| 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. |