Systematic Construction and Validation of a Metabolic Risk Model for Prognostic Prediction in Acute Myelogenous Leukemia.

ABSTRACT

Background: Acute myelogenous leukemia (AML) is a heterogeneous disease with recurrent gene mutations and variations in disease-associated gene expression, which may be useful for prognostic prediction. Methods: RNA matrix and clinical data of AML were downloaded from GEO, TCGA, and TARGET databases. Prognostic metabolic genes were identified by LASSO analysis to establish a metabolic model. Prognostic accuracy of the model was quantified by time-dependent receiver operating characteristic curves and the area under the curve (AUC). Survival analysis was performed by log-rank tests. Enriched pathways in different metabolic risk statuses were evaluated by gene set enrichment analyses (GSEA). Results: We identified nine genes to construct a prognostic model of shorter survival in the high-risk vs. low-risk group. The prognostic model showed good predictive efficacy, with AUCs for 5-year overall survival of 0.78 (0.73-0.83), 0.76 (0.62-0.89), and 0.66 (0.57-0.75) in the training, adult external, and pediatric external cohorts, respectively. Multivariable analysis demonstrated that the metabolic signature had independent prognostic value with hazard ratios of 2.75 (2.06-3.66), 1.89 (1.09-3.29), and 1.96 (1.00-3.84) in the training, adult external, and pediatric external cohorts, respectively. Combining metabolic signatures and classic prognostic factors improved 5-year overall survival prediction compared to the prediction by classic prognostic factors (p < 0.05). GSEA revealed that most pathways were metabolism-related, indicating potential mechanisms. Conclusion: We identified dysregulated metabolic features in AML and constructed a prognostic model to predict the survival of patients with AML.