INTRODUCTION

The prognostic management of acute myeloid leukemia (AML) remains a challenge for clinicians. This study aims to construct a novel risk model for AML patient through comprehensive analysis of scRNA and bulk RNA data to optimize the precise treatment strategies for patients and improve prognosis.

METHODS AND RESULTS

scRNA-seq classified cells into nine clusters, including Bcells, erythrocyte, granulocyte-macrophage progenitor (GMP), hematopoietic stem cell progenitors (HSC/Prog), monocyte/macrophagocyte (Mono/Macro), myelocyte, neutrophils, plasma, and T/NK cells. Functional analysis demonstrated the important role of inflammation immune response in the pathogenesis of AML, and the leukocyte transendothelial migration and adhesion in the process of inflammation should be noticed. ssGSEA method identified four core cells including GMP, HSC/Prog, Mono/Macro, and myelocyte for subsequent analysis, which contains 1,594 marker genes. Furthermore, we identified AML-associated genes (2,067genes) and DEGs (1,010genes) between AML patients and controls usingGSE114868dataset. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of five signature genes, namely, CALR, KDM1A, SUCNR1, TMEM220, and ADM. The prognostic model was then validated by two external datasets. Patients with high-risk scores are predisposed to experience poor overall survival. Further GSEA analysis of risk-model-related genes revealed the significant differences in inflammatory response between high-and low-risk groups.

CONCLUSION

In conclusion, we constructed an inflammation related risk model using internal scRNA data and external bulk RNA data, which can accurately distinguish survival outcomes in AML patients.