PDF(Pubmed)
Abstract：
In this study, we try to find the pathogenic role of immune-related genes in the bone marrow microenvironment of AML. Through WGCNA, seven modules were obtained, among which the turquoise module containing 1793 genes was highly correlated with the immune infiltration score. By unsupervised clustering, the turquoise module was divided into two clusters: the intersection of clinically significant genes in the TCGA and DEGs to obtain 178 genes for mutation analysis, followed by obtaining 17 genes with high mutation frequency. Subsequently, these 17 genes were subjected to LASSO regression analysis to construct a riskscore model of 8 hub genes. The TIMER database, ImmuCellAI portal website, and ssGSEA elucidate that the hub genes and risk scores are closely related to immune cell infiltration into the bone marrow microenvironment. In addition, we also validated the relative expression levels of hub genes using the TCGA database and GSE114868, and additional expression levels of hub genes in AML cell lines in vitro. Therefore, we constructed an immune infiltration-related gene model that identify 8 hub genes with good risk stratification and predictive prognosis for AML.
摘要：
在这项研究中,我们试图发现免疫相关基因在AML骨髓微环境中的致病作用。通过WGCNA,获得了七个模块，其中含有1793个基因的绿松石模块与免疫浸润评分高度相关。通过无监督聚类，将绿松石模块分为两个簇：TCGA和DEGs中具有临床意义的基因的交集，以获得178个基因进行突变分析，获得17个高突变频率基因。随后，对这17个基因进行LASSO回归分析,构建8个hub基因的风险评分模型.TIMER数据库,ImmuCellAI门户网站，ssGSEA阐明hub基因和风险评分与免疫细胞浸润骨髓微环境密切相关。此外,我们还使用TCGA数据库和GSE114868验证了hub基因的相对表达水平,以及体外AML细胞系中hub基因的其他表达水平.因此,我们构建了免疫浸润相关基因模型,该模型鉴定出8个对AML具有良好风险分层和预测预后的hub基因.
