Titanium dioxide (TiO₂) nanotubes can improve the osseointegration of pure titanium implants, but this exact mechanism has not been fully elucidated. The purinergic receptor P2Y6 is expressed in bone marrow mesenchymal stem cells (BMSCs) and participates in the regulation of bone metabolism. However, it is unclear as to whether P2Y6 is involved in the osteogenic differentiation of BMSCs induced by TiO₂ nanotubes. TiO₂ nanotubes were prepared on the surface of titanium specimens using the anodizing method and characterized their features. Quantitative reverse transcriptase polymerase chain reaction and western blotting were used to detect the expression of P2Y6, markers of osteogenic differentiation, and PKCα-ERK1/2. A rat femoral defect model was established to evaluate the osseointegration effect of TiO₂ nanotubes combined with P2Y6 agonists. The results showed that the average inner diameter of the TiO₂ nanotubes increased with an increase in voltage (voltage range of 30-90V), and the expression of P2Y6 in BMSCs could be upregulated by TiO₂ nanotubes in osteogenic culture. Inhibition of P2Y6 expression partially inhibited the osteogenic effect of TiO₂ nanotubes and downregulated the activity of the PKCα-ERK1/2 pathway. When using in vitro and in vivo experiments, the osteogenic effect of TiO₂ nanotubes when combined with P2Y6 agonists was more pronounced. TiO₂ nanotubes promoted the P2Y6 expression of BMSCs during osteogenic differentiation and promoted osteogenesis by activating the PKCα-ERK1/2 pathway. The combined application of TiO₂ nanotubes and P2Y6 agonists may be an effective new strategy to improve the osseointegration of titanium implants. STATEMENT OF SIGNIFICANCE: Titanium dioxide (TiO₂) nanotubes can improve the osseointegration of pure titanium implants, but this exact mechanism has not been fully elucidated. The purinergic receptor P2Y6 is expressed in bone marrow mesenchymal stem cells (BMSCs) and participates in the regulation of bone metabolism. However, it is unclear as to whether P2Y6 is involved in the osteogenic differentiation of BMSCs induced by TiO₂ nanotubes. For the first time, this study revealed the relationship between TiO₂ nanotubes and purine receptor P2Y6, and further explored its mode of action, which may provide clues as to the regulatory role of TiO₂ nanotubes on osteogenic differentiation of BMSCs. These findings will help to develop novel methods for guiding material design and biosafety evaluation of nano implants.

Knee osteoarthritis (KOA) is a prevalent degenerative joint disorder, yet its underlying molecular mechanisms remain puzzling. This study aimed to uncover the genes with a causal relationship to KOA using Mendelian randomization (MR), transcriptomic profiling, and machine learning methods. MR analysis was conducted utilizing expression quantitative trait loci (eQTL) data from the eQTLGen consortium alongside KOA-related GWAS summary statistics to identify candidate genes. Subsequently, differential expression analysis and WGCNA were applied to synovial tissue microarray datasets obtained from the GEO database. The intersecting genes were further refined using three machine learning algorithms: LASSO, random forest, and SVM–RFE. Diagnostic efficacy was assessed via ROC curve analysis and nomogram construction. Validation was ultimately performed using qPCR on clinical synovial tissue samples. Twelve genes with putative causal associations to KOA were identified, with MEG3 and MAPK3 emerging as the most diagnostically robust. Both exhibited high sensitivity and specificity in ROC analysis, and their differential expression was corroborated by qPCR. This study underscores the diagnostic utility of MEG3 and MAPK3 in KOA and offers a promising molecular framework for early disease detection. Nonetheless, validation in larger, independent cohorts and further mechanistic investigations are warranted to substantiate these findings.

Background: Osteosarcoma (OS) stands as the prevailing form of primary bone cancer in clinical practice. Lack of effective treatment options and an overall poor prognosis are caused by the disease’s exceptionally rare occurrence and unclear rationale. Objective: This study’s goal is to determine diagnostic marker genes involved in the progression of OS and investigate related pathways and mechanisms with the purpose of offering effective methods for OS diagnostics and therapy. Methods: The Gene Expression Omnibus database provided the gene microarray data. Core genes were identified through differential expression analysis and WGCNA. Three techniques for machine learning, random forest, least absolute shrinkage and selection operator regression, and support vector machine recursive feature elimination, were used to further screen the core genes and obtain diagnostic marker genes for OS. The specificity and sensitivity of the diagnostic marker genes for OS diagnosis were evaluated using receiver operating characteristic curves. Western blotting analysis was used for preliminary validation of the diagnostic marker genes and their related pathways. Results: Two diagnostic marker genes were identified through screening, including CEP55 and VWF. Receiver operating characteristic curves have been utilized to assess the diagnostic and therapeutic effects of CEP55 and VWF on OS. Western blotting analysis preliminarily validated the overexpression of CEP55 in OS and its capacity to control MMP2 and MMP9 levels by activating the JAK2/STAT3 signaling pathway. Conclusion: At the first time, this research shows that CEP55 and VWF are more powerful diagnostic and predictive indicators for OS. CEP55 holds the capacity to activate the JAK2/STAT3 signaling pathway and modulate MMP2 and MMP9 levels, thereby positioning it as a promising target in OS treatment.

