Integrated network propagation identifies prognostic metabolic signatures in acute myeloid leukemia.
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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 (IDH1 and IDH2). Network-informed modules were derived and optimized using LASSO–Cox regression models trained on the TCGA–LAML cohort (n = 132, adult AML) and externally validated in the BeatAML 1.0 (n = 308, adult AML) and TARGET–AML (n = 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]: PTP4A3, HS3ST3B1, ECHS1, PLA2G4A, ETFB, NDST3, CSK, ARL6IP5, PLD1, and NDUFS8) was derived, stratifying patients in the TCGA–LAML based on overall survival (HR = 3.84, p < 0.0001) and was validated in the BeatAML 1.0 (HR = 1.94, p < 0.0001) and TARGET–AML (HR = 1.57, p < 0.0001) cohorts. Joint network propagation using IDH1 and IDH2 seed genes produced a five-gene signature metabolic and chromatin-modifying functions (G6PD, ENO1, SDHA, H3-3 A, IL4I1), demonstrating prognostic significance in the TCGA–LAML cohort (HR = 2.91, p < 0.0001), BeatAML 1.0 (HR = 1.33, p = 0.07), and TARGET–AML (HR = 1.34, p < 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 FLT3-ITD, NPM1, and CEBPA 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.