高低峰值骨量人群生物标志物差异及其在骨质疏松中的诊疗价值

目的 通过生物信息学分析高低峰值骨量人群生物标志物的差异，并验证其在骨质疏松症中的诊疗价值。方法 从GEO数据库获取高低峰值骨量人群基因表达数据集（GSE7158），利用R语言进行基因差异表达分析，然后进行差异基因GO功能注释和KEGG通路富集分析。利用STRING数据库获取差异基因蛋白互作网络，利用Cytoscape中的CytoHubba插件及R语言筛选得到关键基因及关键基因互作网络。最后，验证关键基因在骨质疏松症中的表达及诊疗价值。结果 共筛选得到高低峰值骨量人群差异表达基因182个，包括73个下调和109个上调基因。KEGG通路分析中破骨细胞分化通路、PI3K-AKT信号通路、糖尿病并发症中的AGE-RAGE信号通路和铁死亡信号通路值得关注。PPI网络分析得到11个关键基因：MCM7、BUB3、RBBP7、GNG2、FSHR、PCNA、CCR5、CDK16、SRSF7、NPM1和CPSF6；进一步验证分析发现CCR5、CDK16、RBBP7和SRSF7和骨质疏松症密切相关。结论 研究发现CCR5、CDK16、RBBP7和SRSF7可能与骨质疏松症的发病密切相关，可能成为早期筛选骨质疏松症高风险人群的生物标志物，对骨质疏松症的防治发挥重要作用，为后续进一步实验研究及临床治疗提供了有效依据。

Differences of biomarkers in high and low peak bone mass population and their diagnostic value in osteoporosis

Objective To identify the differences of biomarkers between high and low peak bone mass population, and their value in the diagnosis and treatment of osteoporosis. Methods The gene expression dataset (GSE7158) of high and low peak bone mass population was obtained from GEO database. The differentially expressed genes were analyzed with limma package in R. Then GO functional annotation and KEGG pathway enrichment analysis were performed. All differential genes were input into the STRING database to obtain the protein-protein interaction (PPI) network. Cytohubba plug-in in Cytoscape and R was used to identify key genes and their interaction network was further mapped by Cytoscape. Finally, further verification of the expression and diagnostic value of key genes in osteoporosis was performed. Results A total of 182 differentially expressed genes were screened in high and low peak bone mass population, among which 73 were down-regulated, and 109 were up-regulated. KEGG pathway analysis showed that osteoclast differentiation pathway, PI3K-Akt signaling pathway, and AGE-RAGE signaling pathway in diabetic complications and ferroptosis were worthy of attention. PPI network analysis revealed 11 key genes: MCM7, Bub3, RBBP7, GNG2, FSHR, PCNA, CCR5, CDK16, SRSF7, NPM1, and CPSF6. Further validation analysis found that CCR5, CDK16, RBBP7, and SRSF7 were closely related to osteoporosis. Conclusion CCR5, CDK16, RBBP7, and SRSF7 may be closely related to the incidence of osteoporosis, and may become biomarkers for early screening of people at high risk of osteoporosis. This provides an effective basis for further experimental research and clinical treatment.