BACKGROUND

Neuroblastoma (NB) is a heterogeneous pediatric malignancy with highly variable outcomes. Traditional clinical factors, such as stage, MYCN status, and patient age, often fail to fully capture disease complexity. Recent advances in single-cell sequencing and integrative transcriptomic analyses provide an opportunity to identify more precise prognostic biomarkers and guide individualized therapies. This study aimed to develop and validate a robust prognostic model for NB by integrating single-cell and bulk transcriptomic data.

METHODS

We integrated seven publicly available single-cell RNA sequencing (scRNA-seq) datasets to form the Neuroblastoma Atlas and stratified tumor cells into high-risk and intermediate/low-risk groups. Differentially expressed genes (DEGs) were identified using defined fold-change and expression thresholds. Candidate genes were further validated using bulk RNA-sequencing data (HRA002064) and intersected with essential genes defined by DepMap Computational estimation of gene dependency using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening (CERES) scores. From the resulting set, we constructed a multivariate Cox regression-based prognostic model using the GSE49710 dataset. The model's performance was evaluated via Kaplan-Meier curves, time-dependent receiver operating characteristic analysis, and decision curve analysis. External validation was performed in the E-MTAB-8248 dataset, and comparisons with standard clinical indicators (International Neuroblastoma Staging System, MYCN status, age) were conducted.

RESULTS

Integrating scRNA-seq and bulk RNA-seq data identified 123 overlapping DEGs, of which seven genes (, , , , , , ) were further prioritized based on CERES dependency scores. Multivariate Cox regression and collinearity screening yielded a four-gene prognostic model (RiskScore) that significantly discriminated high- and low-risk patients in the GSE49710 cohort. Compared to traditional indicators, the four-gene model demonstrated superior time-dependent area under the curve (AUC) values and clinical decision-making benefits. External validation in E-MTAB-8248 confirmed the model's robust predictive performance, with high AUC values at 1-, 3-, and 5-year time points and consistent superiority over clinical parameters.

CONCLUSIONS

This study presents a novel four-gene prognostic signature derived from integrative scRNA-seq and bulk RNA-seq analyses. The model outperforms established clinical factors in predicting NB outcomes and provides added clinical decision-making value. Further prospective validation and mechanistic investigations may facilitate the translation of this prognostic signature into routine clinical practice, enabling more refined risk stratification and personalized treatment strategies for children with NB.