This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes.Aims
Methods
Currently, the effect of drug treatment for osteoporosis is relatively poor, and the side effects are numerous and serious. Melatonin is a potential drug to improve bone mass in postmenopausal women. Unfortunately, the mechanism by which melatonin improves bone metabolism remains unclear. The aim of this study was to further investigate the potential mechanism of melatonin in the treatment of osteoporosis. The effects of melatonin on mitochondrial apoptosis protein, bmal1 gene, and related pathway proteins of RAW264.7 (mouse mononuclear macrophage leukaemia cells) were analyzed by western blot. Cell Counting Kit-8 was used to evaluate the effect of melatonin on cell viability. Flow cytometry was used to evaluate the effect of melatonin on the apoptosis of RAW264.7 cells and mitochondrial membrane potential. A reactive oxygen species (ROS) detection kit was used to evaluate the level of ROS in osteoclast precursors. We used bmal1-small interfering RNAs (siRNAs) to downregulate the Aims
Methods
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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