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Abstract：
Postmenopausal osteoporosis (PMOP) is a common metabolic inflammatory disease. In conditions of estrogen deficiency, chronic activation of the immune system leads to a hypo-inflammatory phenotype and alterations in its cytokine and immune cell profile, although immune cells play an important role in the pathology of osteoporosis, studies on this have been rare. Therefore, it is important to investigate the role of immune cell-related genes in PMOP. PMOP-related datasets were downloaded from the Gene Expression Omnibus database. Immune cells scores between high bone mineral density (BMD) and low BMD samples were assessed based on the single sample gene set enrichment analysis method. Subsequently, weighted gene co-expression network analysis was performed to identify modules highly associated with immune cells and obtain module genes. Differential analysis between high BMD and low BMD was also performed to obtain differentially expressed genes. Module genes are intersected with differentially expressed genes to obtain candidate genes, and functional enrichment analysis was performed. Machine learning methods were used to filter out the signature genes. The receiver operating characteristic (ROC) curves of the signature genes and the nomogram were plotted to determine whether the signature genes can be used as a molecular marker. Gene set enrichment analysis was also performed to explore the potential mechanism of the signature genes. Finally, RNA expression of signature genes was validated in blood samples from PMOP patients and normal control by real-time quantitative polymerase chain reaction. Our study of PMOP patients identified differences in immune cells (activated dendritic cell, CD56 bright natural killer cell, Central memory CD4 T cell, Effector memory CD4 T cell, Mast cell, Natural killer T cell, T follicular helper cell, Type 1 T-helper cell, and Type 17 T-helper cell) between high and low BMD patients. We obtained a total of 73 candidate genes based on modular genes and differential genes, and obtained 5 signature genes by least absolute shrinkage and selection operator and random forest model screening. ROC, principal component analysis, and t-distributed stochastic neighbor embedding down scaling analysis revealed that the 5 signature genes had good discriminatory ability between high and low BMD samples. A logistic regression model was constructed based on 5 signature genes, and both ROC and column line plots indicated that the model accuracy and applicability were good. Five signature genes were found to be associated with proteasome, mitochondria, and lysosome by gene set enrichment analysis. The real-time quantitative polymerase chain reaction results showed that the expression of the signature genes was significantly different between the 2 groups. HIST1H2AG, PYGM, NCKAP1, POMP, and LYPLA1 might play key roles in PMOP and be served as the biomarkers of PMOP.
摘要：
绝经后骨质疏松症(PMOP)是一种常见的代谢性炎症性疾病。在雌激素缺乏的情况下，免疫系统的慢性激活导致低炎症表型及其细胞因子和免疫细胞谱的改变，虽然免疫细胞在骨质疏松的病理过程中起着重要作用,这方面的研究很少见。因此,研究免疫细胞相关基因在PMOP中的作用具有重要意义。从基因表达综合数据库下载PMOP相关数据集。基于单样品基因集富集分析方法评估高骨矿物质密度（BMD）和低BMD样品之间的免疫细胞得分。随后，进行加权基因共表达网络分析以鉴定与免疫细胞高度相关的模块并获得模块基因。还进行了高BMD和低BMD之间的差异分析以获得差异表达的基因。模块基因与差异表达基因相交以获得候选基因，并进行了功能富集分析。使用机器学习方法来过滤标记基因。绘制标记基因的受试者操作特征(ROC)曲线和列线图以确定标记基因是否可用作分子标记。还进行了基因集富集分析以探索特征基因的潜在机制。最后,通过实时定量聚合酶链反应在来自PMOP患者和正常对照的血液样品中验证签名基因的RNA表达。我们对PMOP患者的研究发现了免疫细胞（活化的树突状细胞，CD56明亮的自然杀伤细胞，中央记忆CD4T细胞，效应记忆CD4T细胞，肥大细胞,自然杀伤T细胞,滤泡辅助性T细胞,1型辅助T细胞,和17型T辅助细胞）介于高和低BMD患者之间。我们基于模块基因和差异基因共获得73个候选基因，通过最小绝对收缩和选择算子以及随机森林模型筛选获得5个特征基因。ROC,主成分分析，t分布随机邻居嵌入缩小分析显示，5个特征基因对高、低BMD样本具有良好的判别能力。基于5个特征基因构建了逻辑回归模型，ROC和柱线图均表明模型的准确性和适用性良好。发现5个特征基因与蛋白酶体有关,线粒体,和溶酶体的基因集富集分析。实时定量聚合酶链反应结果显示,2组间特征基因表达差异显著。HIST1H2AG,PYGM,NCKAP1,POMP,LYPLA1可能在PMOP中起关键作用，并可作为PMOP的生物标志物。
