# An MHC-Related Gene’s Signature Predicts Prognosis and Immune Microenvironment Infiltration in Glioblastoma

## TL;DR

This study identifies a four-gene signature that predicts survival and immune response in glioblastoma patients, offering potential for personalized treatment.

## Contribution

A novel MHC-related four-gene signature is developed for glioblastoma prognosis and immune infiltration analysis.

## Key findings

- The four-gene signature stratifies patients into high- and low-risk groups with distinct survival outcomes.
- The risk model correlates with immune pathways and tumor microenvironment infiltration patterns.
- I-BET-762 and Enzastaurin are proposed as potential therapeutic candidates for glioma.

## Abstract

Glioma is the most common primary malignant intracranial tumor with limited treatment options and a dismal prognosis. This study aimed to develop a robust gene expression-based prognostic signature for GBM using the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Using WGCNA and LASSO algorithms, we identified four MHC-related genes (TNFSF14, MXRA5, FCGR2B, and TNFRSF9) as prognostic biomarkers for glioma. A risk model based on these genes effectively stratified patients into high- and low-risk groups with distinct survival outcomes across TCGA and CGGA cohorts. This signature correlated with immune pathways and glioma progression mechanisms, showing strong associations with immune function and tumor microenvironment infiltration patterns. The risk score reflected tumor microenvironment remodeling, suggesting its prognostic relevance. We further propose I-BET-762 and Enzastaurin as potential therapeutic candidates for glioma. In conclusion, the four-gene signature we identified and the corresponding risk score model constructed from it provide valuable tools for the prognosis prediction of glioblastoma multiforme (GBM) and may guide personalized treatment strategies. The least absolute shrinkage and selection operator (LASSO) risk score has demonstrated significant prognostic evaluation utility in clinical GBM patients, bringing potential implications for patient stratification and the optimization of treatment regimens.