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

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...

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Bibliographic Details
Published in:Cancers 2023-07, Vol.15 (15), p.3899
Main Authors: Dang, Huy-Hoang, Ta, Hoang Dang Khoa, Nguyen, Truc Tran Thanh, Wang, Chih-Yang, Lee, Kuen-Haur, Le, Nguyen Quoc Khanh
Format: Article
Language:English
Subjects:
Brain cancer
Central nervous system
Chemotherapy
Datasets
DNA methylation
DNA repair
Gene expression
Genes
Genetic markers
Genomes
Genomics
Glioblastoma
Glioblastoma multiforme
Immunotherapy
Infiltration
Medical prognosis
Medical research
Metastases
Nervous system
Patient outcomes
Prognosis
Risk assessment
Tissue inhibitor of metalloproteinase 1
Tumor microenvironment
Tumors
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Summary: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.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers15153899