Potential Biomarkers Of Osteoarthritis Research Articles

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration

BackgroundOur study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.MethodsWe utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.ResultsWGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.ConclusionsHTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Serum Sirt6 as a Potential Biomarker for Osteoarthritis and its Correlation with IL-6 Alterations.

The study aimed to assess Sirt6 levels in serum of knee osteoarthritis (OA) patients compared to healthy individuals to evaluate its correlation with OA and to understand how Sirt6 is linked with the change in IL-6 levels. The cross-sectional study involved 50 knee OA patients clinically diagnosed as per the American College of Rheumatology guidelines and 50 healthy controls. Radiological examination as per Kellgren-Lawrence (KL) criteria was done to determine the disease severity. Peripheral blood samples were collected from each participant, and serum Sirt6 and IL-6 levels were measured using ELISA. The serum Sirt6 levels in knee OA patients were significantly lower as compared to healthy controls (p = 0.023). Patients with knee OA of KL grade 4 had significantly lower Sirt6 levels as compared to those with KL grade 2 OA (p = 0.031). Individuals of younger age group had higher Sirt6 levels compared to older age group. IL-6 levels in knee OA patients were significantly higher as compared to controls (p = 0.007). A negative correlation was observed between serum Sirt6 and IL-6 levels (r = -0.407; p = 0.035). The study concludes that serum Sirt6 levels are inversely associated with knee OA and may serve as a potential biomarker for the disease. Moreover, a negative correlation between Sirt6 and IL-6 levels was observed in this study. Further investigations are necessary to confirm these findings and to explore the mechanisms by which Sirt6 and IL-6 are involved in OA.

Degrasyn alleviates osteoarthritis by blocking macrophagic pyroptosis via suppressing NLRP3/GSDMD signaling pathway and protecting chondrocytes

Synovitis and cartilage destruction are crucial characteristics of osteoarthritis (OA). Inflammatory cytokines, such as IL-1β, are secreted by synovial macrophages, leading to cartilage destruction. Pyroptosis is a lytic form of programmed cell death, which could be triggered by the NLRP3 inflammasome of macrophages. Pyroptosis promotes the secretion of IL-1β and is supposed as a potential biomarker for OA. However, the function of Pyroptosis and NLRP3 inflammasome and its regulatory mechanism for activation is unclear in OA. In this study, we found that Degrasyn could alleviate the GSDMD-mediated pyroptosis of macrophages and the release of IL-1β, caspase-1, and LDH. Furthermore, it selectively impedes the form of ASC oligomer and speckle to effectively suppress the NLRP3 inflammasome during its assembly phase. Notably, Degrasyn exhibited potential chondroprotective effects in a co-culture system. Additionally, these results also indicate that Degrasyn mitigates synovitis and cartilage damage in a murine model of destabilization of the medial meniscus (DMM)-induced OA. In summary, Degrasyn emerges as a promising pharmaceutical agent for synovitis, paving the way for innovative therapeutic approaches to OA. Our findings underscore the potential of Degrasyn as a viable candidate for OA therapeutics, demonstrating its ability to regulate pyroptosis and NLRP3 inflammasome activation.

Metabolomics in Osteoarthritis Knee: A Systematic Review of Literature.

Osteoarthritis (OA) is a common degenerative disorder of the synovial joints and is usually an age-related disease that occurs due to continuous wear and tear of the cartilage in the joints. Presently, there is no proven medical management to halt the progression of the disease in the early stages. The purpose of our systematic review is to analyze the possible metabolites and metabolic pathways that are specifically involved in OA pathogenesis and early treatment of the disease. The articles were collected from PubMed, Cochrane, Google Scholar, Embase, and Scopus databases. "Knee", "Osteoarthritis", "Proteomics", "Lipidomics", "Metabolomics", "Metabolic Methods", and metabolic* were employed for finding the articles. Only original articles with human or animal OA models with healthy controls were included. From the initial screening, a total of 458 articles were identified from the 5 research databases. From these, 297 articles were selected in the end for screening, of which 53 papers were selected for full-text screening. Finally, 50 articles were taken for the review based on body fluid: 6 urine studies, 15 plasma studies, 16 synovial fluid studies, 11 serum studies, 4 joint tissue studies, and 1 fecal study. Many metabolites were found to be elevated in OA. Some of these metabolites can be used to stage the OA Three pathways that were found to be commonly involved are the TCA cycle, the glycolytic pathway, and the lipid metabolism. All these studies showed a vast array of metabolites and metabolic pathways associated with OA. Metabolites like lysophospholipids, phospholipids, arginine, BCCA, and histidine were identified as potential biomarkers of OA but a definite association was not identified, Three pathways (glycolytic pathway, TCA cycle, and lipid metabolic pathways) have been found as highly significant in OA pathogenesis. These metabolic pathways could provide novel therapeutic targets for the prevention and progression of the disease. The online version contains supplementary material available at 10.1007/s43465-024-01169-5.

