We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. 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.Aims
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This study aimed to assess the effect of age and osteoporosis on the proliferative and differentiating capacity of bone-marrow-derived mesenchymal stem cells (MSCs) in female rats. We also discuss the role of these factors on expression and migration of cells along the C-X-C chemokine receptor type 4 (CXCR-4) / stromal derived factor 1 (SDF-1) axis. Mesenchymal stem cells were harvested from the femora of young, adult, and osteopenic Wistar rats. Cluster of differentiation (CD) marker and CXCR-4 expression was measured using flow cytometry. Cellular proliferation was measured using Alamar Blue, osteogenic differentiation was measured using alkaline phosphatase expression and alizarin red production, and adipogenic differentiation was measured using Oil red O. Cells were incubated in Boyden chambers to quantify their migration towards SDF-1. Data was analyzed using a Student’s Objectives
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