Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis.

Abstract

Given the extremely high inter-patient heterogeneity among acute myeloid leukemia (AML), identifying biomarkers for prognostic assessment and therapeutic guidance is crucial. Cell surface markers (CSMs) have been shown to play an important role in AML leukemogenesis and progression. In this study, we evaluate the prognostic potential of all human CSMs in AML patients based on differential gene expression analysis and univariate Cox regression analysis. Utilizing multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we construct a 9-CSMs prognostic model for risk stratification of AML patients. The predictive value of the 9-CSMs risk score is further confirmed in three independent datasets. Multivariate Cox regression analysis shows that the risk score is an independent prognostic factor for AML patients. AML patients with high 9-CSMs risk scores have shorter overall and event-free survival time than those with lower scores. Notably, our single-cell RNA-seq analysis indicates that patients with high 9-CSMs risk scores exhibit chemotherapy resistance. Further, PI3K inhibitors are identified as potential treatments for these high-risk patients. In conclusion, we construct a 9-CSMs prognostic model which is an independent prognostic factor for the survival of AML patients and has the potential to guide drug therapy.