Identification of an Autophagy-Related Risk Signature Correlates With Immunophenotype and Predicts Immune Checkpoint Blockade Efficacy of Neuroblastoma - Kang, Wenjuan; Hu, Jiajian; Zhao, Qiang; Song, Fengju (Frontiers in Cell and Developmental Biology, 2021)
Neuroblastoma (NB) is one of the malignant solid tumors with the highest mortality in childhood. Targeted immunotherapy still cannot achieve satisfactory results due to the heterogeneity and tolerance. Exploring markers related to prognosis and evaluating immune microenvironment remains the major obstacle. Herein, we constructed an autophagy-related gene (ATG) risk model by multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression model, and identified out four prognostic ATGs (BIRC5, GRID2, HK2, RNASEL) in the training cohort then verified the signature in the internal and external validation cohorts. BIRC5 and HK2 showed higher expression in MYCN amplified cell lines and tumor tissues consistently, while GRID2 and RNASEL showed the opposite trend. The correlation between the signature and clinicopathological parameters was further analyzed and showing consistency. A prognostic nomogram using risk score, INSS stage, age and MYCN status was built subsequently; and the area under curves (AUCs), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed more satisfactory prognostic predicting performance. The ATG prognostic signature itself can significantly divided patients with NB into high- and low-risk groups, differentially expressed genes between the two groups were enriched in autophagy-related behaviors and immune cell reactions in Gene Set Enrichment Analysis (GSEA) analysis (FDR q-value<0.05). Furthermore, we evaluated the relationship of the signature risk score with immune cell infiltration and cancer-immunity cycle. The low-risk group was characterized by more abundant expression of chemokines and higher immune checkpoints (PDL1, PD1, CTLA-4, IDO1). The risk score was significantly correlated with the proportions of CD8+ T cells, CD4+ memory resting T cells, follicular helper T cell, memory B cells, plasma cells and M2 macrophages in tumor tissues. In conclusion, we developed and validated an autophagy-related signature that can accurately predict the prognosis, which might be meaningful to understand immune microenvironment and guide immune checkpoint blockade.