Identification of intra-group, inter-individual, and gene-specific variances in mRNA expression profiles in the rheumatoid arthritis synovial membrane

Identification the role of necroptosis in rheumatoid arthritis by WGCNA network

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. These 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. The close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease.

Mitochondria-Related Gene MAOB is a Key Biomarker of Osteoarthritis and Inhibition of Its Expression Reduces LPS-induced Chondrocyte Damage

The mitochondria are an important organelle in cells responsible for producing energy, and its abnormal function is closely related to the occurrence and development of osteoarthritis. Finding key genes associated with mitochondrial dysfunction in osteoarthritis can provide new ideas for the study of its pathogenesis. Firstly, 371 differential expressed genes (DEGs) were obtained through bioinformatics analysis of the GSE12021 and GSE55235 datasets in the GEO database, and 24 mitochondria-related DEGs (Mito-DEGs) were obtained by crossing differential genes with mitochondrial related genes. Next, KEGG and GO analysis of Mito-DEGs showed that upregulated Mito-DEGs were mainly enriched in small molecule catabolic process and tryptophan metabolism, while downregulated Mito-DEGs were mainly enriched in acetyl-CoA metabolic process and fatty acid biosynthesis. Furthermore, the key genes ME2 and MAOB were obtained through protein-protein interaction network analysis and lasso cox analysis of the 24 Mito-DEGs. In addition, the comparison results of immune cell scores showed differences between T cells CD4 memory resting, T cells regulatory (Tregs), Mast cells resting, and Mast cells activated in the OA group and the control group. More importantly, the potential regulatory mechanisms of key genes were studied through GSEA analysis and their correlation with immune infiltrating cells, immune checkpoints, m6A, and ferroptosis. Finally, in LPS-induced C28/I2 cells, silencing MAOB reduced inflammation injury and inhibited mitochondrial damage. Our research findings suggest that MAOB may hold potential as a target for the diagnosis and treatment of osteoarthritis.

Identification and validation of immune-related genes in osteoarthritic synovial fibroblasts

Objective

OA was generally considered as a non-inflammatory disease dominated by articular cartilage degeneration. However, the role of synovitis in OA pathogenesis has received increasing attention. Recent studies support that OA patients have a pro-inflammatory/catabolic synovial environment similar to RA patients, promoting the occurrence and development of OA. Therefore, we investigated the co-immune-related genes and pathways of OA and RA to explore whether part of the pathogenesis of RA synovitis can be used to explain OA synovitis.

Methods

Data of GSE29746 and GSE12021 were downloaded from the Gene Expression Omnibus (GEO) database. Compared with control group, differentially expressed genes (DEGs) of OA and RA groups were screened separately by R software, Venny website was used to screen co-DEGs. Metascape was used to screen the common enriched terms and pathways between OA and RA. STRING website and Cytoscape software were used to map protein-protein interaction (PPI) networks and screen co-hub genes. GSE29746 was selected as the test dataset, and GSE12021 as the validation dataset for validate the co-hub genes. The results were validated by western blotting (WB) and real-time quantitative polymerase chain reaction (qPCR) of clinical synovial samples.

Results

We identified 573 OA-related DEGs, 148 RA-related DEGs, and 52 co-DEGs, revealing 14 common enriched terms, most of which were related to immune inflammation. IL7R was the only upregulated co-hub gene between OA and RA in the PPI network, consistent with the validation dataset. IL7R was highly expressed in clinical osteoarthritic synovial samples (P < 0.001).

Conclusion

Our findings suggested that IL7R is a critical co-DEG in OA and RA and confirmed the involvement of immune inflammation in disease pathogenesis. Furthermore, it confirms the role of IL7R in synovial inflammation in RA and OA synovitis and provides evidence for further investigation of OA immune inflammation.

