Construction of a deep learning model and identification of BSG, PPARD, and SLC16A8 expression as potential indicators in the context of strategies for precision therapy to acute myeloid leukemia.

OBJECTIVE

Acute myeloid leukemia (AML) exhibits significant heterogeneity and aggressiveness. This study aimed to investigate T cell heterogeneity in the AML tumor microenvironment using single-cell RNA sequencing (scRNA-seq) and identify potential biomarkers for prognosis and precision therapy.

METHODS

scRNA-seq data from AML patient samples were analyzed to identify T cell subsets. A prognostic risk model was constructed using random forest and LASSO regression analyses based on key genes derived from a specific T cell cluster (Cluster 4). The model's predictive performance was validated using external datasets.

RESULTS

Analysis revealed significant functional heterogeneity among T cell subsets. Cluster 4 T cells showed distinct gene set activities related to immune regulation. Three genes - BSG, PPARD, and SLC16A8 - were identified as independent prognostic factors. The risk model effectively stratified patients into high-risk and low-risk groups, with the high-risk group demonstrating significantly poorer survival outcomes. The model showed robust predictive accuracy, with areas under the ROC curve of 0.78, 0.86, and 0.86 for 1-, 3-, and 5-year survival, respectively.

CONCLUSION

This study highlights the functional diversity of T cells in AML and identifies BSG, PPARD, and SLC16A8 as promising biomarkers for prognostic stratification. The developed risk model provides a valuable tool for guiding personalized treatment strategies in AML.