Exploration of the molecular characteristics of the tumor-immune interaction and the development of an individualized immune prognostic signature for neuroblastoma.

Abstract

Neuroblastoma (NBL) exists in a complex tumor-immune microenvironment. Immune cell infiltration and tumor-immune molecules play a critical role in tumor development and significantly impact the prognosis of patients. However, the molecular characteristics describing the NBL-immune interaction and their prognostic potential have yet to be investigated systematically. We first employed multiple machine learning algorithms, such as Gene Sets Enrichment Analysis and cell type identification by estimating relative subsets of RNA transcripts, to identify immunophenotypes and immunological characteristics in NBL patient data from public databases and then investigated the prognostic potential and regulatory networks of identified immune-related genes involved in the NBL-immune interaction. The immunity signature combining nine immunity genes was confirmed as more effective for individual risk stratification and survival outcome prediction in NBL patients than common clinical characteristics (area under the curve [AUC] = 0.819, C-index = 0.718, p < .001). A mechanistic exploration revealed the regulatory network of molecules involved in the NBL-immune interaction. These immune molecules were also discovered to possess a significant correlation with plasma cell infiltration, MYCN status, and the level of chemokines and macrophage-related molecules (p < .001). A nomogram was constructed based on the immune signature and clinical characteristics, which showed high potential for prognosis prediction (AUC = 0.856, C-index = 0.755, p < .001). We systematically elucidated the complex regulatory mechanisms and characteristics of the molecules involved in the NBL-immune interaction and their prognostic potential, which may have important implications for further understanding the molecular mechanism of the NBL-immune interaction and identifying high-risk NBL patients to guide clinical treatment.