Dysregulated fibrinolysis and plasmin activation promote the pathogenesis of osteoarthritis

Joint injury is associated with risk for development of osteoarthritis (OA). Increasing evidence suggests that activation of fibrinolysis is involved in OA pathogenesis. However, the role of the fibrinolytic pathway is not well understood. Here, we showed that the fibrinolytic pathway, which includes plasminogen/plasmin, tissue plasminogen activator, urokinase plasminogen activator (uPA), and the uPA receptor (uPAR), was dysregulated in human OA joints. Pharmacological inhibition of plasmin attenuated OA progression after a destabilization of the medial meniscus in a mouse model whereas genetic deficiency of plasmin activator inhibitor, or injection of plasmin, exacerbated OA. We detected increased uptake of uPA/uPAR in mouse OA joints by microPET/CT imaging. In vitro studies identified that plasmin promotes OA development through multiple mechanisms, including the degradation of lubricin and cartilage proteoglycans and induction of inflammatory and degradative mediators. We showed that uPA and uPAR produced inflammatory and degradative mediators by activating the PI3K, 3'-phosphoinositide-dependent kinase-1, AKT, and ERK signaling cascades and activated matrix metalloproteinases to degrade proteoglycan. Together, we demonstrated that fibrinolysis contributes to the development of OA through multiple mechanisms and suggested that therapeutic targeting of the fibrinolysis pathway can prevent or slow development of OA.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study

Background

Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA.

Methods

We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed.

Results

Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes.

Conclusion

CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

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.

Single-cell sequencing and transcriptome analyses in the construction of a liquid-liquid phase separation-associated gene model for rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a disabling autoimmune disease that affects multiple joints. Accumulating evidence suggests that imbalances in liquid-liquid phase separation (LLPS) can lead to altered spatiotemporal coordination of biomolecular condensates, which play important roles in carcinogenesis and inflammatory diseases. However, the role of LLPS in the development and progression of RA remains unclear. Methods: We screened RA and normal samples from GSE12021, GSE55235, and GSE55457 transcriptome datasets and GSE129087 and GSE109449 single-cell sequencing datasets from Gene Expression Omnibus database to investigate the pathogenesis of LLPS-related hub genes at the transcriptome and single cell sequencing levels. Machine learning algorithms and weighted gene co-expression network analysis were applied to screen hub genes, and hub genes were validated using correlation studies. Results: Differential analysis showed that 36 LLPS-related genes were significantly differentially expressed in RA, further random forest and support vector machine identified four and six LLPS-related genes, respectively, and weighted gene co-expression network analysis identified 396 modular genes. Hybridization of the three sets revealed two hub genes, MYC and MAP1LC3B, with AUCs of 0.907 and 0.911, respectively. Further ROC analysis of the hub genes in the GSE55457 dataset showed that the AUCs of MYC and MAP1LC3B were 0.815 and 0.785, respectively. qRT-PCR showed that the expression of MYC and MAP1LC3B in RA synovial tissues was significantly lower than that in the normal control synovial tissues. Correlation analysis between hub genes and the immune microenvironment and single-cell sequencing analysis revealed that both MYC and MAP1LC3B were significantly correlated with the degree of infiltration of various innate and acquired immune cells. Conclusion: Our study reveals a possible mechanism for LLPS in RA pathogenesis and suggests that MYC and MAP1LC3B may be potential novel molecular markers for RA with immunological significance.

Integrated single-cell and bulk RNA sequencing analysis identified pyroptosis-related signature for diagnosis and prognosis in osteoarthritis

Osteoarthritis (OA), a degenerative disease of the joints, has one of the highest disability rates worldwide. This study investigates the role of pyroptosis-related genes in osteoarthritis and their expression in different chondrocyte subtypes at the individual cell level. Using OA-related datasets for single-cell RNA sequencing and RNA-seq, the study identified PRDEGs and DEGs and conducted Cox regression analysis to identify independent prognostic factors for OA. CASP6, NOD1, and PYCARD were found to be prognostic factors. Combined Weighted Gene Correlation Network Analysis with PPI network, a total of 15 hub genes related to pyroptosis were involved in the notch and oxidative phosphorylation pathways, which could serve as biomarkers for the diagnosis and prognosis of OA patients. The study also explored the heterogeneity of chondrocytes between OA and normal samples, identifying 19 single-cell subpopulation marker genes that were significantly different among 7 chondrocyte cell clusters. AGT, CTSD, CYBC, and THYS1 were expressed differentially among different cell subpopulations, which were associated with cartilage development and metabolism. These findings provide valuable insights into the molecular mechanisms underlying OA and could facilitate the development of new therapeutic strategies for this debilitating disease.

