The high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest. The patients’ gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy. After analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P< 0.001). Besides, the model can perform well in multiple datasets and could further stratify the cytogenetically normal AML patients and intermediate-risk AML population. Compared with the European Leukemia Net Risk Stratification System and other AML prognostic models, our model had the highest C-index and the largest AUC of the ROC curve, which demonstrated that our model had the best prognostic efficacy. A prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.

中文翻译：

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基于新的肿瘤免疫微环境分类的急性髓系白血病预后评分模型的建立

高度的异质性给急性髓系白血病（AML）的诊断和治疗带来了巨大的挑战。尽管已经提出了几种不同的AML预后评分模型来评估患者的预后，但准确性仍需提高。免疫细胞作为肿瘤微环境的重要组成部分，在肿瘤的生理功能中发挥着重要作用，具有一定的研究价值。因此，肿瘤免疫微环境（TIME）是否可以用于评估AML的预后引起了我们的极大兴趣。消除批次效应后，来自 7 个 GEO 数据库的患者基因表达谱被标准化。通过 Xcell 工具探索 TIME 单元组件，然后进行分层聚类以建立 TIME 分类。随后，Lasso-Cox 建立了预后模型。采用多个 GEO 数据库和癌症基因组图谱数据集来验证模型的预后性能。利用受试者工作特征（ROC）和一致性指数（C-指数）来评估预后效果。在分析 AML 中 TIME 细胞的组成后，我们发现有 10 种具有预后意义的细胞浸润。然后使用层次聚类方法，我们建立了一个 TIME 分类系统，将所有患者分为具有不同预后特征的三组。利用TIME分类中第一组和第三组之间的差异基因，我们构建了121个基因的预后模型。该模型成功地将1229名患者分为低组和高组，预后存在明显差异。总生存期较短的高组中，60岁以上患者较多，低危患者较多（均P<0.001）。此外，该模型在多个数据集中表现良好，可以进一步对细胞遗传学正常的 AML 患者和中危 AML 人群进行分层。与欧洲白血病净风险分层系统和其他AML预后模型相比，我们的模型具有最高的C指数和最大的ROC曲线AUC，这表明我们的模型具有最好的预后效果。我们的研究构建了基于TIME分类的AML预后模型，这可能为AML的精准治疗提供新的策略。