Identifying a possible new target for diagnosis and treatment of postmenopausal osteoporosis through bioinformatics and clinical sample analysis

Comparison between Two Different Synthesis Methods of Strontium-Doped Hydroxyapatite Designed for Osteoporotic Bone Restoration

Development of efficient controlled local release of drugs that prevent systemic side effects is a challenge for anti-osteoporotic treatments. Research for new bone-regeneration materials is of high importance. Strontium (Sr) is known as an anti-resorptive and anabolic agent useful in treating osteoporosis. In this study, we compared two different types of synthesis used for obtaining nano hydroxyapatite (HA) and Sr-containing nano hydroxyapatite (SrHA) for bone tissue engineering. Synthesis of HA and SrHA was performed using co-precipitation and hydrothermal methods. Regardless of the synthesis route for the SrHA, the intended content of Sr was 1, 5, 10, 20, and 30 molar %. The chemical, morphological, and biocompatibility properties of HA and SrHA were investigated. Based on our results, it was shown that HA and SrHA exhibited low cytotoxicity and demonstrated toxic behavior only at higher Sr concentrations.

Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.

Long non-coding RNA Homeobox D gene cluster antisense growth-associated long noncoding RNA/microRNA-182-5p/Homeobox protein A10 alleviates postmenopausal osteoporosis via accelerating osteoblast differentiation of bone marrow mesenchymal stem cells

BACKGROUND

Studies have illuminated that long non-coding RNA (lncRNA) influences bone cell differentiation and formation. Nevertheless, whether lncRNA Homeobox D gene cluster antisense growth-associated long noncoding RNA (HAGLR) was implicated in postmenopausal osteoporosis (PMOP) was yet uncertain.

PURPOSE

The research was to explore HAGLR's role in the osteogenic differentiation (OD) process of bone marrow mesenchymal stem cells (BMSCs).

METHODS

BMSCs were isolated from mouse bone marrow tissues and identified by electron microscope and flow cytometry. HAGLR, microRNA (miR)-182-5p, and homeobox protein A10 (Hoxa10) levels in BMSCs were detected. Mouse BMSC OD process was induced, and calcium deposition and alkaline phosphatase content were analyzed, as well as expressions of runt-related transcription factor 2, osteopontin, and osteocalcin, and cell apoptosis. Bilateral ovaries were resected from mice to construct the ovariectomized model and bone mineral density, maximum bending stress, maximum load, and elastic modulus of the femur were tested, and the femur was histopathologically evaluated. Chondrocyte apoptosis in the articular cartilage of mice was analyzed. Analysis of the interaction of HAGLR, miR-182-5p with Hoxa10 was conducted.

RESULTS

HAGLR and Hoxa10 were down-regulated and miR-182-5p was elevated in PMOP patients. During the BMSC OD process, HAGLR and Hoxa10 levels were suppressed, while miR-182-5p was elevated. Promotion of HAGLR or suppression of miR-182-5p accelerated OD of BMSCs. Inhibition of miR-182-5p reversed the inhibitory effect of HAGLR on BMSC OD. In in vivo experiments, up-regulating HAGLR alleviated PMOP, while silencing Hoxa10 reversed the effects of upregulating HAGLR. HAGLR performed as a sponge for miR-182-5p, while miR-182-5p targeted Hoxa10.

CONCLUSION

In general, HAGLR boosted the OD process of BMSCs and relieved PMOP via the miR-182-5p/Hoxa10 axis. These data preliminarily reveal the key role of HAGLR in PMOP, and the research results have a certain reference for the treatment of PMOP.

Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis

BACKGROUND

Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge.

METHODS

PMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by "limma" packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes' function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers.

RESULTS

A total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC).

CONCLUSIONS

ROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target.

GATA4-activated lncRNA MALAT1 promotes osteogenic differentiation through inhibiting NEDD4-mediated RUNX1 degradation

Postmenopausal osteoporosis (PMOP) brings a lot of inconvenience to patients and serious economic burden to society. The osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) plays vital role in the process of PMOP treatment. However, the functional mechanism remains unclear. In this study, GATA4, MALAT1 and KHSRP were downregulated in bone tissues of PMOP patients, while NEDD4 was overexpressed. Through functional experiments, GATA4 overexpression strikingly accelerated osteogenic differentiation of BMSCs and promoted bone formation in vitro and in vivo, while these effects were dramatically reversed after MALAT1 silence. Intermolecular interaction experiments confirmed that GATA4 activated the transcription of MALAT1, which could form a 'RNA-protein' complex with KHSRP to decay NEDD4 mRNA. NEDD4 promoted the degradation of Runx1 by ubiquitination. Moreover, NEDD4 silencing blocked the inhibitory effects of MALAT1 knockdown on BMSCs osteogenic differentiation. In sum up, GATA4-activated MALAT1 promoted BMSCs osteogenic differentiation via regulating KHSPR/NEDD4 axis-regulated RUNX1 degradation, ultimately improving PMOP.