Objective: To analyze the molecular markers associated with occurrence, development and poor prognosis of acute myeloid leukemia (AML) by using the data of GEO and TCGA database, as well as multiomics analysis.
Methods: The transcriptome data meeting requirements were down-loaded from GEO database, the differentially expressed genes were screened by using the R language limma package, and the GO function enrichment analysis and KEGG pathway analysis were performed for differentially expressed genes, at the same time, the protein interaction network was contracted by using STRING database and cytoscape software to screen out the hub gene, then the prognosis analysis was carried out for hub gene by combination with the clinical information affected in TCGA database.
Results: 620 differentially expressed genes were screened out, among which 162 differentially expressed genes were up-regulated, and 458 differentially expressed genes were down-regulated. Based on the results of GO functional enrichment, the KEGG pathway enrichment and protein interaction network, CXCL4, CXCR4, CXCR1, CXCR2, CCL5 and JUN were selected as hub genes. The survival analysis showed that the high expression of CXCL4, CXCR1, and CCL5 was a risk factor for poor prognosis of patiants.
Conclusion: CXCL4, CXCR1 and CCL5 can be used as biomarkers for the occurrence and development of AML, which relateds with the unfavorable prognosis and can provide a basis for further study.
题目: 急性髓系白血病患者预后不良相关基因的多组学分析.
目的: 利用GEO数据库和 TCGA数据库的数据，通过多组学分析方法分析与急性髓系白血病（acute myeloid leukemia，AML）发生、发展及不良预后相关的分子标志物。.
方法: 从GEO数据库下载符合要求的转录组数据，运用R语言Limma程序包进行差异表达基因的筛选，并对差异表达基因进行GO功能富集分析和 KEGG 通路富集分析，同时使用STRING数据库数据，利用Cytoscape软件构建蛋白质相互作用网络，筛选出hub gene，结合TCGA 数据库附带的临床信息对hub gene进行预后分析。.
结果: 共筛选出620个差异基因，上调的差异表达基因162个，下调的差异表达基因458个。综合GO功能富集、KEGG 通路富集分析及蛋白相互作用网络结果，筛选出CXCL4、CXCR4、CXCR1、CXCR2、CCL5、JUN为hub gene。生存分析显示，CXCL4、CXCR1、CCL5高表达是患者预后不良的危险因素。.
结论: CXCL4、CXCR1、CCL5可以作为AML发生、发展的相关生物标志物，且与不良预后相关，这可以为进一步研究提供依据。.