Department of Orthopedics, Zibo Central Hospital, Zibo, 255000, Shandong, China.
J Orthop Surg Res. 2021 Mar 15;16(1):189. doi: 10.1186/s13018-021-02329-1.
Osteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatures for the clinical application.
We downloaded the mRNA profile of 90 peripheral blood samples with or without OP from GEO database (Number: GSE152073). Weighted gene co-expression network analysis (WGCNA) was used to reveal the correlation among genes in all samples. GO term and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. STRING database was applied to screen the interaction pairs among proteins. Protein-protein interaction (PPI) network was visualized based on Cytoscape, and the key genes were screened using the cytoHubba plug-in. The diagnostic model based on these key genes was constructed, and 5-fold cross validation method was applied to evaluate its reliability.
A gene module consisted of 176 genes predicted to be associated with the occurrence of OP was identified. A total of 16 significantly enriched GO terms and 1 significantly enriched KEGG pathway were obtained based on the 176 genes. The top 50 key genes in the PPI network were identified. Then 22 genes were screened based on stepwise regression analysis from the 50 key genes. Of which, 9 genes were further screened out by multivariate regression analysis with the significant threshold of P value < 0.01. The diagnostic model was established based on the optimal 9 key genes, which efficiently separated the normal samples and OP samples.
A diagnostic model established based on nine key genes could reliably separate OP patients from healthy subjects, which provided novel lightings on the diagnostic research of OP.
随着世界人口老龄化，骨质疏松症（OP）的发病率越来越高。迫切需要找到有效的诊断标志物用于临床应用。
我们从 GEO 数据库（编号：GSE152073）中下载了 90 例伴有或不伴有 OP 的外周血样本的 mRNA 谱。使用加权基因共表达网络分析（WGCNA）揭示所有样本中基因之间的相关性。通过 clusterProfiler R 包进行 GO 术语和 KEGG 通路富集分析。应用 STRING 数据库筛选蛋白间的相互作用对。基于 Cytoscape 可视化蛋白质-蛋白质相互作用（PPI）网络，并使用 cytoHubba 插件筛选关键基因。构建基于这些关键基因的诊断模型，并应用 5 倍交叉验证方法评估其可靠性。
鉴定出一个由 176 个基因组成的基因模块，预测与 OP 的发生有关。基于这 176 个基因，共获得 16 个显著富集的 GO 术语和 1 个显著富集的 KEGG 通路。在 PPI 网络中鉴定出前 50 个关键基因。然后，从这 50 个关键基因中，通过逐步回归分析筛选出 22 个基因。其中，通过多元回归分析，进一步筛选出 9 个具有 P 值<0.01 显著阈值的基因。基于这 9 个关键基因建立的诊断模型，可有效地将正常样本和 OP 样本区分开。
基于 9 个关键基因建立的诊断模型可可靠地区分 OP 患者和健康受试者，为 OP 的诊断研究提供了新的思路。
