Fatty acid metabolism-related risk signature revealing the immune landscape of neuroblastoma and predicting overall survival in pediatric neuroblastoma patients.
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BACKGROUND: Tumor metabolic reprogramming is a hallmark in cancer cells, wherein fatty acid metabolism assumes a pivotal role in energy supply and the provision of diverse biosynthetic precursors. However, there is a lack of systematic analysis regarding the impact of fatty acid metabolism on prognosis in neuroblastoma (NB) patients and its influence on the immune microenvironment. METHODS: We acquired RNA expression profiles and corresponding clinical-pathological information for NB patients from the Gene Expression Omnibus, ArrayExpress, and TARGET databases. The GSE49710 cohort was utilized as a training set, whereas E-MTAB-8248 and the TARGET cohorts served as testing sets. Consensus clustering was employed to identify molecular subtypes based on fatty acid metabolism. Independent prognostic genes were pinpointed using LASSO-Cox analysis, which facilitated the development of a novel risk signature that was subsequently validated using the testing sets. We then proceeded to analyze the predictive power of the risk signature for prognosis, its correlation with clinical-pathological features, the immune landscape, and drug sensitivity. RESULTS: In the consensus clustering analysis, patients in the training set were segregated into two clusters. Cluster 2 exhibiting significantly poorer overall survival (OS) compared to cluster 1. Moreover, cluster 2 was markedly associated with clinical-pathological features indicative of poor prognosis. Following this, univariate Cox regression analysis revealed 207 fatty acid metabolism genes (FMGs) correlated with patient OS. A risk signature based on 35 FMGs was constructed using LASSO-Cox regression analysis, demonstrating significant predictive accuracy and discrimination in both the training and testing sets. The risk signature emerged as an independent prognostic factor and was integrated with multiple clinical-pathological features to develop a nomogram. In the immune landscape analysis, the high-risk group displayed a compromised antigen presentation mechanism, reduced infiltration levels of various immune cells, and escaping of CD8 + T cells and NK cells. Additionally, different risk groups could exhibit different responsiveness to immune checkpoint inhibitors. Lastly, potential chemotherapeutic agents for each risk group were predicted. CONCLUSION: The novel risk signature, derived from FMGs, demonstrated promising efficacy in predicting the prognosis of NB patients, elucidating their immune landscape, and guiding therapeutic strategies.