Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Hemotology, Education Department of Guangxi Zhuang Autonomous Region, Guangxi Medical University, Nanning, Guangxi 530021, People's Republic of China.
Hematology. 2022 Dec;27(1):1088-1100. doi: 10.1080/16078454.2022.2122281.
Acute myeloid leukemia (AML) is a malignant clonal disease characterized by abnormal proliferation of immature myeloid cells and bone marrow failure. Regulatory T cells (Treg) play a suppressive role in the anti-tumor immune response in the tumor microenvironment. Screening biomarkers based on Treg immune-related genes may help to predict the prognosis and the efficacy of immunotherapy of AML. Gene expression profiles of AML (non-M3) were obtained from the TCGA and GEO databases. Gene module related to Treg was extracted using CIBERSORT and WGCNA algorithms. Univariate Cox regression and LASSO analyses were performed to identify hub genes and constructed the immune prognostic model. Molecular and immunological features associated with risk signature were explored, and TIDE was used to predict the efficacy of immunotherapy. A risk signature was constructed based on the five IRGs (IFI27L1, YIPF6, PARVB, TRIM32 and RHOBTB3). The risk signature could be served as an independent prognostic factor of AML. Patients in the high-risk group had a poorer OS than those in the low-risk group. In addition, patients in the high-risk group had higher TP53 mutation rate, higher infiltration of Treg, higher immune escape potential and less benefit from ICI therapy compared to low-risk group. Our study constructed a prognostic index based on five Treg-related biomarkers, which help to facilitate the differentiation of immunological and molecular characteristics of AML, predict patient prognosis and provide a reference for predicting benefits from ICI therapy.
急性髓系白血病(AML)是一种恶性克隆性疾病,其特征是不成熟髓样细胞的异常增殖和骨髓衰竭。调节性 T 细胞(Treg)在肿瘤微环境中的抗肿瘤免疫反应中发挥抑制作用。基于 Treg 免疫相关基因筛选生物标志物可能有助于预测 AML 的预后和免疫治疗效果。从 TCGA 和 GEO 数据库中获得 AML(非 M3)的基因表达谱。使用 CIBERSORT 和 WGCNA 算法提取与 Treg 相关的基因模块。进行单变量 Cox 回归和 LASSO 分析,以识别关键基因并构建免疫预后模型。探索与风险特征相关的分子和免疫学特征,并使用 TIDE 预测免疫治疗的效果。基于五个 IRGs(IFI27L1、YIPF6、PARVB、TRIM32 和 RHOBTB3)构建风险特征。风险特征可作为 AML 的独立预后因素。高危组患者的 OS 比低危组患者差。此外,与低危组相比,高危组患者的 TP53 突变率更高,Treg 浸润更高,免疫逃逸潜力更高,从 ICI 治疗中获益更少。我们的研究构建了一个基于五个 Treg 相关生物标志物的预后指标,有助于区分 AML 的免疫学和分子特征,预测患者预后,并为预测 ICI 治疗获益提供参考。