Prediction of acute myeloid leukemia prognosis based on autophagy features and characterization of its immune microenvironment

Background:
Autophagy plays a crucial role in the survival of acute myeloid leukemia (AML) cells by removing damaged organelles and proteins, thereby protecting cells from stress-induced apoptosis. While numerous studies have identified potential autophagy-related genes (ARGs) associated with AML prognosis, significant challenges remain in accurately predicting patient survival outcomes. To enhance prognostic accuracy, it is essential to identify novel autophagy gene markers using molecular-level insights.
Methods:
This study employed Random Forest, SVM, and XGBoost algorithms to identify autophagy genes linked to AML prognosis. Subsequently, Lasso-Cox regression analysis was used to pinpoint six autophagy genes (TSC2, CALCOCO2, BAG3, UBQLN4, ULK1, and DAPK1) significantly associated with overall survival (OS). These genes were incorporated into a predictive model for prognosis. Additionally, the immunological microenvironment of these autophagy genes was analyzed to further elucidate their role in AML.
Results:
The developed predictive model demonstrated strong prognostic capability. After adjusting for clinicopathologic parameters, the model proved to be an independent prognostic predictor and was validated ARS853 using an external AML dataset. Analysis of differentially expressed genes between high-risk and low-risk groups revealed enrichment in immune-related pathways, including humoral immune responses, T cell differentiation in the thymus, and lymphocyte differentiation. Immune infiltration analysis showed significantly lower cellular abundance of activated CD4+ memory T cells, activated NK cells, and CD4+ T cells in the high-risk group compared to the low-risk group.
Conclusion:
This study systematically identified and analyzed autophagy-related genes to develop a robust prognostic model for AML, offering a more precise assessment of patient outcomes. These findings not only improve prognostic evaluation and therapeutic strategies but also provide a foundation for future research and clinical applications targeting autophagy-related pathways in AML.