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
Aims. This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze
Aims. Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA. Methods. First, we obtained gene expression profiles of cartilage, synovium, subchondral bone, and meniscus from the Gene Expression Omnibus (GEO). Several datasets were standardized by merging and removing batch effects. Then, we used unsupervised clustering to divide OA into three subtypes. The gene ontology and pathway enrichment of three subtypes were analyzed. CIBERSORT was used to evaluate the
Aims. This study aimed, through bioinformatics analysis, to identify the potential diagnostic markers of osteoarthritis, and analyze the role of
Exosomes (exo) are involved in the progression of osteoarthritis (OA). This study aimed to investigate the function of dysfunctional chondrocyte-derived exo (DC-exo) on OA in rats and rat macrophages. Rat-derived chondrocytes were isolated, and DCs induced with interleukin (IL)-1β were used for exo isolation. Rats with OA (n = 36) or macrophages were treated with DC-exo or phosphate-buffered saline (PBS). Macrophage polarization and autophagy, and degradation and chondrocyte activity of cartilage tissues, were examined. RNA sequencing was used to detect genes differentially expressed in DC-exo, followed by RNA pull-down and ribonucleoprotein immunoprecipitation (RIP). Long non-coding RNA osteoarthritis non-coding transcript (OANCT) and phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5) were depleted in DC-exo-treated macrophages and OA rats, in order to observe macrophage polarization and cartilage degradation. The PI3K/AKT/mammalian target of rapamycin (mTOR) pathway activity in cells and tissues was measured using western blot.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
Methods
Transforming growth factor-beta2 (TGF-β2) is recognized as a versatile cytokine that plays a vital role in regulation of joint development, homeostasis, and diseases, but its role as a biological mechanism is understood far less than that of its counterpart, TGF-β1. Cartilage as a load-resisting structure in vertebrates however displays a fragile performance when any tissue disturbance occurs, due to its lack of blood vessels, nerves, and lymphatics. Recent reports have indicated that TGF-β2 is involved in the physiological processes of chondrocytes such as proliferation, differentiation, migration, and apoptosis, and the pathological progress of cartilage such as osteoarthritis (OA) and rheumatoid arthritis (RA). TGF-β2 also shows its potent capacity in the repair of cartilage defects by recruiting autologous mesenchymal stem cells and promoting secretion of other growth factor clusters. In addition, some pioneering studies have already considered it as a potential target in the treatment of OA and RA. This article aims to summarize the current progress of TGF-β2 in cartilage development and diseases, which might provide new cues for remodelling of cartilage defect and intervention of cartilage diseases.
