Acute myeloid leukemia (AML) is a hematological malignancy with a high mortality rate and heterogeneous prognosis. Traditional risk stratification is based on the genetic classification in the 2022 guidelines of the European Leukemia Net. However, the risks of some patients remain unclear, and other prognostic assessment methods are required to improve the risk assessment of these patients. Apoptosis-related genes (ARGs) play critical roles in regulating the survival and drug resistance of AML cells. Therefore, we collected gene expression and clinical data from patients with AML from The Cancer Genome Atlas Acute Myeloid Leukemia (TCGA-LAML) datasets to develop a risk assessment model based on 5 ARGs. Using the least absolute shrinkage and selection operator Cox regression (LASSO-Cox) model, we identified 5 key ARGs ( DDIT4 , HSP90B1 , ENO1 , SOD1 , and SLC7A11 ) and constructed a 5-ARG prognostic model. Using this model, we successfully stratified patients in both TCGA-LAML training and independent external validation cohorts, with high-risk patients consistently exhibiting significantly poorer clinical outcomes. In addition, high-risk patients exhibited significant enrichment in pathways related to TP53 dysfunction, mechanistic target of rapamycin complex 1 (mTORC1) signaling activation, and pro-inflammatory responses, which were closely correlated with NPM1c-FLT3 co-mutations. Decitabine, sunitinib, and MK-1775 were identified as potential therapeutic agents. In summary, we established a 5-ARG prognostic model that may facilitate risk stratification and inform therapeutic decision-making in AML.
Pei et al. (2026) studied this question.