Weighted gene coexpression network analysis identifies critical genes in different subtypes of acute myeloid leukaemia
2020
Yan-Hui Yu | Fei Xin | Lu Dong | Li Ge | Chun-Yan Zhai | Xu-Liang Shen
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.
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