Identification of a novel PANoptosis-associated prognostic signature and immune landscape analysis in neuroblastoma with experimental validation

BackgroundNeuroblastoma (NB) is the most common extracranial pediatric solid tumor, and the prognosis of high-risk patients remains poor, while early diagnosis is still challenging. Although PANoptosis plays an important role in tumor development and progression, its role in NB has not been systematically investigated.MethodsGSE49710 (n = 498) was used as the training cohort, providing transcriptomic profiles and clinical annotations.

Patients were stratified by Children’s Oncology Group (COG) risk classification. PANoptosis core genes were defined as the intersection between differentially expressed genes (DEGs) associated with COG risk and a curated PANoptosis gene set.

Based on those core genes, consensus clustering analysis stratified patients into PANoptosis-related clusters (PANclusters). Differential gene analysis, survival analysis, and gene set variation analysis (GSVA) in PANclusters were conducted subsequently.

Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression modules most strongly associated with PANclusters. Candidate genes were further subjected to Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model (NB index).