MiR-29a-3p mediates phosphatase and tensin homolog and inhibits osteoarthritis progression.

Despite substantial progress in clinical trials of osteoarthritis (OA) gene therapy, the prevalence of OA is still on the rise. MiRNAs have a potential biomarker and therapeutic target for OA. OA cartilage and chondrosarcoma cells were studied to determine the role of miR-29a-3p and PTEN. OA cartilage and human chondrosarcoma cells (SW1353) were obtained. miR-29a-3p and PTEN signature expression was determined by RT-qPCR. The binding relationship between miR-29a-3p and PTEN was investigated by dual-luciferase reporter gene and western blot assay. TUNEL, immunohistochemistry, CCK-8, and flow cytometry were utilized to determine the proliferation and apoptosis of SW1353 cells. This study indicated downregulation of miR-29a-3p expression and upregulation of PTEN expression in human OA primary chondrocytes or OA tissue samples, compared with the normal cartilage cells or tissues. PTEN expression was negatively correlated with miR-29a-3p expression, and miR-29a-3p targeted PTEN mechanistically. miR-29a-3p reduced SW1353 cell activity and proliferation and promoted cell apoptosis. However, the aforementioned effects could be reversed by downregulating PTEN. miR-29a-3p can stimulate chondrocyte proliferation and inhibit apoptosis by inhibiting PTEN expression.

Identification and Verification of Hub Mitochondrial Dysfunction Genes in Osteoarthritis Based on Bioinformatics Analysis.

Age-related mitochondrial dysfunction and associated oxidative stress may contribute to the development of osteoarthritis. The aim of this study was to identify hub genes associated with mitochondrial dysfunction in osteoarthritis (OA) patients, helping predict the risk of OA, and revealing the mechanism of OA progression. OA expression data and mitochondrial dysfunction genes were downloaded from GEO (GSE55235, GSE82107, and GSE114007) and GeneCard databases. The differentially expressed mitochondrial dysfunction genes (DEMDFGs) between OA and control samples were screened. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathways were analyzed for DEMDFGs. The hub genes were determined by WGCNA and LASSO regression analysis. ROC curves manifested the diagnostic efficacy of each hub gene. A nomogram model was constructed and validated to predict OA risk. The expression of hub genes in OA and normal chondrocytes was verified by external datasets, qRT-PCR and western blotting. A total of 31 DEMDFGs were identified, with 15 genes upregulated and 16 genes downregulated. GO functional enrichment analysis revealed that DEMDFGs were enriched in biological processes related to energy metabolism and cellular respiration. By employing weighted gene coexpression network analysis, we identified four distinct coexpression modules, among which the blue module exhibited the strongest correlation with OA. The intersection between DEMDFGs and this module yielded eight candidate genes. After LASSO analysis of the data, four hub genes (ACADL, CYBA, SLC19A2, and UCP2) were identified as potential biomarkers for OA. The expression levels of these four genes were externally validated in the GSE114007 dataset. And the biologically differential expression of these four genes has been verified in OA and normal chondrocytes. Moreover, the four hub genes had good sensitivity and specificity by ROC curve analysis, and the risk model constructed with these four genes showed promising performance. In conclusion, our study may provide novel mitochondrial dysfunction hub genes with potential clinical applications for understanding the pathology, diagnosis, and treatment of OA.

