Identification and Validation of Metabolic Hub Genes Using
WGCNA
and Prognostic Risk Modeling in Acute Myeloid Leukemia
Samaneh Ramezani, Fatemeh Akhoundi, Reza Valadan, Hossein Asgarian‐Omran ABSTRACT
Background
Acute myeloid leukemia (AML) is an aggressive and molecularly heterogeneous hematologic malignancy associated with poor clinical outcomes, particularly in elderly and high‐risk patients. Increasing evidence suggests that metabolic reprogramming plays a critical role in leukemia progression, immune dysregulation, and therapeutic resistance. However, the prognostic value of metabolism‐associated molecular networks in AML remains insufficiently understood.
Aim
This study aimed to identify metabolism‐related hub genes involved in AML progression and to establish a robust prognostic signature for survival prediction.
Methods and Results
Integrated transcriptomic analyses were performed using multiple Gene Expression Omnibus datasets and the TCGA‐LAML cohort. Weighted gene co‐expression network analysis identified AML‐associated gene modules, followed by protein–protein interaction and functional enrichment analyses. Metabolic‐related hub genes were selected through integration with curated metabolic gene sets. A prognostic model was subsequently developed using univariable and least absolute shrinkage and selection operator (LASSO) Cox regression analyses and validated in independent cohorts. A four‐gene metabolic signature consisting of CYP4F3, PFKL, G6PD, and DNMT3A was identified as an independent predictor of overall survival. Patients in the high‐risk group showed significantly poorer survival outcomes compared with low‐risk patients ( p < 0.0001). The prognostic model demonstrated stable and reliable predictive performance across training, testing, and validation cohorts. Functional enrichment analyses revealed that the identified genes are closely associated with metabolic pathways, immune‐related signaling, and leukemic microenvironment remodeling. Notably, increased expression of G6PD and PFKL was associated with adverse prognosis, supporting the contribution of altered glycolysis and redox homeostasis to AML pathogenesis.
Conclusions
Our findings establish a metabolic‐based prognostic signature with strong predictive utility in AML. The identified metabolic hub genes provide insight into the interaction between metabolic dysregulation and immune remodeling in leukemia and may serve as promising biomarkers for risk stratification and potential therapeutic targets.