Combining multiple cell death pathway-related risk scores to develop neuroblastoma cell death signature.

PURPOSE: Cell death plays an important role in tumourigenesis and progression; nevertheless, the clinical significance of cell death-related genes in neuroblastoma remains incompletely understood. METHODS: We separately constructed the corresponding risk scores for each of the eight cell death pathways separately and assessed their predictive performance. Through Cox regression analysis, these eight risk scores were integrated to obtain final cell death risk scores (CDRS). We evaluated the predictive performance of CDRS in multiple datasets and compared its accuracy with the clinical characteristics of patients and some existing prognostic models for neuroblastoma. We then explored the differences in immune infiltration between the high and low CDRS groups, and the significance of CDRS on EFS and disease progression. RESULTS: All eight risk scores have high predictive accuracy, with the Immunogenic-RS being the most accurate and the cuproptosis-RS the least accurate. Model genes are mainly enriched in a variety of cancer-related pathways and are closely related to the clinical characteristics. CDRS showed superior and robust predictive performance in multiple datasets and was more accurate than the clinical characteristics of patients and some existing prognostic models for neuroblastoma. High CDRS group featured distinct immune cold tumor profiles and may have poorer immune checkpoint inhibitor efficacy. CDRS had significance in predicting EFS and disease progression. CONCLUSION: We integrated risk scores associated with multiple cell death pathways to develop a high-performing and robust neuroblastoma signature. CDRS was a promising tool that may help with risk assessment and prediction of overall prognosis, and thus improve clinical outcomes.

Identification of Key Genes Associated with Risk and Prognosis of Neuroblastoma.

Neuroblastoma is a childhood malignancy with high morbidity and mortality. We identified key biomarkers associated with neuroblastoma risk and prognosis. The gene modules most associated with neuroblastoma risk were derived by WGCNA. Modular genes were intersected with differentially expressed genes between patients with high-risk (HR) and non-high-risk (NHR) to obtain risk genes, and enrichment analysis was performed. After incorporating risk genes into Cox regression analysis, LASSO algorithm, and K-M survival analysis, key genes were identified and introduced into four external datasets for validation. We performed short time-series expression miner analysis and single-sample genome enrichment analysis. Finally, we evaluated the difference in DNA methylation levels to identify meaningful methylation marks. We identified 5 key genes (ANO6, CPNE2, DST, PLXNC1, SCN3A) for neuroblastoma risk and prognosis, which correlated closely with known neuroblastoma biomarkers. All key genes showed a progressive downregulation trend with increasing risk levels of neuroblastoma. The immune infiltration of 14 immune cells was significantly different between HR-NB and NHR-NB, and most immune cells were negatively correlated with key genes. Furthermore, the expression of ANO6, CPNE2, DST, and PLXNC1 was modified by DNA methylation. This study identified 5 key genes for neuroblastoma risk and prognosis that were potential biomarkers.