Department of Hematology, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, People's Republic of China.
Guangdong Medical University, Zhanjiang, People's Republic of China.
Hematology. 2022 Dec;27(1):840-848. doi: 10.1080/16078454.2022.2107970.
Acute myeloid leukemia (AML) is the most common acute blood malignancy in adults. The complicated and dynamic genomic instability (GI) is the most prominent feature of AML. Our study aimed to explore the prognostic value of GI-related genes in AML patients.
The mRNA data and mutation data were downloaded from the TCGA and GEO databases. Differential expression analyses were completed in limma package. GO and KEGG functional enrichment was conducted using clusterProfiler function of R. Univariate Cox and LASSO Cox regression analyses were performed to screen key genes for Risk score model construction. Nomogram was built with rms package.
We identified 114 DEGs between high TMB patients and low TMB AML patients, which were significantly enriched in 429 GO terms and 13 KEGG pathways. Based on the univariate Cox and LASSO Cox regression analyses, seven optimal genes were finally applied for Risk score model construction, including SELE, LGALS1, ITGAX, TMEM200A, SLC25A21, S100A4 and CRIP1. The Risk score could reliably predict the prognosis of AML patients. Age and Risk score were both independent prognostic indicators for AML, and the Nomogram based on them could also reliably predict the OS of AML patients.
A prognostic signature based on seven GI-related genes and a predictive Nomogram for AML patients are finally successfully constructed.
急性髓系白血病（AML）是成人中最常见的急性血液恶性肿瘤。复杂和动态的基因组不稳定性（GI）是 AML 的最显著特征。我们的研究旨在探讨 GI 相关基因在 AML 患者中的预后价值。
从 TCGA 和 GEO 数据库下载 mRNA 数据和突变数据。使用 limma 包完成差异表达分析。使用 R 中的 clusterProfiler 函数进行 GO 和 KEGG 功能富集。进行单变量 Cox 和 LASSO Cox 回归分析，以筛选关键基因进行风险评分模型构建。使用 rms 包构建列线图。
我们在高 TMB 患者和低 TMB AML 患者之间鉴定出 114 个差异表达基因，这些基因在 429 个 GO 术语和 13 个 KEGG 途径中显著富集。基于单变量 Cox 和 LASSO Cox 回归分析，最终应用七个最佳基因进行风险评分模型构建，包括 SELE、LGALS1、ITGAX、TMEM200A、SLC25A21、S100A4 和 CRIP1。风险评分可以可靠地预测 AML 患者的预后。年龄和风险评分都是 AML 的独立预后指标，基于它们的列线图也可以可靠地预测 AML 患者的 OS。
最终成功构建了基于七个 GI 相关基因的预后特征和用于 AML 患者的预测列线图。