Causal association of genetically determined plasma metabolites with osteoarthritis: a two-sample Mendelian randomization study.

We aimed to elucidate the causal relationship between plasma metabolites and the vulnerability to Osteoarthritis (OA), encompassing both hip OA and knee OA. We conducted a two-way two-sample Mendelian randomization (MR) analysis to investigate the association of 1,400 plasma metabolites with OA. The Inverse Variance Weighted (IVW) model served as the primary two-sample MR Analysis method, with supplementary analysis using the Weighted Median (WM) and MR Egger methods. To ensure the robustness of our findings, sensitivity analyses were performed, incorporating Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and Leave-One-Out analyses. To validate the identified metabolites, we utilized the Steiger test and linkage disequilibrium score regression. A total of 94 plasma metabolites were associated with osteoarthritis, with 60 associated with hip OA and 106 associated with knee OA. IVW analysis revealed that tryptophan levels showed the strongest positive association with hip OA (OR [95% CI]: 1.119 [1.024, 1.223]), while X-24757 levels exhibited the highest positive association with knee osteoarthritis (OR [95% CI]: 1.095 [1.032, 1.162]). Ethylparaben sulfate levels were found to have the greatest positive association with hip OA (OR [95% CI]: 1.118 [1.015, 1.231]). Notably, the plasma metabolite X-2475 showed a strong robust random effect across all three types of osteoarthritis. Metabolic pathway analysis revealed that the pathogenesis of osteoarthritis in the hip was mediated by acetylarginine, specifically in four important metabolic pathways: ethanol degradation (p = 0.044), amino sugar metabolism (p = 0.090), fatty acid biosynthesis (p = 0.095), and aspartate metabolism (p = 0.097816). There is a significant association between tryptophan levels and the risk of hip OA, as well as X-24757 levels and the risk of knee osteoarthritis. Additionally, X-24757 levels are also linked to the risk of hip OA. Moreover, this study has identified four crucial metabolic pathways in hip osteoarthritis, which are all regulated by acetylarginine. These findings provide valuable insights into potential biomarkers for OA and highlight potential pathways for its prevention and clinical intervention.

COL3A1, COL5A1 and COL6A2 serve as potential molecular biomarkers for osteoarthritis based on weighted gene co‑expression network analysis bioinformatics analysis.

Osteoarthritis (OA) is a non-inflammatory degenerative joint disease, characterized by joint pain and stiffness. The prevalence of OA increases with age. However, the relationship between biomarkers [collagen type III α1 (COL3A1), COL5A1, COL6A2, COL12A1] and OA remains unclear. The OA subchondral bone dataset GSE51588 was downloaded from the GEO database, and the differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis was performed, and a protein-protein interaction network was constructed and further analyzed using Cytoscape and STRING. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and then Gene Set Enrichment Analysis (GSEA) was used to formulate the molecular functions and pathways based on the results of GO and KEGG analyses. Comparative Toxicogenomics Database and TargetScan were used to identify the hub-gene-related diseases and the microRNAs that regulated the central hub genes. Immunohistochemical staining was performed to confirm the expression of related proteins in OA and non-OA tissue samples. A total of 1,679 DEGs were identified. GO analysis showed that the DEGs were primarily enriched in the process of 'immune system', 'extracellular region', 'secretory granule', 'collagen-containing extracellular matrix', 'ECM-receptor, glycosaminoglycan binding' and 'systemic lupus erythematosus'. The results of GSEA were similar to those of GO and KEGG enrichment terms for DEGs. A total of 25 important modules were generated, and two core gene clusters and seven core genes were obtained (COL6A2, COL5A2, COL12A1, COL5A1, COL6A1, LUM and COL3A1). Core genes were expressed differentially between OA subchondral bone and normal tissue samples. The expression levels of COL3A1, COL5A1 and COL6A2 in OA subchondral bone tissue were higher compared with those in normal tissues, but COL12A1 expression was not significantly increased; all stained markers were highly expressed in surrounding tissues of immunohistochemical staining. In conclusion, COL3A1, COL5A1 and COL6A2 may be potential molecular biomarkers for OA.

