ArticleAnnals of hematology2026
A macrophage-related efferocytosis-based two-gene prognostic model for acute myeloid leukemia identified by multi-omics and machine learning.
Article in Annals of hematology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background Acute myeloid leukemia (AML) remains a lethal hematologic malignancy with high heterogeneity. Macrophage-mediated efferocytosis in the tumor microenvironment is implicated in immune suppression and disease progression. Methods We integrated single-cell and bulk transcriptomic data from public cohorts to identify genes associated with macrophages and efferocytosis in AML. Candidate genes were screened for prognosis using univariate Cox regression. A comprehensive machine learning framework, evaluating 117 algorithm combinations, was employed to construct a robust prognostic model. The optimal LASSO and random survival forest approach identified CD52 and S100A4 as core prognostic genes. The resulting two-gene model was rigorously validated using Kaplan-Meier analysis, time-dependent ROC curves, and calibration plots across multiple independent cohorts. The associations of the risk score with the immune microenvironment and drug sensitivity were further analyzed. SHapley Additive exPlanations (SHAP) analysis was applied to interpret the model's decision-making. Results The two-gene signature demonstrated stable and powerful predictive performance for overall survival in both training and external validation sets. The risk score was an independent prognostic factor and showed significant correlations with immune cell infiltration patterns and response to chemotherapeutic agents. SHAP analysis confirmed the consistent and biologically plausible contributions of CD52 and S100A4. Single-cell resolution analysis revealed their specific enrichment in distinct AML-associated macrophage subpopulations. Conclusions We developed a novel macrophage efferocytosis-based prognostic model using a multi-omics and machine learning approach. This model provides valuable insights into the immune microenvironment of AML and offers a potential tool for risk stratification and therapeutic guidance.
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