Pan Ying, Meng Ye, Zhai Zhimin, Xiong Shudao
Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
PeerJ. 2021 Jun 28;9:e11320. doi: 10.7717/peerj.11320. eCollection 2021.
Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles.
Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan-Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters.
GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients.
In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.
多发性骨髓瘤（MM）是第二常见的血液系统恶性肿瘤，发病率高，至今仍无法治愈。MM的发病机制尚不清楚。本研究旨在基于基因表达谱确定MM的新型预后模型。
从基因表达综合数据库（GEO）下载MM的基因表达数据（GSE6477、GSE136337）。使用limma软件包筛选GSE6477中病例样本与正常对照样本之间的差异表达基因（DEG）。同时进行富集分析，并通过STRING和Cytoscape软件构建这些DEG的蛋白质-蛋白质相互作用（PPI）网络。通过加权相关网络分析（WGCNA）构建基因共表达模块。从枢纽基因和DEG中鉴定关键基因。进行单因素和多因素Cox回归以筛选独立的预后基因，构建预测模型。通过Kaplan-Meier曲线和时间依赖性受试者工作特征（ROC）曲线评估模型的预测能力。最后，使用单因素和多因素Cox回归分析来研究预后模型是否独立于其他临床参数。
筛选出GSE6477数据集，包括101例病例和15例正常对照。共鉴定出178个DEG，包括59个上调基因和119个下调基因。在WGCNA分析中，黑色模块和紫色模块是与癌症特征最相关的模块，选择这两个模块中的92个枢纽基因进行进一步分析。接下来，从DEG和枢纽基因中选择47个基因作为关键基因。通过单因素和多因素Cox回归分析最终筛选出三个基因（LYVE1、RNASE1和RNASE2）并用于构建风险模型。此外，三基因预后模型显示出与MM患者其他临床参数相关的独立且准确的预后能力。
总之，我们鉴定并构建了一个基于三基因的预后模型，可用于预测MM患者的总生存期。
