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

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Background: Neuroblastoma (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. Methods: GSE49710 (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). The relationship between the NB index and the immune microenvironment and drug therapy was then evaluated, and a predictive nomogram was developed. External validation was performed using the TARGET cohort (n = 155) and an additional cross-platform cohort GSE62564 (n = 498). Finally, functional experiments were conducted to validate key genes. Results: Based on the PANoptosis gene set, GSE49710 was divided into two distinct clusters with different prognoses. In PANclusters, there were significant differences in cell cycle, DNA replication, mismatch repair, and regulation of autophagy. The yellow gene module in WGCNA was most associated with NB risk. 116 risk genes were obtained by intersecting the module genes with DEGs among patients in the PANcluster subgroup. Through Cox regression analysis and LASSO algorithm, an eight-gene signature was used to build the NB index. Patients with high NB index exhibited significantly worse survival, which was consistently validated in external cohorts. Notably, GSVA analysis demonstrated that PANoptosis activity was significantly decreased in the high-NB-index group, suggesting that suppression of PANoptosis may contribute to NB progression. The risk score was associated with immune-checkpoint expression and with predicted responses to immunotherapy and chemotherapy. Functional experiments showed that TOP2A knockdown inhibited proliferation, migration, and invasion of NB cells. Conclusions: We established an eight-gene PANoptosis-related prognostic index for neuroblastoma and highlighted four hub genes (KIF18A, EXO1, TOP2A, and TACC3) closely associated with NB risk and prognosis.

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