The Role of Matrix Metalloproteinase 13 and Vitamin D in Osteoarthritis: A Hospital-Based Observational Study.

Introduction Osteoarthritis (OA) is the most common form of degenerative joint disease characterized by the progressive degeneration of articular cartilage, osteophyte formation, and joint space narrowing. Matrix metalloproteinases (MMPs) are potential biomarkers for osteoarthritis. Aims and objective The study's aim is the estimation of serum and synovial fluid matrix metalloproteinase (MMP) 13 and serum vitamin D levels in the grade 3 and grade 4 stages of osteoarthritis according to the Kellgren and Lawrence (KL) system of classification. Materials and methods A total of 100 subjects were included; of them, 25 patients with grade 3 and 25 patients with grade 4 knee osteoarthritis diagnosed clinically and radiologically according to the Kellgren and Lawrence criteria have been enrolled in the study, and 50 patients with knee pain having a diagnosis other than degenerative OA of the knee were taken as controls. Venous blood and synovial fluid have been collected from all of them for the estimation of MMP-13 and vitamin D. The enzyme-linked immunosorbent assay (ELISA) and chemiluminescent microparticle immunoassay (CMIA) methods were used for the estimation of MMP-13 and vitamin D, respectively. Results The mean value of synovial fluid MMP-13 was found to be elevated in grade 4 as compared to grade 3 and the control group, whereas the mean value of serum MMP-13 was found to be elevated in grade 3 as compared to grade 4 and control. The level of serum vitamin D was found deficient in OA patients as compared to control. The Kruskal-Wallis test was performed to compare these groups, and there was a significant difference between these groups(p-value of <0.05). Summary and conclusion High synovial and serum MMP-13 is associated with knee structural abnormalities in patients with knee OA as compared to the control groupsuggesting that MMP-13 can be a biomarker in knee OA, whereas the decreased level of vitamin D may be associated with an increased risk for the progression of OA; hence, serum vitamin D may be a good indicator for the prediction of the initiation of OA.

SNORC knockdown alleviates inflammation, autophagy defect and matrix degradation of chondrocytes in osteoarthritis development.

Excessive inflammation and autophagy defect of chondrocytes play important roles in the pathological process of osteoarthritis (OA). The present study aimed to clarify the roles of small novel rich in cartilage (SNORC) in these pathological changes of chondrocytes in OA. Bioinformatics analysis of GEO dataset GSE207881 displayed that SNORC was a potential biomarker for OA. As confirmed by quantitative real-time PCR, immunohistochemical staining and western blotting, SNORC was significantly up-regulated in cartilage of OA rat model and interleukin (IL)-1β-stimulated primary rat articular chondrocytes in contrast to their corresponding normal control. Knocking down SNORC in IL-1β-induced chondrocytes obviously suppressed the production of nitric oxide (NO), IL-6, tumor necrosis factor (TNF)-α and prostaglandin E2 (PGE2) to alleviate inflammation, and reduced the protein levels of a disintegrin and metalloproteinase with thrombospondin 5 (ADAMTS5) and matrix metallopeptidase (MMP)13 and elevated collagen type 2 alpha 1 (COL2A1) level to improve matrix degradation. Down-regulation of SNORC increased Beclin1 expression and LC3II/LC3I ratio, but suppressed p62 expression to restore impaired autophagy in IL-1β-induced chondrocytes. Moreover, down-regulating SNORC mitigated mitochondrial dysfunction and apoptosis in IL-1β-stimulated chondrocytes. Mechanically, SNORC simultaneously activated the phosphatidylinositol-3-kinase/serine threonine kinase (PI3K/AKT) and c-Jun N-terminal kinase (JNK)/c-Jun signaling pathway in the IL-1β-induced chondrocyte, while re-activating the PI3K and JNK signals abolished the suppressive effect of down-regulating SNORC on IL-1β-induced chondrocyte damage. In a word, SNORC knockdown alleviates inflammation, matrix degradation, autophagy defect and excessive apoptosis of chondrocytes during OA development via suppressing the PI3K and JNK signaling pathway.

