Single-cell RNA-seq reveals multimodal regulatory networks and clinical predictive models in specific medulloblastoma cells

Background Medulloblastoma is a common primary tumor of the central nervous system. The impact of cellular heterogeneity on its treatment remains elusive. Methods Single-cell variational inference (scVI) model was used for batch effects correction. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were performed for evaluation of pathway activity. Cellchat algorithm was performed for inference of cell-cell interaction. SCENIC algorithm was performed for inferring gene regulatory networks (GRNs). Logistic regression and least absolute shrinkage and selection operator (LASSO) were conducted for identifying gene signature-associated with poor prognosis. Results This study integrates single-cell RNA sequencing data from 7 medulloblastoma samples, which exhibited satisfactory batch effect correction (Silhouette batch: 0.91; cLISI: 0.97) and biological conservation (bioconservation score: 0.62) performance. Unsupervised leiden clustering identified 24 cellular clusters, including differentiated malignant cells, stem-like, proliferative, stress-responsive, immune cells, and cancer-associated fibroblasts. WNT (C6: CTNNB1 , TSPYL1 ) and SHH (C14/C23: ATOH1 , SOX2 ) malignant cells exhibited pathway-specific enrichments. GSVA and GSEA implicated the activation of WNT and Hedgehog signaling pathways and overexpression of MYCN , ABCB1 , and GLI in C14 SHH malignant cells. CellChat analysis revealed C14 SHH cells engage in ligand-receptor crosstalk (MIF, MDK, NCAM1) with immune/malignant cells, while SCENIC uncovered a regulatory network driven by SOX9, JUN/JUND, and SOX2, modulating inflammation, hypoxia, and WNT pathways. A LASSO-Cox integrated analysis identified a 13-gene signature (C14 signature) predicted poor prognosis (log-rank p < 0.001). Functional enrichment analysis linked the signature to neurodevelopmental dysregulation and synaptic signaling. Conclusion These findings demonstrate novel gene signature and cell subtype as potential driver of unfavorable prognosis, providing mechanistic insights and actionable biomarkers for medulloblastoma stratification.