一种利用外周血单个核细胞预测多发性硬化症下次复发时间的新型基因特征。
A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells.
作者信息
Zhang Huimin, Yang Jiahui, Zhang Xiaobo, Wu Chaoyi, Zhao Zhen, Yang Ming, Wu Zhaoping
机构信息
Department of Neurology, The First People's Hospital of Lin'an District, Hangzhou, Zhejiang, 311300, China.
Department of Neurology, The Affiliated Zhuzhou Hospital Xiangya Medical College CSU, Zhuzhou, Hunan, 412007, China.
出版信息
BMC Neurol. 2025 Jul 1;25(1):261. doi: 10.1186/s12883-025-04231-3.
AIM
The purpose of this research study was to develop and validate a gene signature based on peripheral blood mononuclear cells (PBMCs) for predicting the time to the next relapse in multiple sclerosis (MS).
METHODS
The GSE15245 dataset (N = 94) was divided into a training set (N = 65) and a testing set (N = 29). First, the training set was analyzed using weighted gene co-expression network analysis (WGCNA) to identify key modules that were highly correlated with the timing of the next acute relapse. Subsequently, the hub genes within these key modules were subjected to univariate Cox regression analysis, and genes related to the recurrence time of MS were identified. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to refine the extraction further. Then, the gene signatures were constructed using multivariate Cox regression. The efficacy of the model that was based on the training set database was evaluated using receiver operating characteristic (ROC) curves and validated using an independent testing set. Additionally, gene signatures were also validated for differential expression using an external independent dataset, GSE21942 (N = 29), along with experimental verification.
RESULT
Two key modules were identified with WGCNA. Univariate Cox regression analysis yielded 30 genes related to the relapse time of MS from these two modules, and then LASSO regression analysis further refined the selection to four genes, namely, BLK, P2RX5, GP1BA, and PF4. These four genes were used within the training dataset to build a Cox regression model, and this showed high prediction performance in the training as well as the testing datasets. Both external dataset analysis and experimental validation corroborated the differential expression of BLK and P2RX5 in patients with MS.
CONCLUSION
BLK, P2RX5, GP1BA, and PF4 emerge as potential predictors of future disease activity in individuals with MS.
目的
本研究旨在开发并验证一种基于外周血单个核细胞（PBMC）的基因特征，用于预测多发性硬化症（MS）下次复发的时间。
方法
将GSE15245数据集（N = 94）分为训练集（N = 65）和测试集（N = 29）。首先，使用加权基因共表达网络分析（WGCNA）对训练集进行分析，以识别与下次急性复发时间高度相关的关键模块。随后，对这些关键模块内的枢纽基因进行单变量Cox回归分析，确定与MS复发时间相关的基因。使用最小绝对收缩和选择算子（LASSO）Cox回归进一步优化提取。然后，使用多变量Cox回归构建基因特征。基于训练集数据库的模型效能通过受试者工作特征（ROC）曲线进行评估，并使用独立测试集进行验证。此外，还使用外部独立数据集GSE21942（N = 29）以及实验验证对基因特征的差异表达进行了验证。
结果
通过WGCNA识别出两个关键模块。单变量Cox回归分析从这两个模块中得出30个与MS复发时间相关的基因，然后LASSO回归分析进一步将选择优化为四个基因，即BLK、P2RX5、GP1BA和PF4。在训练数据集中使用这四个基因构建了Cox回归模型，该模型在训练集和测试集中均显示出较高的预测性能。外部数据集分析和实验验证均证实了BLK和P2RX5在MS患者中的差异表达。
结论
BLK、P2RX5、GP1BA和PF4成为MS患者未来疾病活动的潜在预测指标。
Zotero 插件