Identification of EGFR as an essential regulator in chondrocytes ferroptosis of osteoarthritis using bioinformatics, in vivo, and in vitro study

ObjectiveThe mechanisms of chondrocytes ferroptosis in osteoarthritis (OA) have not yet been fully elucidated. This study aimed to identify key ferroptosis related genes (FRGs) involved in chondrocytes ferroptosis. MethodsLASSO, SVM-RFE, and receiver operating characteristic curve (ROC) were performed to screen key differentially expressed FRGs (DEFRGs). Functional analyses were conducted using GO, and KEGG analyses. Unsupervised clustering analysis was used to identify ferroptosis related patterns. The CeRNA network was constructed to predict the upstream miRNAs and lncRNAs. Finally, we validated the role of EGFR in chondrocytes ferroptosis using in vivo and in vitro experiments. ResultsA total of 42 DEFRGs were identified between OA and normal cartilages. GO and KEGG analyses indicated that these DEFRGs were significantly engaged in ferroptosis related biological processes and pathways, such as cellular response to oxidative stress, positive regulation of programmed cell death, MAPK and PI3K-Akt signaling pathways. Moreover, four key DEFRGs, including ACSF2, AURKA, EGFR, and KLHL24, were considered as potential biomarkers of OA. Moreover, two distinct ferroptosis related patterns were determined, and a total of 882 differentially expressed genes were identified which might participate in extracellular matrix degradation and inflammatory response. In addition, the CeRNA network showed that EGFR could be competitively regulated by 3 lncRNAs and 4 miRNAs. Significantly, the expression of EGFR was downregulated in human OA cartilages, OA mouse model, and erastin induced chondrocytes. EGFR inhibition could induce the occurrence of chondrocytes ferroptosis and ECM degradation which could be reversed by the addition of Ferrostatin-1. ConclusionOur study has identified ACSF2, AURKA, EGFR, and KLHL24 as ferroptosis-related biomarkers in OA. Furthermore, we have conducted a preliminary investigation into the role of EGFR in regulating chondrocytes ferroptosis. These findings offer novel insights into the molecular mechanisms underlying OA.

Identification of polyunsaturated fatty acids as potential biomarkers of osteoarthritis after sodium hyaluronate and mesenchymal stem cell treatment through metabolomics.

Introduction: Osteoarthritis (OA) is a prevalent joint disorder worldwide. Sodium hyaluronate (SH) and mesenchymal stem cells (MSCs) are promising therapeutic strategies for OA. Previous studies showed they could improve knee function and clinical symptoms of OA. However, the mechanism of the therapeutic effects on the improvement of OA has not been clearly explained. Methods: In our study, we used a technique called 5-(diisopropylamino)amylamine derivatization liquid chromatography coupled with mass spectrometry to find the metabolites in OA synovial fluid under different treatments. Results and Discussion: After looking into the metabolomics, we discovered that SH and MSC treatment led to the downregulation of ω-6 polyunsaturated fatty acids (PUFAs) and the upregulation of ω-3 PUFAs. Significantly, the contents of 5(S)-HETE, PGA2, PGB2, and PGJ2 were lower in the MSC group than in the SH group after quantification using 5-(diisopropylamino)amylamine derivatization-UHPLC-QQQ-MS. This is the first report on the relationship of 11(S)-HETE, PGA2, PGB2, PGF2β, 11β-PGF2α, and DK-PGE2 with OA. Moreover, the correlation analysis of metabolites and inflammation factors showed the positive association of ω-6 PUFAs with pro-inflammation cytokines, and of ω-3 PUFAs with anti-inflammation cytokines. Our results indicated the therapeutic effect of SH and MSCs in patients with OA. In addition, this reliable metabolic approach could uncover novel biomarkers to treat OA.

Identification of Novel Diagnostic Biomarkers and Classification Patterns for Osteoarthritis by Analyzing a Specific Set of Genes Related to Inflammation.

