Immune Characteristics of eQTL and Gene Risk Model and the Inhibitory Effect of DCTD and RRAS on Ferroptosis in Glioblastoma.

Background: Glioblastoma (GBM) is an extremely aggressive brain tumor, marked by restricted therapeutic possibilities and a generally unfavorable prognosis. GBM's complexity and heterogeneity necessitate comprehensive genetic and immunological profiling to enhance therapeutic strategies.

Methods: The study integrated The Cancer Genome Atlas (TCGA) and Integrative Epidemiology Unit Open Genome-Wide Association Studies (IEU OpenGWAS) data to identify genetic factors influencing GBM using expression quantitative trait loci (eQTL) and genome-wide association studies (GWAS). Mendelian randomization (MR) analysis revealed 250 GBM-associated genes. A GBM risk prediction model was built using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. The research examined immune infiltration, drug response, and mutation profiles to characterize GBM molecular features. Functional enrichment and experiments validated key findings.

Results: The analysis uncovered significant genetic associations with GBM, emphasizing key genes such as follistatin-like 1 (), FXYD domain-containing ion transport regulator 5 (), Ras-related protein (), and ring finger protein 216 pseudogene 1 (). The risk model effectively categorized patients into low-risk and high-risk groups, showing significantly worse survival outcomes in the high-risk group. Immune profiling revealed differential infiltration of cancer-associated fibroblasts (CAFs), macrophages, and T cells, which correlated with the expression levels of the genes that were identified. Patients at high risk showed increased sensitivity to chemotherapeutic drugs such as dasatinib and lapatinib, while those at low risk were more responsive to elesclomol and lisitinib. Notably, key genes such as DCMP Deaminase () and were found to regulate ferroptosis, underscoring their potential as therapeutic targets for GBM treatment.

Conclusion: This study deepens the understanding of GBM by pinpointing critical genetic markers and elucidating their influence on the tumor immune microenvironment (TME) as well as treatment response. The risk model developed in this study holds promise for enhancing prognostic accuracy and facilitating the personalization of GBM therapy.