Constructing a tumor immune microenvironment-driven prognostic model in acute myeloid leukemia using bioinformatics...

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• The tumor immune microenvironment (TIME) significantly impacts prognosis in acute myeloid leukemia (AML).
• Accurate risk stratification and identification of therapeutic targets are crucial for improving AML patient outcomes.
• Existing prognostic models may not fully capture the complexity of immune cell infiltration and function within the TIME.

Purpose of the Study:

• To develop a prognostic model for AML based on immune-related hub differentially expressed genes (hub-DEGs).
• To refine risk stratification and identify potential therapeutic targets by analyzing the TIME.
• To validate the model's predictive accuracy and clinical utility in AML patients.

Main Methods:

• Transcriptomic and clinical data from TCGA-AML and GEO-AML cohorts were analyzed.
• Immune scores were inferred using ESTIMATE and xCell algorithms.
• Differentially expressed genes (DEGs), hub-DEGs, and prognostic genes were identified through network and survival analyses.
• An immune prognostic model (IPM) was constructed and validated, with correlation analyses to immune cell subsets.

Main Results:

• A total of 680 immune-related DEGs were identified, enriched in immune response pathways.
• Four key genes (CD163, IL10, MRC1, FCGR2B) were selected for the IPM.
• The IPM effectively stratified AML patients into high- and low-risk groups with significantly divergent overall survival (p=0.00072).
• High-risk scores correlated with immunosuppressive immune cell subsets, such as Tregs and M2 macrophages.

Conclusions:

• The developed TIME-centric prognostic model demonstrates clinical utility for AML risk stratification.
• The model aids in identifying potential therapeutic targets within the immune landscape of AML.
• Further prospective validation is warranted to enhance the translational applicability of this prognostic model.