclusions:

• A novel prediction model for LGG patients based on cuproptosis-associated genes was successfully developed.
• The risk model exhibits acceptable predictive performance for stratifying glioma patient prognosis.
• This study provides a foundation for targeted therapies and improved management of LGG.