Construction and validation of a 13-gene signature for prognosis prediction in medulloblastoma

Background: Recent studies have identified several molecular subgroups of medulloblastoma associated with distinct clinical outcomes, however, no robust gene signature has been established for prognosis prediction. Our objective was to construct a robust gene signature-based model to predict the prognosis of patients with medulloblastoma. Methods: Expression data of medulloblastoma patients were acquired from the Gene Expression Omnibus (GSE85217, n=763; GSE37418, n=76). Cases with unknown clinical information or age<3 were excluded. GSE85217 were then randomly assigned as training (70%) or validation set 1 (30%), proportionately stratified by molecular subtypes (SHH, WNT, group 3, and group 4). To identify genes associated with overall survival (OS), we performed univariate survival analysis and LASSO Cox regression using the training set. Risk score model was constructed based on selected genes and was validated using the validation set 1. Additionally, GSE37418 dataset was used for external validation (validation set 2) of the model. Using the validated model, we identified differentially expressed genes (DEGs) between the risk groups, and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis to reveal enriched pathways. Results: A 13-gene model was constructed and validated. Patients classified as high-risk group had significantly worse OS than those as low-risk group (Training set: p<0.0001; Validation set 1: p<0.0001; Validation set 2: p=0.00052). AUCs for 1, 3, 5-year OS perdition were high (training set: 0.782, 0.833, 0.845; validation set 1 0.904, 0.790, 0.720; validation set 2: 0.848, 0.804, 0.722). Multivariate analysis integrating clinical factors demonstrated that the risk score was an independent predictor for the OS (validation set 1: p=0.001, validation set 2: p=0.004). Most of the 13 signature genes were related to neurological functions and diseases. We identified 265 DEGs between risk groups. GO and KEGG analysis demonstrated that these DEGs were associated with pathways related to neurological functions and diseases including synapse components, axon-related pathways, and Wnt-signaling. Conclusion: Our study constructed and validated a robust 13-gene signature model estimating the prognosis of medulloblastoma patients. We also revealed genes and pathways that may be related to the development of medulloblastoma, which might provide candidate targets for future investigation.