Osteoarthritis (OA) is a prevalent joint disease globally. TNFA is recognized as a crucial inflammatory cytokine that plays a significant role in the pathophysiological mechanisms that occur during the progression of OA. However, the TNFA_SIGNALING_VIA_NFKB (TSVN)-related genes (TRGs) during the progression of OA remain unclear. By conducting a combinatory analysis of OA transcriptome data from three datasets, various differentially expressed TRGs were identified. The logistic regression model was used to mine hub TRGs for OA, and a nomogram prediction model was subsequently constructed using these TRGs. To identify new molecular subgroups, we performed consensus clustering. We then conducted functional analyses, including GO, KEGG, GSVA, and GSEA, to elucidate the underlying mechanisms. To determine the immune microenvironment, we applied xCell. The logistic regression analysis identified three hub TRGs (BHLHE40, BTG2, and CCNL1) as potential biomarkers for OA. Based on these TRGs, we constructed an OA predictive model. This model has demonstrated promising results in enhancing the accuracy of OA diagnosis, as evident from the ROC analysis (AUC merged dataset = 0.937, AUC validating dataset = 0.924). We identified two molecular subtypes, C1 and C2, and found that the C1 subtype showed activation of immune- and inflammation-related pathways. The involvement of TSVN in the development and progression of OA has been established. We identified several hub genes, such as BHLHE40, BTG2, and CCNL1, that may have a significant association with the progression of OA. Furthermore, our logistic regression model based on these genes has shown promising results in accurately diagnosing OA patients.

Identification of four-gene signature to diagnose osteoarthritis through bioinformatics and machine learning methods

BackgroundAlthough osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA. MethodsGene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients’ samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA). Results4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell. ConclusionA machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA.

Associations of Changes in Knee Hyaline Cartilage Composition Measured With Dual-Energy Computed Tomography in Gout, Aging and Osteoarthritis.

To characterize dual-energy computed tomography (DECT) changes depicting hyaline cartilage changes in gout patients with and without osteoarthritis (OA) and in comparators without gout. Patients with suspected crystal-associated arthropathy were enrolled and underwent bilateral DECT scans of the knees. Standardized regions of interest were defined in the femorotibial hyaline cartilage. Five DECT parameters were obtained: CT numbers in Hounsfield units (HU) at 80 and 140 kV, the electron density (Rho), the effective atomic number (Zeff), and the dual-energy index (DEI). Zones were compared between patients with gout, with and without knee OA, and between patients with gout and comparators without gout, after adjustment for confounders. A total of 113 patients with gout (mean age 63.5 ± 14.3 years) and 15 comparators without gout (mean age 75.8 ± 11.5 years) were included, n = 65 (51%) had knee OA, and 466 zones of hyaline cartilage were analyzed. Older age was associated with lower attenuations at 80 kV (P < 0.01) and 140 kV (P < 0.01), and with Rho (P < 0.01). OA was characterized by lower attenuation at 140 kV (P = 0.03), but the lower Rho was nonsignificant after adjustment for confounders. In gout, hyaline cartilage exhibited lower Rho values (adjusted P = 0.04). Multivariable coefficients of association with Rho were -0.21 [-0.38;-0.04] (P = 0.014) for age, -4.15 [-9.0;0.7] (P = 0.093) for OA and 0.73 [-0.1;1.56] (P = 0.085) for monosodium urate volume. Gout was associated with DECT-detected changes in cartilage composition, similar to those observed in older patients, with some similarities and some differences to those seen in OA. These results suggest the possibility of potential DECT biomarkers of OA.

MicroRNA expression in osteoarthritis: a meta-analysis.

