Abstract
Acute myeloid leukemia (AML) remains challenging due to molecular heterogeneity and limited prognostic models integrating tumor microenvironment dynamics. While thrombocytopenia correlates with poor outcomes, the roles of platelet-related genes (PRGs) in AML pathogenesis are unclear. We integrated multiomics data to analyze platelet-clinicopathological associations, identify PRGs via weighted gene coexpression network analysis, and define molecular subtypes through unsupervised clustering. A machine learning-derived PRGScore model was developed and validated. Immune features were assessed using CIBERSORT, and drug responses via ex vivo profiling. The results showed that low platelet counts predicted a poor prognosis and were inversely correlated with blast proportions, suggesting suppression of leukemia-mediated megakaryopoiesis. Platelet recovery is linked to inflammatory and coagulation pathways. We identified 22 key PRGs that were downregulated in AML and enriched in immunomodulatory pathways. Unsupervised clustering stratified AML into three PRG-based subtypes: C1 (low PRGs/platelets, best survival), C2 (intermediate), and C3 (high PRGs/platelets, worst survival). C1 exhibited cytotoxic T-cell enrichment, reduced immune checkpoint expression, a high proportion of blasts and increased proliferative activity. C3 was characterized by myeloid immunosuppression, elevated checkpoint levels, and the activation of chemoresistance pathways. The PRGScore model demonstrated robust prognostic accuracy across the 10 cohorts. Patients with a high PRGScore had a significantly worse prognosis and sensitivity to immune checkpoint inhibitors, whereas patients with a low PRGScore had a better response to cytarabine and venetoclax. This study establishes PRGs as key regulators of AML biology and prognosis, bridging platelet dynamics, immune interactions, and machine learning. The PRGScore framework advances precision medicine through risk stratification, therapeutic targeting, and biomarker discovery.