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pmid:41602819
Exploring the prognostic molecular mechanisms of medulloblastoma through methylation-transcriptome integration.
Kaisai Tuerxun · Wenyu Ji · Turtuohut Tukebai · Dong Liu · Junhong Zhao · Yongxin Wang
SAGE open medicine · 2026 doi:10.1177/20503121251386139
Paper
complete
Experiment
complete
Data
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Analysis
partial
Execution
absent
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Complete file
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The disease exhibits biological heterogeneity, with significant variability in cells of origin, genetic mutation profiles, and prognoses across different subgroups, posing challenges for effective treatment. Aberrant DNA methylation has been identified as a promoter of tumorigenesis, influencing the tumor microenvironment and patient prognosis.\nObjective: This study aimed to investigate epigenetic changes in different medulloblastoma subtypes by integrating genomics, transcriptomics, epigenetics, and clinicopathological data to identify potential therapeutic targets and drugs that could significantly improve patient outcomes.\nMethods: We obtained medulloblastoma transcriptomic and methylation data (GSE85217 and GSE85212) from the NCBI GEO database and performed differential gene expression and methylation analysis using the limma and ChAMP packages. Functional pathway enrichment was assessed via GO and KEGG analysis. A prognostic model was constructed using LASSO regression, while WGCNA and GSEA were employed to analyze key gene modules and signaling pathways. In addition, CIBERSORT and Gene set variation analysis (GSVA) were used to evaluate the immune microenvironment and drug sensitivity.\nResults: We identified 1135 differentially expressed genes and 2582 differentially methylated sites, with inhibin beta B and ubiquitin-specific peptidase 2 significantly upregulated in Group 3/Group 4 subtypes. The prognostic model comprised 25 genes, and risk stratification effectively distinguished high- and low-risk patients (AUC 0.76-0.78). Immune analysis revealed decreased plasma cells and monocytes in the high-risk group, alongside increased naïve B cells and M0 macrophages. Drug prediction suggested that atovaquone and embelin may reverse tumor progression.\nConclusion: Inhibin beta B and ubiquitin-specific peptidase 2 are key marker genes for predicting medulloblastoma patient prognosis stratification, with their expression patterns closely linked to molecular subtypes and the immune microenvironment. This study provides novel molecular targets and strategies for precision therapy and prognostic assessment in medulloblastoma.", "claims": [{"id": "claim-method", "statement": "A prognostic model was constructed using LASSO regression, while WGCNA and GSEA were employed to analyze key gene modules and signaling pathways.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41602819", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A prognostic model was constructed using LASSO regression, while WGCNA and GSEA were employed to analyze key gene modules and signaling pathways."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We sourced medulloblastoma-related data from the NCBI GEO public database (GSE85217), 3 encompassing expression profile data of 763 samples, including 293 samples in the control group (WNT + SHH subtypes) and 470 samples in the experimental group (Group 3 + Group 4).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41602819", "locator": "sections[3] \"Data acquisition and differential analysis\" paragraph 2", "quote": "We sourced medulloblastoma-related data from the NCBI GEO public database (GSE85217), 3 encompassing expression profile data of 763 samples, including 293 samples in the control group (WNT + SHH subtypes) and 470 samples in the experimental group (Group 3 + Group 4)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic model comprised 25 genes, and risk stratification effectively distinguished high- and low-risk patients (AUC 0.76–0.78).", "kind": "quantitative", "target": {"value": 0.76, "unit": "auc", "tolerance": null, "source": "The prognostic model comprised 25 genes, and risk stratification effectively distinguished high- and low-risk patients (AUC 0.76–0.78)."}, "evidence": [{"asset": "source:pmid:41602819", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The prognostic model comprised 25 genes, and risk stratification effectively distinguished high- and low-risk patients (AUC 0.76–0.78)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic model comprised 25 genes, and risk stratification effectively distinguished high- and low-risk patients (AUC 0.76–0.78).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41602819"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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