The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730000, Gansu, China.
Department of Hematology, Lanzhou University Second Hospital, Lanzhou, 730000, Gansu, China.
J Cancer Res Clin Oncol. 2023 Oct;149(13):11457-11469. doi: 10.1007/s00432-023-05029-x. Epub 2023 Jun 30.
The study aims to investigate the impact of m6A modulators on drug resistance and the immune microenvironment in acute myeloid leukemia (AML). The emergence of drug resistance is a significant factor that contributes to relapse and refractory AML, leading to a poor prognosis.
The AML transcriptome data were retrieved from the TCGA database. The "oncoPredict" R package was utilized to assess the sensitivity of each sample to cytarabine (Ara-C) and classify them into distinct groups. Differential expression analysis was performed to identify m6A modulators differentially expressed between the two groups. Select Random Forest (RF) to build a predictive model. Model performance was evaluated using calibration curve, clinical decision curve, and clinical impact curve. The impacts of METTL3 on Ara-C sensitivity and immune microenvironment in AML were examined using GO, KEGG, CIBERSORT, and GSEA analyses.
Seventeen out of 26 m6A modulators exhibited differential expression between the Ara-C-sensitive and resistant groups, with a high degree of correlation. We selected the 5 genes with the highest scores in the RF model to build a reliable and accurate prediction model. METTL3 plays a vital role in m6A modification, and further analysis shows its impact on the sensitivity of AML cells to Ara-C through its interaction with 7 types of immune-infiltrating cells and autophagy.
This study utilizes m6A modulators to develop a prediction model for the sensitivity of AML patients to Ara-C, which can assist in treating AML drug resistance by targeting mRNA methylation.
本研究旨在探讨 m6A 调节剂对急性髓系白血病(AML)耐药性和免疫微环境的影响。耐药性的出现是导致 AML 复发和难治的重要因素,导致预后不良。
从 TCGA 数据库中检索 AML 转录组数据。使用“oncoPredict”R 包评估每个样本对阿糖胞苷(Ara-C)的敏感性,并将其分为不同的组。进行差异表达分析以鉴定两组之间差异表达的 m6A 调节剂。选择随机森林(RF)构建预测模型。使用校准曲线、临床决策曲线和临床影响曲线评估模型性能。使用 GO、KEGG、CIBERSORT 和 GSEA 分析检查 METTL3 对 AML 中 Ara-C 敏感性和免疫微环境的影响。
在 Ara-C 敏感和耐药组之间,有 17 种 m6A 调节剂表现出差异表达,具有高度相关性。我们选择 RF 模型中得分最高的 5 个基因构建可靠准确的预测模型。METTL3 在 m6A 修饰中发挥重要作用,进一步分析表明其通过与 7 种免疫细胞浸润和自噬相互作用,影响 AML 细胞对 Ara-C 的敏感性。
本研究利用 m6A 调节剂开发了一种预测 AML 患者对 Ara-C 敏感性的模型,通过靶向 mRNA 甲基化,可以辅助治疗 AML 耐药性。