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

• Published in: Cancers Vol. 15; no. 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: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.07.2023; MDPI
• Subjects: Brain cancer; Central nervous system; Chemotherapy; Datasets; differentially expressed genes; DNA methylation; DNA repair; Gene expression; Genes; genetic biomarkers; Genetic markers; Genomes; Genomics; Glioblastoma; Glioblastoma multiforme; Immunotherapy; Infiltration; Medical prognosis; Medical research; Metastases; Nervous system; Patient outcomes; Prognosis; prognosis-related genes; Risk assessment; survival analysis; Tissue inhibitor of metalloproteinase 1; Tumor microenvironment; Tumors; univariate cox regression; Taiwan; univariate cox regression; differentially expressed genes; genetic biomarkers; glioblastoma; survival analysis; prognosis-related genes
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15153899

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.