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Background <p>Pediatric 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.</p> Objective <p>This study aimed to develop and validate a prognostic model based on TME-specific genes in pediatric brain tumors, providing insights into potential therapeutic targets.</p> Methods <p>We 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.</p> Results <p>The 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.</p> Conclusion <p>The 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.</p>

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Prognostic model for pediatric brain tumors based on tumor microenvironment-specific gene signatures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Yuxin Liu,
  • Longgang Sang,
  • Alexandr N. Chernov,
  • Guodong Liu,
  • Tao Jiang,
  • Guanjian Zhao

摘要

Background

Pediatric 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.

Objective

This study aimed to develop and validate a prognostic model based on TME-specific genes in pediatric brain tumors, providing insights into potential therapeutic targets.

Methods

We 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.

Results

The 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.

Conclusion

The 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.