Weighted gene coexpression network analysis identifies critical genes in different subtypes of acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a common blood cancer with rapid progression and a high death rate. The aim of this study was to illustrate the molecular mechanism and identify potential prognostic genes by performing weighted gene coexpression network analysis (WGCNA) of AML. WGCNA of AML was performed with R software based on GDC The Cancer Genome Atlas (TCGA) Acute Myeloid Leukaemia (LAML) RNA-seq data from TCGA database. The gene modules showing significant correlation with the M0-M7 subtypes and the NPMc mutation were analysed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Hub genes from the key modules were identified using the cytoHubba package, and the prognostic values of these hub genes were analysed with a Cox proportional hazards model based on TCGA clinical data. A total of 151 patients from the TCGA database with RNA-seq data were included in the present study. The weighted gene coexpression network contained 21 coexpression modules. Three modules (blue, yellow and purple) were highly correlated with AML subtypes and the NPMc mutation. In total, six key hub genes were identified from the AML subtype- and NPMc mutation-related modules: TLR8, SLC15A3, ADAP2, HOXA6 and HOXA10. Kaplan–Meier curve analysis showed that the five hub genes in the gene expression network are significantly associated with AML prognosis. WGCNA of AML performed in the present study revealed potential prognostic genes that not only have important clinical significance for prognosis but also provide important clues for identifying effective targets in AML therapeutic strategies.

急性髓系白血病（Acute myeloid leukaemia, AML）是一类进展迅速、致死率较高的常见血液系统恶性肿瘤。本研究旨在通过对AML开展加权基因共表达网络分析（weighted gene coexpression network analysis, WGCNA），阐明其分子机制并筛选潜在预后基因。本研究基于基因组数据共享中心（Genomic Data Commons, GDC）的癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中急性髓系白血病（Acute Myeloid Leukaemia, LAML）的RNA测序数据，使用R软件完成AML的WGCNA分析。通过基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析，对与M0-M7亚型及NPMc突变显著相关的基因模块进行解析。利用cytoHubba工具包从关键模块中鉴定核心枢纽基因，并基于TCGA临床数据，通过Cox比例风险模型分析这些核心基因的预后价值。本研究共纳入TCGA数据库中151例具备RNA测序数据的AML患者。构建的加权基因共表达网络共包含21个共表达模块，其中蓝色、黄色与紫色3个模块与AML亚型及NPMc突变呈现显著相关性。从与AML亚型和NPMc突变相关的模块中共筛选得到6个核心枢纽基因：TLR8、SLC15A3、ADAP2、HOXA6及HOXA10。Kaplan-Meier曲线分析结果显示，该基因表达网络中的5个核心基因与AML预后显著相关。本研究通过对AML开展WGCNA分析，筛选得到潜在预后基因，这些基因不仅对AML的预后评估具有重要临床意义，同时也为AML治疗策略中的有效靶点鉴定提供了关键线索。