Mao Songbo, Wang Yanbiao, Gu Mingyong, Liu Kai, Ma Jibin, Miao Jun, Zhao Fang
Orthopedics department, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, 250031, China.
Department of Orthopedics, The Third Affiliated Hospital of Shandong First Medical University（Affiliated Hospital of Shandong Academy of Medical Sciences), Jinan, 250031, China.
Sci Rep. 2025 Aug 25;15(1):31287. doi: 10.1038/s41598-025-16891-9.
Osteoporosis is a common skeletal disorder characterized by low bone mass and structural deterioration, leading to an increased risk of fractures. This study aimed to explore the molecular mechanisms underlying osteoporosis using bioinformatics approaches, with a focus on pattern recognition receptor-related differentially expressed genes (PRR-related DEGs). Two gene expression datasets (GSE7429 and GSE56815) were retrieved from the GEO database, comprising 30 osteoporosis samples and 30 matched controls. Data integration and batch effect correction were performed using the GEOquery and sva packages, followed by differential expression analysis with limma. A total of 1,052 pattern recognition receptor-related genes (PRR-related genes) were obtained from GeneCards, and 98 PRR-related DEGs were identified. GO and KEGG enrichment analyses revealed that these genes are primarily involved in the MAPK cascade, calcium ion homeostasis, leukocyte migration, and the regulation of inflammatory responses. A protein-protein interaction (PPI) network was constructed using STRING, and six hub genes (MDM2, AKT1, ESR1, NCOR1, CCND1, and NCOA2) were identified via CytoHubba. Regulatory networks involving miRNAs and transcription factors were constructed using ENCORI and ChIPBase. ROC curve analysis showed that ESR1 exhibited moderate diagnostic potential (AUC > 0.7). In conclusion, this study systematically identifies PRR-related DEGs and hub genes associated with osteoporosis through integrated bioinformatics analysis. These findings may contribute to a better understanding of immune-related molecular mechanisms and lay the groundwork for future research on potential diagnostic markers or therapeutic strategies.
骨质疏松症是一种常见的骨骼疾病，其特征为骨量低和结构退化，导致骨折风险增加。本研究旨在使用生物信息学方法探索骨质疏松症的分子机制，重点关注模式识别受体相关的差异表达基因（PRR相关DEG）。从GEO数据库中检索了两个基因表达数据集（GSE7429和GSE56815），包括30个骨质疏松症样本和30个匹配的对照。使用GEOquery和sva软件包进行数据整合和批次效应校正，随后用limma进行差异表达分析。从GeneCards获得了总共1052个模式识别受体相关基因（PRR相关基因），并鉴定出98个PRR相关DEG。GO和KEGG富集分析表明，这些基因主要参与MAPK级联反应、钙离子稳态、白细胞迁移以及炎症反应的调节。使用STRING构建了蛋白质-蛋白质相互作用（PPI）网络，并通过CytoHubba鉴定出六个枢纽基因（MDM2、AKT1、ESR1、NCOR1、CCND1和NCOA2）。使用ENCORI和ChIPBase构建了涉及miRNA和转录因子的调控网络。ROC曲线分析表明，ESR1具有中等诊断潜力（AUC>0.7）。总之，本研究通过综合生物信息学分析系统地鉴定了与骨质疏松症相关的PRR相关DEG和枢纽基因。这些发现可能有助于更好地理解免疫相关分子机制，并为未来潜在诊断标志物或治疗策略的研究奠定基础。
