Development of a prognostic model for overall survival in neuroblastoma based on Schwann cell-specific genes, clinical predictors, and MYCN amplification.

Background: Neuroblastoma (NBL) is a common pediatric malignancy with diverse prognoses influenced by multiple factors. Accurate overall survival (OS) predictions are essential for guiding treatment. However, the contribution of specific cell types within the tumor microenvironment (TME), which significantly influence disease progression, is often overlooked. This study aimed to develop an NBL prognostic model that incorporates TME, genetic, and clinical factors to improve prediction accuracy and clinical relevance.

Methods: Data were collected from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database (n=106, test set) and the Gene Expression Omnibus (GEO) database (n=238, train set). Including clinical details such as MYCN amplification, International NBL Staging System (INSS) stage, age at diagnosis, and OS outcomes. Additionally, single-cell RNA sequencing (scRNA-seq) data from 16 NBL patients (160,910 cells) were included to improve model precision. Uniform manifold approximation and projection (UMAP) was utilized for cell clustering, while weighted gene co-expression network analysis (WGCNA) helped identify cell-type-specific modules. Prognostic genes were pinpointed using univariate and multivariate Cox regression analyses, which also served to refine the model by integrating essential clinical variables and molecular markers. The model's effectiveness was assessed through Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and calibration plots. Additional evaluations included immune cell infiltration and drug sensitivity analysis.

Results: MYCN amplification was present in 79.4% of patients in the train set and 79.2% of patients in the test set, and the majority of patients in both cohorts were classified as Stage 4. The median age at diagnosis was 399.5 days in the train set and 1,069 days in the test set. Key findings demonstrate that Schwann cell-specific genes (CALR, KLF10, UBL3) considerably affect survival outcomes in NBL patients. The initial model showed robust predictive accuracy in the train set with areas under the curve (AUCs) of 0.832 and acceptable performance in the test set with AUC of 0.777. A refined model, incorporating three genes, two clinical indicators (age and INSS stage), and MYCN amplification, exhibited enhanced accuracy with AUC of 0.857. Differences in immune cell expression between high-risk and low-risk groups were noted, alongside significant disparities in drug sensitivity, indicating lower half maximal inhibitory concentration (IC50) values for targeted therapies in the high-risk group.

Conclusions: This study developed a model for predicting OS in NBL by integrating Schwann cell-specific genes, clinical factors, and the TME. The model highlights the importance of specific cellular contributions to prognosis and provides a more personalized approach to NBL treatment, particularly for high-risk patients.