ArticleScientific reports2024
Identification of a novel monocyte/macrophage-related gene signature for predicting survival and immune response in acute myeloid leukemia.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning-driven M2 macrophage signature for precision prediction of survival and therapy response in acute myeloid leukemia.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- MAP3K1: A Multifunctional Kinase at the Crossroads of Cancer Progression and Tumor Suppression.Cells · 2026Review
- Review
- Study on the mechanism and clinical value of miR-210-3p and miR-582-5p in acute myocardial infarction by targeting MXD1.Journal of cardiothoracic surgery · 2025Article
- Integrative multi-omic analysis of NLRP3 inflammasome dysregulation and subtyping for personalized treatment in acute myeloid leukemia.Discover oncology · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Acute myeloid leukemia (AML) is a heterogeneous hematological tumor with poor immunotherapy effect. This study was to develop a monocyte/macrophage-related prognostic risk score (MMrisk) and identify new therapeutic biomarkers for AML. We utilized differentially expressed genes (DEGs) in combination with single-cell RNA sequencing to identify monocyte/macrophage-related genes (MMGs). Eight genes were selected for the construction of a MMrisk model using univariate Cox regression analysis and LASSO regression analysis. We then validated the MMrisk on two GEO datasets. Lastly, we investigated the immunologic characteristics and advantages of immunotherapy and potential targeted drugs for MMrisk groups. Our study identified that the MMrisk is composed of eight MMGs, including HOPX, CSTB, MAP3K1, LGALS1, CFD, MXD1, CASP1 and BCL2A1. The low MMrisk group survived longer than high MMrisk group (P < 0.001). The high MMrisk group was positively correlated with B cells, plasma cells, CD4 memory cells, Mast cells, CAFs, monocytes, M2 macrophages, Endothelial, tumor mutation, and most immune checkpoints (PD1, Tim-3, CTLA4, LAG3). Furthermore, drug sensitivity analysis showed that AZD.2281, Axitinib, AUY922, ABT.888, and ATRA were effective in high-risk MM patients. Our research shows that MMrisk is a potential biomarker which is helpful to identify the molecular characteristics of AML immunology.
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