Background
Glioblastoma multiforme (GBM) is a common and highly aggressive brain tumor with a poor prognosis. However, the prognostic value of ferroptosis-related genes (FRGs) and their classification remains insufficiently studied.
Conclusion
The ferroptosis-based risk model provides valuable prognostic insights into GBM and highlights potential therapeutic targets, emphasizing the biological significance of ferroptosis-related genes in tumor progression.
Methods
Ferroptosis-related genes (FRGs) were retrieved from databases such as FerrDB. The TCGA-GBM and CGGA-GBM datasets were used as training and testing cohorts, respectively. Univariate Cox regression and LASSO regression analyses were performed to establish a risk model comprising five genes (OSMR, G0S2, IGFBP6, IGHG2, FMOD). A Meta-analysis of integrated TCGA and GTEx data was conducted to examine the differential expression of these genes between GBM and normal tissues. Key gene protein expression differences were analyzed using CPTAC and HPA databases. Single-cell RNA sequencing (scRNA-seq) analysis was employed to explore the cell type-specific distribution of these genes.
Objective
This study aims to explore the significance of ferroptosis classification and its risk model in GBM using multi-omics approaches and to evaluate its potential in prognostic assessment.
Results
The five-gene risk model demonstrated significant prognostic value in GBM. Meta-analysis revealed distinct expression patterns of the identified genes between GBM and normal tissues. Protein expression analysis confirmed these differences. scRNA-seq analysis highlighted the diverse distribution of these genes across different cell types, offering insights into their biological roles.