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
Year: 2025
Type: article
Abstract: 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.
Source: Hematology
Authors: Yongyu Chen, Bin Liang, Ruilin He, Zhongming Zhang
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,557
OpenAlex ID: W4416587014