骨质疏松,作为一种常见的代谢紊乱,以骨量减少为特征,会导致骨折,从而威胁女性的生活质量,尤其是绝经后的妇女。因此,有必要揭示与骨质疏松症有关的基因并探索骨质疏松症的生物标志物。在这项研究中,两组,不同骨密度(BMD)水平的吸烟者和非吸烟者,从基因表达综合(GEO)数据库GSE13850收集。确定了两组的共识模块;观察了不同BMD水平的吸烟者和非吸烟者之间基因模块的多样性;以及共识模块,包括390个与不同BMD水平显著相关的基因,已确定。功能分析揭示了显著丰富的骨质疏松症相关通路,如PI3K-Akt信号通路。Hub基因分析揭示了CXCL12和CHRM2在与BMD水平相关的模块中的关键作用。基于支持向量机递归特征消除(SVM-RFE)分析,模型包含10个基因(TMS4,IRF2,BSG,GZMM,ARRB2COX15RALY,TP53,RPS6KA3和SYNPO)在识别具有不同BMD水平的人方面具有良好的性能。其中,实验验证了RALY和SYNPO在hBMSCs成骨分化中的作用。总的来说,本研究提供了一种通过共识模块分析来探索骨质疏松症生物标志物的策略.
Osteoporosis, as a common metabolic disorder characterized by the decrease of bone mass, can cause fractures, thereby threatening the life quality of females, especially postmenopausal women. Thus, it is necessary to reveal the genes involved in osteoporosis and explore biomarkers for osteoporosis. In this study, two groups, smokers and nonsmokers with different bone mineral density (BMD) levels, were collected from the Gene Expression Omnibus (GEO) database GSE13850.
Consensus modules of the two groups were identified; the variety of gene modules between smokers and nonsmokers with different BMD levels was observed; and a
consensus module, including 390 genes significantly correlated with different BMD levels, was identified. Function analysis revealed the significantly enriched osteoporosis-related pathways, such as the PI3K-Akt signaling pathway. Hub genes analysis revealed the critical role of CXCL12 and CHRM2 in modules related to BMD levels. Based on the support vector machine recursive feature elimination (SVM-RFE) analysis, the model containing 10 genes (TNS4, IRF2, BSG, GZMM, ARRB2, COX15, RALY, TP53, RPS6KA3, and SYNPO) with good performance in identifying people with different BMD levels was constructed. Among them, the roles of RALY and SYNPO in the osteogenic differentiation of hBMSCs were verified experimentally. Overall, this study provides a strategy to explore the biomarkers for osteoporosis through analysis of
consensus modules.