| Popis: |
Neuroblastoma (NBL) is clinically and biologically heterogeneous, and novel therapies are desperately needed as the poor prognosis of high-risk NBL cases. Increasingly, studies about metabolic reprogramming and tumor microenvironment (TME) open the way to change cancer risk stratification and treatment. Through machine learning, this study identified two metabolic clusters in NBL, which have distinct clinical features, Hallmark pathways and TME. By Weighted gene co-expression network analysis (WGCNA) and Cytoscape, we discovered that RNA and glycosphingolipid (GSL) metabolism play a crucial role in metabolic subtyping. Subsequently, we constructed and verified a risk signature based on key module genes, which performs a good prediction of NBL prognosis. Two risk groups, divided by the median value, are closely associated with clinical features, and risk scores show an inverse correlation with immune infiltration. In combination with gene set enrichment analysis (GSEA) results, we conclude that ribosome biogenesis regulated by rRNA metabolism might be a target for MYCN-amplification NBLs, and GSL metabolism might contribute to TME formation in NBL. Finally, we tried to predict different risk groups' immunotherapy and chemotherapy sensitivity and screened potential targets that might be useful against NBL. In summary, we used multiple bioinformatics analyses to explore the interaction between metabolic processes and TME in NBL and provide new ideas for developing new therapies. |
