Han Lin-Jing, Zhu Jian-Zong, Liu Hong-Cai, Lin Xiao-Sheng, Yang Shu-Zhong
Orthopedics Department, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Med (Lausanne). 2025 Apr 17;12:1544390. doi: 10.3389/fmed.2025.1544390. eCollection 2025.
Osteoporosis (OP), marked by reduced bone density and structural decay, poses a heightened risk of fractures. Our study formulates a predictive diagnostic model for OP by analyzing differential gene expression, thereby improving early diagnosis and therapeutic approaches.
Using GSE62402, GSE56815, and GSE35958 datasets from the Gene Expression Omnibus (GEO) database, we identified differentially expressed genes (DEGs) via R packages, and evaluated the underlying molecular mechanisms by network analysis. Immune checkpoint and drug sensitivity were analyzed to construct and validate diagnostic models. The single-sample gene-set enrichment analysis (ssGSEA) was used to assess immune cell infiltration; the CIBERSORT algorithm was used to evaluate immune cells within the different subtypes of OP.
The study identified 1,297 DEGs, with 14 DEGs related to autophagy, osteogenesis, and adipogenesis (AP&OG&AGRDEGs) showing significant expression differences between OP and control groups, including seven upregulated and seven downregulated genes (-value < 0.05). The analysis results from gene ontology (GO), gene set enrichment analysis (GSEA), and the Kyoto encyclopedia of genes and genomes (KEGG) indicated that oxidative stress and inflammation-related signaling pathways are closely connected to OP. Immune checkpoint analysis identified differential expression of eight genes between OP patients and controls (-value < 0.05). The ssGSEA findings showed significant variations in immune cell infiltration levels, particularly of natural killer cells, Th2 cells, mast cells, and plasmacytoid dendritic cells (-value < 0.05). The diagnostic model, developed utilizing logistic regression, support vector machine (SVM), and the least absolute shrinkage and selection operator (LASSO), pinpointed nine pivotal genes--and confirmed their diagnostic efficacy through validation. In further subgroup analysis, eight types of immune cells were found to be differentially expressed across various risk groups. Subtype analysis based on ConsensusClusterPlus revealed differential expression of six key genes in distinct subtypes of OP.
This comprehensive study established a network of OP-associated genes, and provides insights into the molecular mechanisms involving immune responses in OP. It identified key diagnostic genes and analyzed immune cell infiltration to better understand OP pathogenesis. The study underscores the importance of personalized treatment and the potential role of immune modulation in managing OP.
骨质疏松症（OP）以骨密度降低和结构破坏为特征，骨折风险增加。我们的研究通过分析差异基因表达构建了一种OP预测诊断模型，从而改善早期诊断和治疗方法。
使用来自基因表达综合数据库（GEO）的GSE62402、GSE56815和GSE35958数据集，我们通过R包识别差异表达基因（DEGs），并通过网络分析评估潜在的分子机制。分析免疫检查点和药物敏感性以构建和验证诊断模型。使用单样本基因集富集分析（ssGSEA）评估免疫细胞浸润；使用CIBERSORT算法评估OP不同亚型内的免疫细胞。
该研究鉴定出1297个DEGs，其中14个与自噬、成骨和成脂相关的差异表达基因（AP&OG&AGRDEGs）在OP组和对照组之间表现出显著的表达差异，包括7个上调基因和7个下调基因（P值<0.05）。基因本体论（GO）、基因集富集分析（GSEA）和京都基因与基因组百科全书（KEGG）的分析结果表明，氧化应激和炎症相关信号通路与OP密切相关。免疫检查点分析确定了OP患者和对照组之间8个基因的差异表达（P值<0.05）。ssGSEA结果显示免疫细胞浸润水平存在显著差异，尤其是自然杀伤细胞、Th2细胞、肥大细胞和浆细胞样树突状细胞（P值<0.05）。利用逻辑回归、支持向量机（SVM）和最小绝对收缩和选择算子（LASSO）开发的诊断模型确定了9个关键基因，并通过验证证实了它们的诊断效力。在进一步的亚组分析中，发现8种免疫细胞在不同风险组中差异表达。基于ConsensusClusterPlus的亚型分析揭示了OP不同亚型中6个关键基因的差异表达。
这项综合研究建立了一个OP相关基因网络，并深入了解了OP中涉及免疫反应的分子机制。它鉴定了关键诊断基因并分析了免疫细胞浸润，以更好地理解OP发病机制。该研究强调了个性化治疗的重要性以及免疫调节在OP管理中的潜在作用。
