ArticleFrontiers in genetics2025
The joint role of the immune microenvironment and N
Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Triggering receptor expressed on myeloid cell family in atherosclerosis: from mechanisms to intervention strategies.Cell communication and signaling : CCS · 2026Review
- Recent advances in deep learning for leukemia diagnosis: a scoping review of diagnostic modalities and fusion-based approaches.Frontiers in digital health · 2026Review
- Monitoring of (Leukemia-Specific) Immune Cells in Stages, Treatment Groups and in the Course of Disease and Therapy Contributes to Qualify Antileukemic Potential and Survival in Patients with AML.International journal of molecular sciences · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: The tumor immune microenvironment (TIME) and N Methods: Immune score for the Cancer Genome Atlas acute myeloid leukemia (AML) patients were calculated using the ESTIMATE algorithm, followed by identification of immune score-associated differentially expressed genes Non-negative matrix factorization (NMF) clustering was performed to stratify AML subtypes based on immune microenvironment (immune microenvironment)-related DEGs and 29 m7G regulatory genes. Intersecting DEGs co-linked to IME and m7G features were analyzed through weighted gene co-expression network analysis Weighted correlation network analysis combined with univariate Cox, LASSO, and multivariate Cox regression to establish a prognostic signature. Biological pathway disparities between risk subgroups were analyzed via Gene Set Enrichment Analysis, Gene Set Variation Analysis, and ssGSEA. A clinical nomogram integrating the signature with prognostic indicators was developed. The expression of the 12 prognostic genes were tested and compared in AML and healthy donors. Drug sensitivity predictions for high-risk patients were generated using oncoPredict, supported by molecular docking simulations of ligand-target interactions and Results: We constructed an IMEm7G prognostic signature comprising 12 genes (MPZL3, TREML2, PTP4A3, AHCYL1, CBR1, REEP5, PPM1H, WDFY3, LAMC3, KCTD1, DDIT4, KBTBD8) that robustly stratified AML risk and predicted survival in multiple cohorts. The high- and low-risk subgroups exhibited divergent biological pathways, mutational landscapes, immune infiltration patterns, immune checkpoint expression, and HLA profiles. This signature further guided therapeutic selection, with dactolisib identified as a high-risk-specific candidate. The quantitative real-time PCR (qPCR) analysis demonstrated that in comparison with healthy donors, the expression of WDFY3, PPM1H, and REEP5 was significantly lower, while that of PTP4A3, AHCYL1, CBR1, MPZL3, TREML2, and KBTBD8 was higher in AML patients. Conclusion: The IMEm7G gene signature established in our study effectively optimized the risk classification and predicted immunotherapy response in AML. Moreover, dactolisib was identified and demonstrated cytostatic activity alone and synergistic effects with doxorubicin in AML cells.
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