Background: Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints.Objective: The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways.Methods: Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways.Results: Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-β1-activated Smad3/4 signaling pathway.Conclusion: The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-β1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

Background: Osteoarthritis (OA) is the most prevalent and commonly chronic joint disease that frequently develops among the elderly population. It is not just a single tissue that is affected, but rather a pathology involving the entire joint. Among them, synovitis is a key pathological change in OA. Ferroptosis is a newly discovered form of cell death that results from the buildup of lipid peroxidation. However, the role and impact of it in OA are yet to be explored. Objective: The key to this work is to uncover the mechanisms of ferroptosis-related OA pathogenesis and develop more novel diagnostic biomarkers to facilitate the diagnostic and therapeutic of OA. Materials and methods: Download ferroptosis-related genes and OA synovial chip datasets separately from the FerrDB and Gene Expression Omnibus databases. Identify ferroptosis differentially expressed genes using R software, obtain the intersection genes through two machine learning algorithms, and obtain diagnostic biomarkers after logistic regression analysis. Verify the diagnostic and therapeutic efficacy of specific genes for OA through the construction of clinical risk prognostic models using ROC curves and nomogram. Simultaneously, correlations between specific genes and OA immune cell infiltration co-expression were constructed. Finally, verify the differential presentation of specific genes in OA and health control synovium. Results: Obtain 38 ferroptosis differentially expressed genes through screening. Based on machine learning algorithms and logistic regression analysis, select AGPS, BRD4, RBMS1, and EGR1 as diagnostic biomarker genes. The diagnostic and therapeutic efficacy of the four specific genes for OA has been validated by ROC curves and nomogram of clinical risk prognostic models. The analysis of immune cell infiltration and correlation suggests a close association between specific genes and OA immune cell infiltration. Further revealing the diagnostic value of specific genes for OA by the differential presentation analysis of their differential presentation in synovial tissue from OA and health control. Conclusion: This study identified four diagnostic biomarkers for OA that are associated with iron death. The establishment of a risk-prognostic model is conducive to the premature diagnosis of OA, evaluating functional recovery during rehabilitation, and guidance for subsequent treatment.

Background Osteoarthritis (OA) is the most prevalent and commonly chronic joint disease that frequently develops among the elderly population. It is not just a single tissue that is affected, but rather a pathology involving the entire joint. Among them, synovitis is a key pathological change in OA. Ferroptosis is a newly discovered form of cell death that results from the buildup of lipid peroxidation. However, the role and impact of it in OA are yet to be explored. Objective The key to this work is to uncover the mechanisms of ferroptosis-related OA pathogenesis and develop more novel diagnostic biomarkers to facilitate the diagnostic and therapeutic of OA. Materials and Methods Download FRGs and OA synovial chip datasets separately from the FerrDB and GEO databases. Identify FDEGs using R software, obtain the intersection genes through two machine learning algorithms, and obtain diagnostic biomarkers after logistic regression analysis. Verify the diagnostic and therapeutic efficacy of specific genes for OA through the construction of clinical risk prognostic models using ROC curves and nomogram. Simultaneously, correlations between specific genes and OA immune cell infiltration co-expression were constructed. Finally, verify the differential presentation of specific genes in OA and HC synovium. Results Obtain 38 FDEGs through screening. Based on machine learning algorithms and logistic regression analysis, select AGPS, BRD4, RBMS1, and EGR1 as diagnostic biomarker genes. The diagnostic and therapeutic efficacy of the four specific genes for OA has been validated by ROC curves and nomogram of clinical risk prognostic models. The analysis of immune cell infiltration and correlation suggests a close association between specific genes and OA immune cell infiltration. Further revealing the diagnostic value of specific genes for OA by the differential presentation analysis of their differential presentation in synovial tissue from OA and HC. Conclusion This study identified four diagnostic biomarkers for OA that are associated with iron death. The establishment of a risk-prognostic model is conducive to the premature diagnosis of OA, evaluating functional recovery during rehabilitation, and guidance for subsequent treatment.