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases globally, leading to chronic disability and poor prognosis. One of the approaches for optimizing OA treatment is to find early effective diagnostic biomarkers. The contribution of microRNAs (miRNAs) in OA progression is now being increasingly recognized. This review provides a comprehensive summary on studies reporting the expression profiling of miRNAs in OA and associated signaling pathways. We performed a systematic search of the Embase, Web of Science, PubMed, and Cochrane library databases. This systematic review is reported according to the PRISMA checklist. Studies which identified miRNAs with aberrant expression compared to controls during OA progression were included, and a meta-analysis was performed. Results from the random effects model were provided as log10 odds ratios (logORs) and 95% confidence intervals. Sensitivity analysis was conducted to confirm the accuracy of the results. Subgroup analysis was conducted based on tissue source. The target genes of miRNAs identified in this study were extracted from the MiRWalk database, and these target genes were enriched in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. A total of 191 studies reporting 162 miRNAs were included in our meta-analysis. Among them, 36 miRNAs distributed across 96 studies were expressed in the same direction in at least two studies (13 up-regulated and 23 down-regulated). Subgroup analysis of tissue source revealed that the highest number of studies was conducted using articular cartilage, where the most up-regulated miRNAs were miR-146a-5p (logOR 7.355; P < 0.001) and miR-34a-5p (logOR 6.955; P < 0.001), and the most down-regulated miRNAs were miR-127-5p (logOR 6.586; P < 0.001) and miR-140-5p (logOR 6.373; P < 0.001). Enrichment analysis of 752 downstream target genes of all identified miRNAs was performed, and the regulatory relationships among them were displayed. Mesenchymal stem cells and transforming growth factor-β were found to be the most important downstream effectors regulated by miRNA in OA. This study highlighted the importance of miRNA signaling in OA progression and identified a number of prominent miRNAs including miR-146a-5p, miR-34a-5p, miR-127-5p, and miR-140-5p which might be considered as potential biomarkers for OA.

Identification of Key Diagnostic Markers and Immune Infiltration in Osteoarthritis.

Osteoarthritis (OA) is a worldwide chronic disease of the articulating joints. An increasing body of data demonstrates the immune system's involvement in osteoarthritis. The molecular mechanisms of OA are still unclear. This study aimed to search for OA immunerelated hub genes and determine appropriate diagnostic markers to help the detection and treatment of the disease. Gene expression data were downloaded from the GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) using R packages. Meanwhile, ssGSEA was used to determine the activation degree of immune-related genes (IRGs), and WGCNA analysis was applied to search for co-expressed gene modules associated with immune cells. Then, critical networks and hub genes were found in the PPI network. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analyzed the biological functions of genes. The ability of the hub genes to differentiate OA from controls was assessed by the area under the ROC curve. A miRNA and transcription factor (TF) regulatory network was constructed according to their relationship with hub genes. Finally, the validation of hub genes was carried out by qPCR. In total, 353 DEGs were identified in OA patients compared with controls, including 222 upregulated and 131 downregulated genes. WGCNA successfully identified 34 main functional modules involved in the pathogenesis of OA. The most crucial functional module involved in OA included 89 genes. 19 immune-related genes were obtained by overlapping DEGs with the darkgrey module. The String database was constructed using the protein-protein interaction (PPI) network of 19 target genes, and 7 hub genes were identified by MCODE. ROC curve showed that 7 hub genes were potential biomarkers of OA. The expression levels of hub genes were validated by qPCR, and the results were consistent with those from bioinformatic analyses. Immune-related hub genes, including TYROBP, ITGAM, ITGB2, C1QC, MARCO, C1QB, and TLR8, may play critical roles in OA development. ITGAM had the highest correction on immune cells.

Bioinformatics Prediction and Experimental Validation Identify a Novel Cuproptosis-Related Gene Signature in Human Synovial Inflammation during Osteoarthritis Progression.

Osteoarthritis (OA) is the one of most common joint diseases worldwide. Cuproptosis, which had been discovered lately, is a novel form of cell death induced by copper. Our purpose is to study the relationship between cuproptosis-related genes (CRGs) and inflammatory microenvironments in patients with OA and identify characteristic cuproptosis-related biomarkers. First, the combinatory analysis of OA transcriptome data from five datasets identified differentially expressed CRGs associated with OA. Then, we applied single-sample gene set enrichment analysis (ssGSEA) to evaluate immune-cell infiltration and immune-function levels in OA patients and normal controls, respectively. Hub CRGs for OA were mined based on the random forest (RF) model, and a nomogram prediction model was constructed based on them. In total, four differentially expressed CRGs were identified through bioinformatics analysis and confirmed by RT-qPCR. FDX1 and LIPT1 were expressed at a high level in OA, while DBT and DLST were expressed higher in the normal group. In total, 10 CRGs were found to be significantly correlated with immune landscape. Four hub CRGs were subsequently obtained by the RF analysis as potential biomarkers for OA. We constructed an OA predictive model based on these four CRGs (DBT, DLST, FDX1, and LIPT1).

