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

Melatonin alleviates senile osteoporosis by regulating autophagy and enhancing fracture healing in aged mice

Aims

In our previous research, we have found that melatonin (MEL) affects the osteoporotic process. By balancing bone remoulding, autophagy is involved in age-related bone loss. However, as a regulator of autophagy, whether MEL influences senile osteoporosis via regulating autophagy remains unclear.

Methods

Cellular, radiological, and histopathological evaluations were performed on 36 16-month-old male C57BL6/L mice or aged bone marrow-derived mesenchymal stem cells. A MEL-gelatin methacrylamide system was constructed to aid osteoporotic fracture healing.

Results

In this study, we found that bone loss, low level of MEL, and decreased autophagy coexisted in aged C57BL6/L mice. A physiological (low, 10 nM but not 100 nM) concentration of MEL restored bone loss, transformed the cytokine framework, and increased the autophagic level in aged mice, whereas inhibition of autophagy unfavourably reduced the positive effects of MEL on bone mass. The autophagy-conducted increased osteogenic lineage commitment and extracellular matrix mineralization, but not matrix synthesis of aged bone marrow-derived mesenchymal stem cells, was responsible for MEL anabolic effects on bone. PIK3C-AKT-MTOR signal was tested to be a main pathway that is involved in MEL-induced autophagy.

Conclusion

Our data suggest that the application of MEL can restore degenerative osteogenesis of aged bone marrow-derived mesenchymal stem cells, and has the potential to regain bone mass in aged mice through activating autophagy via the PIK3C-AKT-MTOR pathway. MEL therefore may serve as a potential clinical therapy to treat senile osteoporosis.

Identification of the molecular link: STAT3 is a shared key gene linking postmenopausal osteoporosis and sarcopenia

Aims

This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms.

Methods

We analyzed microarray data from the Gene Expression Omnibus (GEO) database using weighted gene co-expression network analysis (WGCNA), machine learning, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify common genetic factors between POMP and sarcopenia. Further validation was done via differential gene expression in a new cohort. Single-cell analysis identified high expression cell subsets, with mononuclear macrophages in osteoporosis and muscle stem cells in sarcopenia, among others. A competitive endogenous RNA network suggested regulatory elements for these genes.

Results

Signal transducer and activator of transcription 3 (STAT3) was notably expressed in both conditions. Single-cell analysis pinpointed specific cells with high STAT3 expression, and microRNA (miRNA)-125a-5p emerged as a potential regulator. Experiments confirmed the crucial role of STAT3 in osteoclast differentiation and muscle proliferation.

Conclusion

STAT3 has emerged as a key gene in both POMP and sarcopenia. This insight positions STAT3 as a potential common therapeutic target, possibly improving management strategies for these age-related diseases.

Investigating the impact of Wnt pathway-related genes on biomarker and diagnostic model development for osteoporosis in postmenopausal females

The Wnt signaling pathway is essential for bone development and maintaining skeletal homeostasis, making it particularly relevant in osteoporosis patients. Our study aimed to identify distinct molecular clusters associated with the Wnt pathway and develop a diagnostic model for osteoporosis in postmenopausal Caucasian women. We downloaded three datasets (GSE56814, GSE56815 and GSE2208) related to osteoporosis from the GEO database. Our analysis identified a total of 371 differentially expressed genes (DEGs) between low and high bone mineral density (BMD) groups, with 12 genes associated with the Wnt signaling pathway, referred to as osteoporosis-associated Wnt pathway-related genes. Employing four independent machine learning models, we established a diagnostic model using the 12 osteoporosis-associated Wnt pathway-related genes in the training set. The XGB model showed the most promising discriminative potential. We further validate the predictive capability of our diagnostic model by applying it to three external datasets specifically related to osteoporosis. Subsequently, we constructed a diagnostic nomogram based on the five crucial genes identified from the XGB model. In addition, through the utilization of DGIdb, we identified a total of 30 molecular compounds or medications that exhibit potential as promising therapeutic targets for osteoporosis. In summary, our comprehensive analysis provides valuable insights into the relationship between the osteoporosis and Wnt signaling pathway.