摘　要：为了构建急性髓系白血病（AML）患者的预后风险模型，本研究通过IMMPORT数据库提取免疫基因，通过基因表达综合（GEO）数据库和AML相关转录组RNA测序数据集筛选差异表达的免疫基因，基于癌症基因组图谱（TCGA）数据库下载AML相关的表达谱数据集（TCGA-LAML）及临床数据，对差异表达的免疫基因进一步进行单因素COX回归分析和多因素COX回归分析，最终确定了关键免疫基因，随后根据免疫基因进行单基因预后分析以及免疫浸润分析。结果显示：与正常对照组相比，在AML患者的骨髓样本中，有55个免疫基因显著上调或下调，其中由4个免疫基因（FGF13、GZMB、FLT3、CRLF3）构建的风险模型能预测AML患者的预后［曲线下面积（AUC）=0.741］；高风险组和低风险组之间免疫细胞的含量存在显著差异，其中FGF13、CRLF3为低风险基因，GZMB、FLT3为高风险基因。这为4个免疫基因FGF13、GZMB、FLT3、CRLF3及其构建的风险模型用于监测AML患者的预后和免疫浸润情况提供了理论基础。
关键词：急性髓系白血病；免疫浸润；预后；免疫基因；生物信息学
中图分类号：R733                            文献标志码：A　　　　　DOI：10.3969/j.issn.1007-7146.2022.06.012

Abstract: In order to build a prognostic risk model for patients with acute myeloid leukemia (AML), this study extracted immune genes through IMMPORT database, the Gene Expression Omnibus (GEO) database was used to search and download AML-related transcriptome RNA sequencing datasets, and the differentially expressed immune genes in AML were searched and analyzed. And we downloaded the AML related expression profile dataset (TCGA-LAML) and clinical data based on the Cancer Genome Atlas (TCGA) database, the differentially expressed immune genes were further analyzed by constructing the prognostic risk model through univariate COX regression analysis and multivariate COX regression analysis. The key immune genes were finally determined, and the single gene prognosis analysis and immune infiltration analysis were carried out according to the immune genes. The results of analysis showed that compared with the normal control group, 55 immune genes were significantly up-regulated or down-regulated in the bone marrow samples of AML patients. The risk model based on four immune genes (FGF13, GZMB, FLT3 and CRLF3) could accurately predict the prognosis of AML patients ［in which the area under the curve (AUC) = 0.741］. There were significant differences in the content of immune cells in high/low-risk groups. FGF13 and CRLF3 were low-risk genes, GZMB and FLT3 were high-risk genes. We constructed a risk model based on four immune genes (FGF13, GZMB, FLT3, CRLF3), which provides a theoretical basis for monitoring the prognosis and immune infiltration of AML patients.
Key words: acute myeloid leukemia; immune infiltration; prognosis; immune gene; bioinformatics
（Acta Laser Biology Sinica, 2022, 31(6): 568-576）