The joint role of the immune microenvironment and N7-methylguanosine for prognosis prediction and targeted therapy in acute myeloid leukemia.

Abstract

Background: The tumor immune microenvironment (TIME) and N⁷-methylguanosine (m7G) modification play crucial roles in the progression of acute myeloid leukemia (AML). This study aims to establish an IME-related and m7G-related prognostic model for improved risk stratification and personalized treatment in AML.

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 in vitro validation of candidate compounds in AML cell models.

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. In vitro CCK-8 assays validated dactolisib's monotherapy efficacy and synergistic cytotoxicity when combined with doxorubicin in AML cells.

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.