ArticleDiscover oncology2025
The role of genes related to excitotoxicity in glioma prognosis.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundGliomas 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.
methodA 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.
resultsThe 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.
conclusionOur 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.