Development and validation of a gene signature predicting the risk of postmenopausal osteoporosis

• Published in: Bone & joint research Vol. 11; no. 8; pp. 548 - 560
• Main Authors: Yuan, Wei, Yang, Maowei, Zhu, Yue
• Format: Journal Article
• Language: English
• Published: London British Editorial Society of Bone & Joint Surgery 01.08.2022; The British Editorial Society of Bone & Joint Surgery
• Subjects: Actin; Bone Biology; Gene expression; gene expressions; Gene signature; logistic regression analysis; Muscle contraction; Osteoporosis; Polymerase chain reaction; Post-menopause; Postmenopausal osteoporosis; Statistical analysis; Weighted gene co-expression network analysis
• ISSN: 2046-3758; 2046-3758
• DOI: 10.1302/2046-3758.118.BJR-2021-0565.R1

Aims We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Methods Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature. Results We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that RAB2A and FYCO1 were amplified in clinical samples. Conclusion The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of RAB2A and FYCO1 underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring. Cite this article: Bone Joint Res 2022;11(8):548–560.