Lanzhou University Second Hospital, Gansu, 730000, China.
J Cancer Res Clin Oncol. 2023 Sep;149(12):9609-9619. doi: 10.1007/s00432-023-04871-3. Epub 2023 May 24.
Acute myeloid leukemia (AML) is a hematological cancer driven on by aberrant myeloid precursor cell proliferation and differentiation. A prognostic model was created in this study to direct therapeutic care.
Differentially expressed genes (DEGs) were investigated using the RNA-seq data from the TCGA-LAML and GTEx. Weighted Gene Coexpression Network Analysis (WGCNA) examines the genes involved in cancer. Find the intersection genes and construct the PPI network to discover hub genes and remove prognosis-related genes. A nomogram was produced for predicting the prognosis of AML patients using the risk prognosis model that was constructed using COX and Lasso regression analysis. GO, KEGG, and ssGSEA analysis were used to look into its biological function. TIDE score predicts immunotherapy response.
Differentially expressed gene analysis revealed 1004 genes, WGCNA analysis revealed 19,575 tumor-related genes, and 941 intersection genes in total. Twelve prognostic genes were found using the PPI network and prognostic analysis. To build a risk rating model, RPS3A and PSMA2 were examined using COX and Lasso regression analysis. The risk score was used to divide the patients into two groups, and Kaplan-Meier analysis indicated that the two groups had different overall survival rates. Univariate and multivariate COX studies demonstrated that risk score is an independent prognostic factor. According to the TIDE study, the immunotherapy response was better in the low-risk group than in the high-risk group.
We eventually selected out two molecules to construct prediction models that might be used as biomarkers for predicting AML immunotherapy and prognosis.
急性髓系白血病（AML）是一种由异常髓系前体细胞增殖和分化驱动的血液系统癌症。本研究建立了一个预后模型，以指导治疗。
使用 TCGA-LAML 和 GTEx 的 RNA-seq 数据进行差异表达基因（DEG）分析。加权基因共表达网络分析（WGCNA）研究癌症相关基因。找到交集基因，并构建 PPI 网络，发现关键基因并去除与预后相关的基因。使用 COX 和 Lasso 回归分析构建风险预后模型，为 AML 患者的预后预测生成列线图。GO、KEGG 和 ssGSEA 分析用于研究其生物学功能。TIDE 评分预测免疫治疗反应。
差异表达基因分析显示有 1004 个基因，WGCNA 分析显示有 19575 个肿瘤相关基因，总共有 941 个交集基因。使用 PPI 网络和预后分析发现了 12 个预后基因。为了构建风险评分模型，使用 COX 和 Lasso 回归分析研究 RPS3A 和 PSMA2。使用风险评分将患者分为两组，Kaplan-Meier 分析表明两组的总生存率不同。单因素和多因素 COX 研究表明风险评分是一个独立的预后因素。根据 TIDE 研究，低风险组的免疫治疗反应优于高风险组。
我们最终选择了两种分子来构建预测模型，这些模型可能被用作预测 AML 免疫治疗和预后的生物标志物。
