Abstract
BACKGROUND: Acute Myeloid Leukemia (AML) is a highly heterogeneous hematologic malignancy influenced by various factors affecting prognosis. Recently, the role of coagulation-related genes in tumor biology has garnered increasing attention. This study aims to investigate the expression patterns of coagulation-related genes in AML and their clinical relevance. METHODS: We obtained RNA-seq data and clinical information for AML patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), followed by data cleaning and normalization. Unsupervised consensus clustering was performed to identify molecular subtypes, and Kaplan-Meier survival analysis was utilized to assess survival differences. We further identified differentially expressed genes (DEGs) between groups and conducted functional enrichment analyses. Additionally, a prognostic model was constructed using machine learning techniques, and its prognostic ability was validated. RESULTS: Clustering analysis categorized 151 tumor samples into the high coagulation-related gene expression group (C1, high-expression) and the low coagulation-related gene expression group (C2, low-expression), revealing 1,747 DEGs. Functional enrichment analysis indicated that DEGs were mainly associated with leukocyte migration and cytokine signaling pathways. Immune landscape analysis showed that the high expression group had elevated immune and stromal scores, distinct immune cell infiltration patterns, and a higher ESTIMATE score. The constructed coagulation score risk model indicated that age, cytogenetics, and risk scores were significantly associated with AML prognosis. Furthermore, intersection analysis using three machine learning methods identified MMP7 and F12 as key biomarkers. CONCLUSION: Our study demonstrates that coagulation-related genes play a crucial role in the molecular characteristics, prognostic assessment, and immune modulation in AML. MMP7 and F12 are highlighted as potential biomarkers that could aid in optimizing the diagnosis and treatment strategies for AML. These findings offer new insights into personalized therapies for AML.