Deng Yuxuan, Wang Yunyun, Shi Qing, Jiang Yanxia
Department of Endocrinology, First Affiliated Hospital of Nanchang University, Nanchang, China.
Academic Affairs Office, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Front Genet. 2023 Feb 23;14:1028681. doi: 10.3389/fgene.2023.1028681. eCollection 2023.
Osteoporosis (OP) is a systemic bone disease caused by various factors, including, the decrease of bone density and quality, the destruction of bone microstructure, and the increase of bone fragility. It is a disease with a high incidence in a large proportion of the world's elderly population. However, osteoporosis lacks obvious symptoms and sensitive biomarkers. Therefore, it is extremely urgent to discover and identify disease-related biomarkers for early clinical diagnosis and effective intervention for osteoporosis. In our study, the Linear Models for Microarray Data (LIMMA) tool was used to screen differential expressed genes from transcriptome sequencing data of OP blood samples downloaded from the GEO database, and cluster Profiler was used for enriching analysis of differently expressed genes. In order to analyzed the relevance of gene modules, clinical symptoms, and the most related module setting genes associated with disease progression, we adapted Weighted Gene Co-expression Network Analysis (WGCNA) to screen and analyze the related pathways and relevant molecules. We used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to construct protein interaction network of key modules, and Cytoscape software was used to complete network visualization and screen of core genes in the network. Various plug-in algorithms of cytoHubba were used to identify key genes of OP. Finally, correlation analysis and single-gene gene probe concentration analysis (GSEA) analysis were performed for each core gene. Results of a total of 8 key genes that were closely related to the occurrence and development of OP were screened out, which provided a brand-new idea for the clinical diagnosis and early prevention of OP. Quantitative real-time PCR (qRT-PCR) was performed for validation, the expression levels of CUL1, PTEN and STAT1 genes in the OS group were significantly higher than in the non-OS groups. Receiver operating characteristic analysis demonstrated that CUL1, PTEN and STAT1 displayed considerable diagnostic accuracy for OS.
骨质疏松症（OP）是一种由多种因素引起的全身性骨病，包括骨密度和质量的下降、骨微结构的破坏以及骨脆性的增加。它是一种在世界上很大一部分老年人群中发病率很高的疾病。然而，骨质疏松症缺乏明显症状和敏感的生物标志物。因此，发现和鉴定与疾病相关的生物标志物以用于骨质疏松症的早期临床诊断和有效干预极为迫切。在我们的研究中，使用微阵列数据线性模型（LIMMA）工具从从基因表达综合数据库（GEO数据库）下载的OP血液样本转录组测序数据中筛选差异表达基因，并使用cluster Profiler对差异表达基因进行富集分析。为了分析基因模块、临床症状以及与疾病进展最相关的模块设定基因之间的相关性，我们采用加权基因共表达网络分析（WGCNA）来筛选和分析相关途径及相关分子。我们使用检索相互作用基因/蛋白质的搜索工具（STRING）数据库构建关键模块的蛋白质相互作用网络，并使用Cytoscape软件完成网络可视化以及网络中核心基因的筛选。使用cytoHubba的各种插件算法鉴定OP的关键基因。最后，对每个核心基因进行相关性分析和单基因基因探针浓度分析（GSEA）分析。结果筛选出总共8个与OP的发生和发展密切相关的关键基因，这为OP的临床诊断和早期预防提供了全新的思路。进行定量实时聚合酶链反应（qRT-PCR）验证，OS组中CUL1、PTEN和STAT1基因的表达水平显著高于非OS组。受试者工作特征分析表明，CUL1、PTEN和STAT1对OS显示出相当高的诊断准确性。
