Comprehensive characterization of immunogenic cell death in acute myeloid leukemia revealing the association with prognosis and tumor immune microenvironment.

BACKGROUND

This study aimed to explore the clinical significance of immunogenic cell death (ICD) in acute myeloid leukemia (AML) and its relationship with the tumor immune microenvironment characteristics. It also aimed to provide a potential perspective for bridging the pathogenesis of AML and immunological research, and to provide a theoretical basis for precise individualized treatment of AML patients.

METHODS

Firstly, we identified two subtypes associated with ICD by consensus clustering and explored the biological enrichment pathways, somatic mutations, and tumor microenvironment landscape between the ICD subtypes. Additionally, we developed and validated a prognostic model associated with ICD-related genes. Finally, we conducted a preliminary exploration of the construction of disease regulatory networks and prediction of small molecule drugs based on five signature genes.

RESULTS

Differentially expressed ICD-related genes can distinguish AML into subgroups with significant differences in clinical characteristics and survival prognosis. The relationship between the ICD- high subgroup and the immune microenvironment was tight, showing significant enrichment in immune-related pathways such as antibody production in the intestinal immune environment, allograft rejection, and Leishmaniasis infection. Additionally, the ICD- high subtype showed significant upregulation in a variety of immune cells such as B_cells, Macrophages_M2, Monocytes, and T_cells_CD4. We constructed a prognostic risk feature based on five signature genes (TNF, CXCR3, CD4, PIK3CA and CALR), and the time-dependent ROC curve confirmed the high accuracy in predicting the clinical outcomes.

CONCLUSION

There is a strong close relationship between the ICD- high subgroup and the immune microenvironment. Immunogenicity-related genes have the potential to be a prognostic biomarker for AML.