Image_2_Systematic Construction and Validation of a Metabolic Risk Model for Prognostic Prediction in Acute Myelogenous Leukemia.TIF (570.98 kB)

Image_2_Systematic Construction and Validation of a Metabolic Risk Model for Prognostic Prediction in Acute Myelogenous Leukemia.TIF

Download (570.98 kB)
figure
posted on 21.04.2020 by Yun Wang, Fang Hu, Jin-yuan Li, Run-cong Nie, Si-liang Chen, Yan-yu Cai, Ling-ling Shu, De-jun Deng, Jing-bo Xu, Yang Liang

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.

History

Licence

Exports