A Hypoxia-Related Long Non-Coding RNAs Signature Associated With Prognosis in Lower-Grade Glioma

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Hypoxia microenvironment and long non-coding RNAs (lncRNAs) are crucial in tumor development.
• Predicting survival outcomes for lower-grade glioma (LGG) patients remains a challenge.

Purpose of the Study:

• To develop and validate a hypoxia-related lncRNA (HRL) model for predicting LGG patient survival.
• To explore the association of the HRL model with clinicopathologic features and the tumor immune microenvironment.

Main Methods:

• Consensus clustering of hypoxia-related mRNAs in 505 LGG samples from TCGA.
• LASSO regression to select eight informative lncRNAs for the prognostic HRL model.
• Validation in independent cohorts and correlation analysis with clinical data and immune markers.

Main Results:

• The HRL model demonstrated strong predictive performance for LGG patient overall survival in TCGA and validation cohorts.
• The model correlated significantly with age, tumor grade, IDH mutation, 1p/19q codeletion, MGMT methylation, and tumor progression risk.
• The HRL signature was linked to inflammatory response, hypoxia, Kras signaling, immune cell infiltration, tumor mutation burden, and immune checkpoints.

Conclusions:

• An effective HRL model was established for prognosis assessment in LGG patients.
• The model provides insights into tumor biology and may aid in designing individualized treatment strategies.
• The HRL model's association with immune features suggests potential therapeutic implications.