EXPLORING SCS SIGNATURES AS POTENTIAL BIOMARKERS FOR PROGNOSTIC ASSESSMENT IN ACUTE MYELOID LEUKEMIA: A SPORTS MEDICINE PERSPECTIVE

Abstract

Objective: To establish a prognostic model for acute myeloid leukemia (AML) based on stem cell (SC) signatures, we utilized transcriptomic data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases to identify markers of stemness in AML cells. This study aimed to enhance the understanding of SC involvement in AML prognosis and potentially guide targeted therapies in a clinical setting. Methods: We downloaded transcriptomic data from TCGA and combined it with SC pathway genes for differential expression analysis. A total of 86 differentially expressed genes (DEGs) associated with the Wnt, Hippo, and Notch signaling pathways were identified. Through one-way Cox and Lasso regression analyses, we constructed a prognostic model comprising four key SC genes: DLL3, RASSF1, ID1, and ACTB. Results: The prognostic model was validated in vitro through immunohistochemistry, confirming the expression of DLL3, RASSF1, ID1, and ACTB in AML samples. The model demonstrated significant predictive capability for patient outcomes, emphasizing the role of SC-related pathways in the progression and prognosis of AML. Conclusion: The identification of SC signatures as prognostic markers offers a novel approach to predicting outcomes in AML patients. By understanding the underlying stemness-associated genetic alterations, we can potentially improve prognostic assessments and tailor more effective therapeutic strategies for AML, emphasizing a personalized medicine approach in oncological sports medicine, where maintaining peak physical health is crucial for recovery and rehabilitation in athlete patients undergoing treatment.