CircZSWIM6 mediates dysregulation of ECM and energy homeostasis in ageing chondrocytes through RPS14 post-translational modification.

Circular RNAs (CircRNAs) are important and have different roles in disease progression. Herein, we aim to elucidate the roles of a novel CircRNA (CircZSWIM6) which is upregulated in ageing chondrocytes. We verified the roles of CircZSWIM6 in senescent and osteoarthritis (OA) development in vitro through CircZSWIM6 knockdown and overexpression. RNA pulldown assay and RNA binding protein immunoprecipitation were performed to identify the interaction between CircZSWIM6 and Ribosomal protein S14 (RPS14). The roles of CircZSWIM6 in ageing-related OA were also confirmed in non-traumatic and traumatic model respectively. CircZSWIM6 regulates extracellular matrix (ECM) and energy metabolism in ageing chondrocyte. Mechanistically, CircZSWIM6 competitively bound to the E3 ligase STUB1 binding site on RPS14 (K125) to inhibit proteasomal degradation of RPS14 to maintain RPS14 function. CircZSWIM6-RPS14 axis is highly associated with AMPK signaling transduction, which keeps energy metabolism in chondrocyte. Furthermore, CircZSWIM6 AAV infection leads to senescent and OA phenotypes in a non-traumatic model and accelerates OA progression in a traumatic model. Our results revealed a significant role of CircZSWIM6 in age-related OA by regulating ECM metabolism and AMPK-associated energy metabolism. We highlight the CircZSWIM6-RPS14-PCK1-AMPK axis is a potential biomarker for OA.

Integrated Analysis of miRNAs and Gene Expression Profiles Reveals Potential Biomarkers for Osteoarthritis.

Purpose: Currently, the early diagnosis and treatment of osteoarthritis (OA) remain a challenge. In the present study, we attempted to explore potential biomarkers for the diagnosis and treatment of OA. Methods: The differentially expressed genes (DEGs) were identified based on three mRNA datasets of synovial tissues for OA patients and normal controls downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used for evaluating gene function related categories. Then, miRNA sequencing was performed for differentially expressed miRNAs’ identification. Finally, weighted gene co-expression network analysis (WGCNA) was performed for genes detected by the three mRNA datasets and a competing endogenous RNA (ceRNA) network with DEGs and differentially expressed microRNAs (miRNAs) was constructed for central genes identification. In addition, the relationship between central gene expression and immune infiltration was analyzed, and the candidate agents for OA were predicted based on the Connectivity Map database. Quantitative RT-PCR (qRT-PCR), Western blotting analysis, and immunofluorescent staining were performed to validate the expression levels of differentially expressed miRNAs and differentially expressed target genes in normal and OA tissues and chondrocytes. MiRNA–mRNA network was also validated in chondrocytes in vitro. Results: A total of 259 DEGs and 26 differentially expressed miRNAs were identified, among which 94 miRNA–mRNA interactions were predicted. The brown module in WGCNA was most closely correlated with the clinical traits of OA. After overlapping the brown module genes with miRNA–mRNA pairs, 27 miRNA–mRNA pairs were obtained. A ceRNA network was constructed with 5505 lncRNA–miRNA–mRNA interactions. B-cell translocation gene 2(BTG2), Abelson-related gene (ABL2), and vascular endothelial growth factor A (VEGFA) were identified to be the central genes with good predictive performance, which were significantly correlated with immune cell infiltration in OA, reflected by declined activated dendritic cells (aDCs), and elevated contents of B cells, macrophages, neutrophils, and T helper cells. Anisomycin, MG-132, thapsigargin, and lycorine were predicted to be the potential candidate agents for OA intervention. In vitro, the expression levels of differentially expressed miRNAs and biomarkers identified in the present study were consistent with the results obtained in normal or OA knee cartilage tissues and chondrocytes. Furthermore, BTG2 was identified to be negatively regulated by miR-125a-5p. Conclusion: BTG2, ABL2, and VEGFA can be regarded as potential predictive and treatment biomarkers for OA, which might guide the clinical therapy of OA.