Prognostic implications of store-operated calcium entry signatures and immune dynamics in neuroblastoma via machine learning.

BACKGROUND

Neuroblastoma is a highly heterogeneous pediatric malignancy, with high-risk cases exhibiting poor clinical outcomes. Store-operated calcium entry (SOCE) channels have been implicated in cancer progression, yet their prognostic significance in neuroblastoma remains unclear. This study aimed to investigate the relevance of SOCE-related genes in predicting patient prognosis and guiding therapeutic strategies.

METHODS

We performed unsupervised clustering based on SOCE-related gene expression in multiple neuroblastoma RNA sequencing (RNA-seq) datasets. A prognostic scoring system, the SOCE_Score, was developed using machine learning algorithms. The model's predictive performance was validated across independent datasets. Immune characteristics were assessed using established deconvolution algorithms, and candidate therapeutic compounds were identified via the Connectivity Map (CMap) platform.

RESULTS

Two distinct molecular clusters were identified, differing significantly in survival outcomes, immune infiltration, and stemness signatures. The SOCE_Score stratified patients with high accuracy and outperformed conventional clinical predictors. Lower SOCE_Score groups were associated with favorable immune landscapes and greater responsiveness to immune checkpoint blockade. CMap analysis highlighted MS-275, a histone deacetylase (HDAC) inhibitor, as a promising compound targeting low SOCE_Score phenotypes.

CONCLUSIONS

SOCE-related transcriptional features serve as robust biomarkers for prognosis and immune activity in neuroblastoma. The SOCE_Score holds potential for guiding risk stratification, immunotherapeutic selection, and drug repurposing efforts. These findings underscore the clinical utility of integrating calcium signaling profiles into neuroblastoma management and warrant further experimental validation.