Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning
ABSTRACT Importance Medulloblastoma (MB) is the most common malignant brain tumor in children, with metastasis being the primary cause of recurrence and mortality. The tumor microenvironment (TME) plays a critical role in driving metastasis; however, the mechanisms underlying TME alterations in MB m...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Pediatric Investigation |
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| Online Access: | https://doi.org/10.1002/ped4.12471 |
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| author | Fengmao Zhao Xiangjun Liu Jingang Gui Hailang Sun Nan Zhang Yun Peng Ming Ge Wei Wang |
| author_facet | Fengmao Zhao Xiangjun Liu Jingang Gui Hailang Sun Nan Zhang Yun Peng Ming Ge Wei Wang |
| author_sort | Fengmao Zhao |
| collection | DOAJ |
| description | ABSTRACT Importance Medulloblastoma (MB) is the most common malignant brain tumor in children, with metastasis being the primary cause of recurrence and mortality. The tumor microenvironment (TME) plays a critical role in driving metastasis; however, the mechanisms underlying TME alterations in MB metastasis remain poorly understood. Objective To develop and validate machine learning (ML) models for predicting patient outcomes in MB and to investigate the role of TME components, particularly immune cells and immunoregulatory molecules, in metastasis. Methods ML models were constructed and validated to predict prognosis and metastasis in MB patients. Eight algorithms were evaluated, and the optimal model was selected. Lasso regression was employed for feature selection, and SHapley Additive exPlanations values were used to interpret the contribution of individual features to model predictions. Immune cell infiltration in tumor tissues was quantified using the microenvironment cell populations‐counter method, and immunohistochemistry was applied to analyze the expression and distribution of specific proteins in tumor tissues. Results The ML models identified metastasis as the strongest predictor of poor prognosis in MB patients, with significantly worse survival outcomes observed in metastatic cases. High infiltration of CD8+ T cells and cytotoxic T lymphocytes (CTLs), along with elevated expression of the TGFB1 gene encoding transforming growth factor beta 1 (TGF‐β1), were strongly associated with metastasis. Independent transcriptomic and immunohistochemical analyses confirmed significantly higher CD8+ T cell/CTL infiltration and TGF‐β1 expression in metastatic compared to nonmetastatic MB samples. Patients with both high CD8+ T cell/CTL infiltration and elevated TGFB1 expression in the context of metastasis exhibited significantly worse survival outcomes compared to patients with low expression and no metastasis. Interpretation This study identifies metastasis as the key prognostic factor in MB and reveals the pivotal roles of CD8+ T cells, CTLs, and TGF‐β1 within the TME in promoting metastasis and poor outcomes. These findings provide a foundation for developing future therapeutic strategies targeting the TME to improve MB patient outcomes. |
| format | Article |
| id | doaj-art-9dfd2e8b441b4807a7576955505ff18c |
| institution | DOAJ |
| issn | 2574-2272 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Pediatric Investigation |
| spelling | doaj-art-9dfd2e8b441b4807a7576955505ff18c2025-08-20T03:10:36ZengWileyPediatric Investigation2574-22722025-03-0191596910.1002/ped4.12471Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learningFengmao Zhao0Xiangjun Liu1Jingang Gui2Hailang Sun3Nan Zhang4Yun Peng5Ming Ge6Wei Wang7Department of Neurosurgery Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaLaboratory of Tumor Immunology Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaLaboratory of Tumor Immunology Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaDepartment of Neurosurgery Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaDepartment of Pathology Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaLaboratory of Tumor Immunology Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaDepartment of Neurosurgery Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaLaboratory of Tumor Immunology Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing ChinaABSTRACT Importance Medulloblastoma (MB) is the most common malignant brain tumor in children, with metastasis being the primary cause of recurrence and mortality. The tumor microenvironment (TME) plays a critical role in driving metastasis; however, the mechanisms underlying TME alterations in MB metastasis remain poorly understood. Objective To develop and validate machine learning (ML) models for predicting patient outcomes in MB and to investigate the role of TME components, particularly immune cells and immunoregulatory molecules, in metastasis. Methods ML models were constructed and validated to predict prognosis and metastasis in MB patients. Eight algorithms were evaluated, and the optimal model was selected. Lasso regression was employed for feature selection, and SHapley Additive exPlanations values were used to interpret the contribution of individual features to model predictions. Immune cell infiltration in tumor tissues was quantified using the microenvironment cell populations‐counter method, and immunohistochemistry was applied to analyze the expression and distribution of specific proteins in tumor tissues. Results The ML models identified metastasis as the strongest predictor of poor prognosis in MB patients, with significantly worse survival outcomes observed in metastatic cases. High infiltration of CD8+ T cells and cytotoxic T lymphocytes (CTLs), along with elevated expression of the TGFB1 gene encoding transforming growth factor beta 1 (TGF‐β1), were strongly associated with metastasis. Independent transcriptomic and immunohistochemical analyses confirmed significantly higher CD8+ T cell/CTL infiltration and TGF‐β1 expression in metastatic compared to nonmetastatic MB samples. Patients with both high CD8+ T cell/CTL infiltration and elevated TGFB1 expression in the context of metastasis exhibited significantly worse survival outcomes compared to patients with low expression and no metastasis. Interpretation This study identifies metastasis as the key prognostic factor in MB and reveals the pivotal roles of CD8+ T cells, CTLs, and TGF‐β1 within the TME in promoting metastasis and poor outcomes. These findings provide a foundation for developing future therapeutic strategies targeting the TME to improve MB patient outcomes.https://doi.org/10.1002/ped4.12471CD8+ T lymphocytesMachine learningMedulloblastomaMetastasisTransforming growth factor beta 1 |
| spellingShingle | Fengmao Zhao Xiangjun Liu Jingang Gui Hailang Sun Nan Zhang Yun Peng Ming Ge Wei Wang Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning Pediatric Investigation CD8+ T lymphocytes Machine learning Medulloblastoma Metastasis Transforming growth factor beta 1 |
| title | Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning |
| title_full | Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning |
| title_fullStr | Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning |
| title_full_unstemmed | Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning |
| title_short | Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning |
| title_sort | characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning |
| topic | CD8+ T lymphocytes Machine learning Medulloblastoma Metastasis Transforming growth factor beta 1 |
| url | https://doi.org/10.1002/ped4.12471 |
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