Platelet‐Related Gene Signature Predicts Prognosis, Immune Landscape, and Drug Sensitivity in Acute Myeloid Leukemia

ABSTRACT

Acute myeloid leukemia (AML) remains challenging to treat due to clinical heterogeneity and a lack of prognostic biomarkers. To address this, we developed a prognostic signature based on platelet‐related genes (PRGs). By analyzing transcriptomic data from TCGA‐LAML, GSE146173, and Beat AML 2.0 cohorts, we identified and validated an 11‐gene signature (PSME2, PPIF, SYTL4, S100A4, CCND3, SMIM15, PARVB, STXBP5, KCNMB1, GABRE, SLC50A1) using LASSO‐Cox regression. This model effectively stratified patients into high‐ and low‐risk groups with distinct survival outcomes ( p  < 0.001) and demonstrated high predictive accuracy (1‐/3‐/5‐year AUC: 0.832/0.782/0.880). High‐risk patients exhibited immunosuppressive features, including upregulated immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1LG2, PDCD1), prominent monocyte infiltration, and reduced dendritic`11 cell activity. Drug sensitivity analysis suggested gefitinib, zebularine, and simvastatin as potential therapies for high‐risk AML ( p  < 0.05). We further validated the signature's prognostic value using qPCR and clinical grouping. Notably, in vitro studies indicated that KCNMB1 facilitates AML progression. In conclusion, our robust PRG‐based model elucidates the link between platelet biology, immune dysregulation, and therapeutic vulnerability in AML, offering clinical utility for risk stratification and treatment decisions.