ArticleDiscover oncology2025
Prognostic model for pediatric brain tumors based on tumor microenvironment-specific gene signatures.
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.
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Abstract
backgroundPediatric brain tumors (PBT) represent a major public health challenge, accounting for approximately 15-20% of all childhood malignancies. The tumor microenvironment (TME) plays a crucial role in tumor progression and prognosis, yet its specific contributions in PBT remain insufficiently explored.
objectiveThis study aimed to develop and validate a prognostic model based on TME-specific genes in pediatric brain tumors, providing insights into potential therapeutic targets.
methodsWe analyzed gene expression data from a cohort of 70 pediatric brain tumor samples using the ESTIMATE algorithm to classify tumors into high- and low-risk groups based on stromal and immune scores. To identify TME-specific genes, we performed differential gene expression and weighted gene co-expression network analysis (WGCNA). A prognostic model was constructed using LASSO regression and Cox proportional hazards models, comprising eight key genes: CASP10, EPSTI1, FGL2, ITGAX, etc. The model's predictive accuracy was validated in independent cohorts through ROC curve analysis.
resultsThe model demonstrated strong prognostic capability, with AUC values exceeding 0.8 for 1-year, 3-year, and 5-year survival predictions. Among the identified genes, CASP10 emerged as significantly associated with tumor progression, suggesting its potential as a therapeutic target. Furthermore, Mendelian randomization analysis provided additional support for a causal relationship between CASP10 expression and brain tumor risk.
conclusionThe TME-specific gene-based prognostic model effectively predicts survival outcomes in pediatric brain tumors, offering promising biomarkers for personalized medicine and potential therapeutic targets. However, further research is required to validate these findings across different tumor subtypes and to explore their clinical applications.
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