Precision prognostication in neuroblastomas via clinically validated E2F activity signatures

Background: Neuroblastoma (NB) is the most common extracranial solid tumor in children, with high-risk NB (HR-NB) exhibiting dismal survival rates due to aggressive biology and therapy resistance. E2F transcription factors (E2Fs) are pivotal regulators of cell cycle progression and immune modulation, yet their prognostic and therapeutic implications in NB remain underexplored. Methods: Using transcriptomic data from the GEO, TARGET, and E-MTAB-8248 cohorts, we identified E2F-associated molecular subtypes via consensus clustering. A prognostic signature was constructed via LASSO regression and validated for risk stratification. Immune infiltration, tumor mutation burden (TMB), and drug sensitivity were analyzed via the CIBERSORT, ESTIMATE, and GDSC databases. Results: Four E2F-related genes (MAD2L1, CDC25A, CKS2, and NME1) were used to construct a prognostic nomogram that stratified patients into high- and low-risk groups, with low-risk patients exhibiting superior overall survival (P < 0.05). Multivariate Cox regression confirmed that the model was an independent prognostic factor (P < 0.001). High-risk patients presented lower immune and stromal scores, reduced immune checkpoint expression, distinct immune cell infiltration patterns, and significant differences in mutation spectrum and drug sensitivity (P < 0.001). Conclusions: The E2F-related prognostic signature effectively stratifies NB patients by risk and provides potential biomarkers for prognosis and targeted therapy in HR-NB patients. The identified signature enhances patient stratification and provides insights into NB tumor biology, the immune landscape, and potential treatment strategies.