Wang Jing, Guo Lili, Lv Chenglan, Zhou Min, Wan Yuan
Department of Oncology and Hematology, Yizheng Hospital of Nanjing Drum Tower Hospital Group, Yizheng, China.
Department of Hematology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Front Oncol. 2023 Feb 23;13:1105196. doi: 10.3389/fonc.2023.1105196. eCollection 2023.
Multiple myeloma (MM) remains an essentially incurable disease. This study aimed to establish a predictive model for estimating prognosis in newly diagnosed MM based on gene expression profiles.
RNA-seq data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study and the Genotype-Tissue Expression (GTEx) databases. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction network analysis were performed to identify hub genes. Enrichment analysis was also conducted. Patients were randomly split into training (70%) and validation (30%) datasets to build a prognostic scoring model based on the least absolute shrinkage and selection operator (LASSO). CIBERSORT was applied to estimate the proportion of 22 immune cells in the microenvironment. Drug sensitivity was analyzed using the OncoPredict algorithm.
A total of 860 newly diagnosed MM samples and 444 normal counterparts were screened as the datasets. WGCNA was applied to analyze the RNA-seq data of 1589 intersecting genes between differentially expressed genes and prognostic genes. The blue module in the PPI networks was analyzed with Cytoscape, and 10 hub genes were identified using the MCODE plug-in. A three-gene (TTK, GINS1, and NCAPG) prognostic model was constructed. This risk model showed remarkable prognostic value. CIBERSORT assessment revealed the risk model to be correlated with activated memory CD4 T cells, M0 macrophages, M1 macrophages, eosinophils, activated dendritic cells, and activated mast cells. Furthermore, based on OncoPredict, high-risk MM patients were sensitive to eight drugs.
We identified and constructed a three-gene-based prognostic model, which may provide new and in-depth insights into the treatment of MM patients.
多发性骨髓瘤（MM）仍然是一种基本上无法治愈的疾病。本研究旨在基于基因表达谱建立一种预测新诊断MM患者预后的模型。
从多发性骨髓瘤研究基金会（MMRF）的CoMMpass研究和基因型-组织表达（GTEx）数据库下载RNA测序数据。进行加权基因共表达网络分析（WGCNA）和蛋白质-蛋白质相互作用网络分析以鉴定枢纽基因。还进行了富集分析。将患者随机分为训练集（70%）和验证集（30%），以基于最小绝对收缩和选择算子（LASSO）构建预后评分模型。应用CIBERSORT估计微环境中22种免疫细胞的比例。使用OncoPredict算法分析药物敏感性。
共筛选出860例新诊断的MM样本和444例正常对照样本作为数据集。应用WGCNA分析差异表达基因和预后基因之间1589个交集基因的RNA测序数据。使用Cytoscape分析PPI网络中的蓝色模块，并使用MCODE插件鉴定出10个枢纽基因。构建了一个三基因（TTK、GINS1和NCAPG）预后模型。该风险模型显示出显著的预后价值。CIBERSORT评估显示该风险模型与活化记忆CD4 T细胞、M0巨噬细胞、M1巨噬细胞、嗜酸性粒细胞、活化树突状细胞和活化肥大细胞相关。此外，基于OncoPredict，高危MM患者对8种药物敏感。
我们鉴定并构建了一种基于三基因的预后模型，这可能为MM患者的治疗提供新的深入见解。
