Construction and Validation of a 13-Gene Signature for Prognosis Prediction in Medulloblastoma

Abstract

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 medulloblastomas were acquired from the Gene Expression Omnibus (GSE85217, n = 763; GSE37418, n = 76). To identify genes associated with overall survival (OS), we performed univariate survival analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. A risk score model was constructed based on selected genes and was validated using multiple datasets. Differentially expressed genes (DEGs) between the risk groups were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and protein-protein interaction (PPI) analyses were performed. Network modules and hub genes were identified using Cytoscape. Furthermore, tumor microenvironment (TME) was evaluated using ESTIMATE algorithm. Tumor-infiltrating immune cells (TIICs) were inferred using CIBERSORTx. 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). The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis indicated a good performance in predicting 1-, 3-, and 5-year OS in all datasets. 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). We then identified 265 DEGs between risk groups and PPI analysis predicted modules that were highly related to central nervous system and embryonic development. The risk score was significantly correlated with programmed deathligand 1 (PD-L1) expression (p < 0.001), as well as immune score (p = 0.035), stromal score (p = 0.010), and tumor purity (p = 0.010) in Group 4 medulloblastomas. Correlations between the 13-gene signature and the TIICs in Sonic hedgehog and Group 4 medulloblastomas were revealed.

Association of an eight-gene signature prognosis model with tumor immunity in medulloblastoma

“…This suggests that these TME scores are speci c to different cancers. Wang et al analyzed the TME scores of MB data in the GSE85217 dataset and found that the SHH subgroup had the highest tumor immune and stromal scores [15]. However, in our study, Group3 had the highest immune and stromal scores.…”

A transcriptome-based classifier to determine molecular subtypes in medulloblastoma

“…Specific to Group 4, we observed CACNA1A (Calcium Voltage-gated Channel Subunit Alpha1 A), in 21 GERs and was identified in systems biology analysis of Group 4 MBs as a novel therapeutic target [29]. Another recent study profiled the immune repertoire of MBs and found high expression of CACNA1A in Group 4 MB plasma cells [30]. KHDRBS2 was present in 19 GERs and over-expressed in Group 4 tumors and was previously reported as a subgroup-specific gene [31].…”

Association of an eight-gene signature prognosis model with tumor immunity in medulloblastoma

“…This implies score speci city across cancers. Wang et al analyzed TME scores of MB data in the GSE85217 dataset, nding the SHH subgroup had the highest tumor immune and stromal scores (Li et al, 2020). However, Group3 exhibited the highest immune and stromal scores in our study.…”