Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis

Overview

Journal Cancers (Basel)

Publisher MDPI

Specialty Oncology

Date 2023 Aug 12

PMID 37568715

Authors

Huy-Hoang Dang

Hoang Dang Khoa Ta

Truc Tran Thanh Nguyen

Chih-Yang Wang

Kuen-Haur Lee

Nguyen Quoc Khanh Le

Affiliations

Soon will be listed here.

Abstract

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes' biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely , , , , , , , and . The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

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