Glycolysis-Driven Prognostic Model for Acute Myeloid Leukemia: Insights into the Immune Landscape and Drug Sensitivity.

: Acute myeloid leukemia (AML), a malignant blood disease, is caused by the excessive growth of undifferentiated myeloid cells, which disrupt normal hematopoiesis and may invade several organs. Given the high heterogeneity in prognosis, identifying stable prognostic biomarkers is crucial for improved risk stratification and personalized treatment strategies. Although glycolysis has been extensively studied in cancer, its prognostic significance in AML remains unclear. : Glycolysis-related prognostic genes were identified by differential expression profiles. We modeled prognostic risk by least absolute shrinkage and selection operator (LASSO) regression and validated it by Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, and independent datasets (BeatAML2.0, GSE37642, GSE71014). Mechanisms were further explored through immune microenvironment analysis and drug sensitivity scores. : Differential expression and survival correlation analysis across the genes associated with glycolysis revealed multiple glycolytic genes associated with the outcomes of AML. We constructed a seven-gene prognostic model (, , , , , , ). Kaplan-Meier analysis demonstrated significantly reduced survival in high-risk patients (hazard ratio (HR) = 3.4, < 0.01). The model predicted the 1-, 3-, and 5-year survival outcomes, achieving area under the curve (AUC) values greater than 0.8. Immune profiling indicated distinct cellular compositions between risk groups: high-risk patients exhibited elevated monocytes and neutrophils but reduced Th1 cell infiltration. Drug sensitivity analysis showed that high-risk patients exhibited resistance to crizotinib and lapatinib but were more sensitive to motesanib. : We established a novel glycolysis-related gene signature for AML prognosis, enabling effective risk classification. Combined with immune microenvironment analysis and drug sensitivity analysis, we screened metabolic characteristics and identified an immune signature to provide deeper insight into AML. Our findings may assist in identifying new therapeutic targets and more effective personalized treatment regimes.