摘要目的:筛选急性髓系白血病（AML）潜在的生物标志物和药物，以期提高白血病的治愈率。方法:从基因表达综合（GEO）数据库下载包括AML患者和健康供者的基因芯片GSE90062数据集（包括3个白血病干细胞样本和3个正常骨髓造血干细胞样本）和GSE17054数据集（包括9个白血病干细胞样本和4个正常骨髓造血干细胞样本）。通过GEO2R软件分析GEO数据库两数据集AML患者和健康供者骨髓之间的共同差异表达基因（DEG）。利用DAVID在线软件对共同DEG进行基因本体（GO）生物功能富集分析和京都基因与基因组百科全书（KEGG）通路富集分析，利用STRING数据库构建蛋白相互作用（PPI）网络，借助CytoHubba插件筛选相关强度前10的关键基因。应用基因表达谱交互分析（GEPIA）数据库从10个关键基因中再次确认关键基因；采用Kaplan-Meier生存曲线评估10个关键基因不同水平AML患者的预后，比较采用log-rank检验；利用药物-基因相互作用数据库（DGIdb）筛选关键基因相关药物。结果:从GEO数据库筛选出AML患者骨髓中较健康供者骨髓上调的共同DEG 75个和下调的共同DEG 61个。在GO富集分析中，共同DEG主要与细胞凋亡、细胞迁移相关；在KEGG通路分析中，这些基因主要与细胞凋亡、造血通路相关。经PPI网络筛选出的强相关性前10个关键基因是ITGA4、ITGAL、HNRNPA3、CDC42、PRF1、SRSF3、HNRNPD、GTPBP4、CXCR4、RPL35A，其中6个基因（ITGA4、ITGAL、HNRNPA3、GTPBP4、CXCR4、RPL35A）通过GEPIA2数据库再次确认。Kaplan-Meier生存曲线分析显示，与ITGAL低表达患者相比，ITGAL高表达AML患者总生存不佳，差异有统计学意义（ P=0.010）；其余9个关键基因高表达与低表达患者间总生存差异均无统计学意义（均 P>0.05）。从DGIdb筛选出4种可能与ITGAL相关的药物，分别为efalizumab、odulimomab、lifitegrast、rovelizumab。 结论:ITGAL在AML患者骨髓中过表达，是AML患者的不利预后因素。筛选出的靶向ITGAL的潜在药物efalizumab、odulimomab、lifitegrast、rovelizumab为AML的治疗提供了新思路。

abstractsObjective:To screen the potential biomarkers and drugs in acute myeloid leukemia (AML) to improve the cure rate of leukemia.Methods:The GeneChip GSE90062 dataset (including 3 leukemia stem cell samples and 3 normal bone marrow hematopoietic stem cell samples) and GSE17054 dataset (including 9 leukemia stem cell samples and 4 normal bone marrow hematopoietic stem cell samples), which included AML patients and healthy donors, were downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed gene (DEG) between bone marrow of AML patients and healthy donors in both GEO database datasets was analyzed by GEO2R software. Gene Ontology (GO) biofunction enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of common DEG were performed using DAVID online software. The protein-protein interaction (PPI) networks were constructed using the STRING database, and key genes in the top 10 of the correlation intensities were screened with the help of CytoHubba plug-in. Gene Expression Profiling Interaction Analysis (GEPIA) database was applied to reconfirm the key genes from the 10 key genes; Kaplan-Meier survival curves were used to assess the prognosis of AML patients with different levels of the 10 key genes, and comparisons were made using the log-rank test; the Drug-Gene Interaction Database (DGIdb) was used to screen for the key gene-related drugs.Results:A total of 75 up-regulated common DEG and 61 down-regulated common DEG were identified. In GO enrichment analysis, common DEG was mainly associated with cell apoptosis and cell migration. In KEGG pathway analysis, they were mainly related to apoptosis and hematopoietic pathway. The top 10 key genes with strong correlations screened by the PPI networks were ITGA4, ITGAL, HNRNPA3, CDC42, PRF1, SRSF3, HNRNPD, GTPBP4, CXCR4, and RPL35A, of which 6 genes (ITGA4, ITGAL, HNRNPA3, GTPBP4, CXCR4, and RPL35A) were reconfirmed by the GEPIA2 database. Kaplan-Meier survival curve analysis showed a statistically significant difference in poor overall survival of AML patients with high expression of ITGAL compared to patients with low expression of ITGAL ( P = 0.010); the difference in overall survival between patients with high and low expression of the remaining 9 key genes was not statistically significant (all P > 0.05). Four potential drugs which may be related to ITGAL were screened from DGIdb, they were efalizumab, odulimomab, lifitegrast, and rovelizumab. Conclusions:ITGAL is overexpressed in the bone marrow of AML patients compared to healthy individuals and it is an unfavorable prognostic factor for AML patients. The screened potential drugs targeting ITGAL (efalizumab, odulimomab, lifitegrast, and rovelizumab) provides new ideas for the treatment of AML.