Yuan Wei, Yang Maowei, Zhu Yue
Department of Orthopedics, The First Hospital of China Medical University, Shenyang, China.
Bone Joint Res. 2022 Aug;11(8):548-560. doi: 10.1302/2046-3758.118.BJR-2021-0565.R1.
We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism.
Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell's concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature.
We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that and were amplified in clinical samples.
The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of and underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring.Cite this article: 2022;11(8):548-560.
通过研究绝经后骨质疏松症（PMOP）的遗传机制，开发一种预测其发生的基因特征。
从基因表达综合数据库获得五个数据集。采用无监督一致性聚类分析来确定新的PMOP亚型。为了确定与PMOP相关的核心基因和核心模块，应用了加权基因共表达网络分析（WCGNA）。基因本体富集分析用于探索关键基因背后的生物学过程。采用逻辑回归单变量分析筛选具有统计学意义的变量。使用两种算法选择与PMOP相关的重要基因。采用逻辑回归模型构建与PMOP相关的基因图谱。使用曲线下的受试者工作特征面积、Harrell一致性指数、校准图和决策曲线分析来表征与PMOP相关的基因。然后，采用定量实时聚合酶链反应（qRT-PCR）验证基因特征中与PMOP相关基因的表达。
我们确定了三种与PMOP相关的亚型和四个核心模块。在枢纽基因中，肌肉系统过程、肌肉收缩和基于肌动蛋白丝的运动更为活跃。我们获得了五个与PMOP相关的特征基因。我们的分析证实该基因特征具有良好的预测能力和适用性。发现GSE56815队列的结果与早期研究结果一致。qRT-PCR结果显示，[此处原文可能有缺失信息]在临床样本中被扩增。
我们开发并验证的与PMOP相关的基因特征可以准确预测患者发生PMOP的风险。这些结果可以阐明PMOP潜在的[此处原文可能有缺失信息]分子机制，并产生新的和改进的治疗策略，最终有助于PMOP的监测。引用本文：2022；11(8):548 - 560。
