Abstract
BACKGROUND: Gliomas pose a significant challenge in the realm of neuro-oncology, marked by high rates of morbidity and mortality, as well as substantial healthcare costs. Current treatment strategies include surgical procedures, radiation therapy, and chemotherapy. However, these approaches often face obstacles due to tumor heterogeneity and the high risk of recurrence. Unraveling the molecular pathways that drive glioma progression is crucial for developing improved therapeutic options. This study investigates the prognostic relevance of excitotoxicity-related genes (ERGs) in the context of glioma. METHOD: A comprehensive bioinformatic analysis was performed using publicly available glioma datasets. We developed and validated a prognostic risk model based on five ERGs, and conducted survival analysis to evaluate the prognostic significance of the ERGs. This investigation involved the identification of differentially expressed genes (DEGs), additionally, pathway enrichment analysis, CIBERSORT immune cell infiltration analysis, and drug response analysis were carried out to offer deeper insights into the fundamental biological mechanisms and therapeutic implications. RESULTS: The developed prognostic risk model demonstrated impressive predictive performance, with area under the curve (AUC) values of 0.858, 0.915, and 0.855 at the 1, 3, and 5-year, respectively. Additionally, pathway enrichment analysis suggested involvement of neuroactive ligand-receptor interactions and immune response mechanisms. CIBERSORT analysis further revealed distinct immune cell infiltration patterns associated with different risk categories. Moreover, the analysis of drug responses highlighted potential therapeutic targets aligned with the expression of key hub genes. CONCLUSION: Our bioinformatics analysis reveals a significant association between an ERGs signature and glioma prognosis, providing a novel prognostic risk model with strong predictive capabilities. The identified pathways and immune cell infiltration patterns highlight potential therapeutic targets and suggest a role for the tumor microenvironment in glioma progression. These findings warrant further in vitro and in vivo validation to translate bioinformatics discoveries into personalized therapies that improve clinical outcomes.