Integrated network propagation identifies prognostic metabolic signatures in acute myeloid leukemia.

[BACKGROUND] Acute myeloid leukemia (AML), a biologically heterogeneous malignancy, requires improved prognostic models, particularly for patients with intermediate-risk profiles and lacking definitive genetic markers. Therefore, this study aims to identify biologically coherent and clinically informative gene signatures using a novel prognostic modeling approach integrating gene expression profiles with protein–protein interaction networks.

[METHODS] We applied network propagation using Personalized PageRank with seed genes from a literature-based six-gene signature (LBS6) and two recurrent AML mutations ( and ). Network-informed modules were derived and optimized using LASSO–Cox regression models trained on the TCGA–LAML cohort ( = 132, adult AML) and externally validated in the BeatAML 1.0 ( = 308, adult AML) and TARGET–AML ( = 1,889, pediatric AML) cohorts. Cox proportional hazards models were used to evaluate associations with overall survival. Functional enrichment analyses were conducted using KEGG and Gene Ontology databases.

[RESULTS] From LBS6 propagation, a ten-gene signature (LBS6-Derived Network Gene Signature [LBSnet]: ,,,,,,,,,) was derived, stratifying patients in the TCGA–LAML based on overall survival (HR = 3.84,  < 0.0001) and was validated in the BeatAML 1.0 (HR = 1.94,  < 0.0001) and TARGET–AML (HR = 1.57,  < 0.0001) cohorts. Joint network propagation using and seed genes produced a five-gene signature metabolic and chromatin-modifying functions (,,,,), demonstrating prognostic significance in the TCGA–LAML cohort (HR = 2.91,  < 0.0001), BeatAML 1.0 (HR = 1.33,  = 0.07), and TARGET–AML (HR = 1.34,  < 0.001). These network-derived risk scores remained independent predictors of overall survival in multivariate Cox models adjusted for age and key genetic covariates, including ,, and mutations. Functional enrichment analyses revealed significant involvement in fatty-acid oxidation, mitochondrial respiration, and platelet activation pathways.

[CONCLUSION] This study presents a novel network-based framework for prognostic modeling in AML, generating biologically interpretable gene signatures with validated predictive power across adult and pediatric cohorts. Integrating transcriptomic data with molecular interaction networks provides a scalable strategy for biomarker discovery, enhancing risk stratification and offering insight into potential metabolic vulnerabilities in AML.

[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12967-025-07524-w.