Consensus cluster analysis of apoptosis-related genes in patients with osteoarthritis and their correlation with immune cell infiltration

Background

Osteoarthritis (OA) progression involves multiple factors, including cartilage erosion as the basic pathological mechanism of degeneration, and is closely related to chondrocyte apoptosis. To analyze the correlation between apoptosis and OA development, we selected apoptosis genes from the differentially expressed genes (DEGs) between OA and normal samples from the Gene Expression Omnibus (GEO) database, used lasso regression analysis to identify characteristic genes, and performed consensus cluster analysis to further explore the pathogenesis of this disease.

Methods

The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed.

Results

A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively.

Conclusion

ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.

A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis

Introduction

Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis.

Methods

A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx.

Results

A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein-protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results.

Discussion

In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.

Bioinformatics identification of ferroptosis-related genes and therapeutic drugs in rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is a chronic immune disease characterized by synovial inflammation and bone destruction, with a largely unclear etiology. Evidence has indicated that ferroptosis may play an increasingly important role in the onset and development of RA. However, ferroptosis-related genes are still largely unexplored in RA. Therefore, this work focused on identifying and validating the potential ferroptosis-related genes involved in RA through bioinformatics analysis.

Methods

We screened differentially expressed ferroptosis-related genes (DEFGs) between RA patients and healthy individuals based on GSE55235 dataset. Subsequently, correlation analysis, protein-protein interaction (PPI) network analysis, GO, and KEGG enrichment analyses were performed using these DEFGs. Finally, our results were validated by GSE12021 dataset.

Results

We discovered 34 potential DEFGs in RA based on bioinformatics analysis. According to functional enrichment analysis, these genes were mainly enriched in HIF-1 signaling pathway, FoxO signaling pathway, and Ferroptosis pathway. Four genes (GABARPL1, DUSP1, JUN, and MAPK8) were validated to be downregulated by GSE12021 dataset and were diagnostic biomarkers and therapeutic targets for RA via the regulation of ferroptosis.

Discussion

Our results help shed more light on the pathogenesis of RA. Ferroptosis-related genes in RA are valuable diagnostic biomarkers and they will be exploited clinically as therapeutic targets in the future.

Exosomal hsa-miR-335-5p and hsa-miR-483-5p are novel biomarkers for rheumatoid arthritis: A development and validation study

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes cartilage and bone damage. Exosomes are small extracellular vesicles that play a critical role in intercellular communication and various biological processes by serving as vehicles for the transfer of diverse molecules, such as nucleic acids, proteins, and lipids, between cells. The purpose of this study was to develop potential biomarkers for RA in peripheral blood by performing small non-coding RNA (sncRNA) sequencing using circulating exosomes from healthy controls and patients with RA.

METHODS

In this study, we investigated extracellular sncRNAs associated with RA in peripheral blood. Using RNA sequencing and differentially expressed sncRNA analysis, we identified a miRNA signature and target genes. Target gene expression was validated via the four GEO datasets.

RESULTS

Exosomal RNAs were successfully isolated from the peripheral blood of 13 patients with RA and 10 healthy controls. The hsa-miR-335-5p and hsa-miR-486-5p expression levels were higher in patients with RA than in controls. We identified the SRSF4 gene, which is a common target of hsa-miR-335-5p and hsa-miR-483-5p. As expected, the expression of this gene was found to be decreased in the synovial tissues of patients with RA through external validation. In addition, hsa-miR-335-5p was positively correlated with antiCCP, DAS28ESR, DAS28CRP, and rheumatoid factor.

CONCLUSIONS

Our results provide strong evidence that circulating exosomal miRNA (hsa-miR-335-5p and hsa-miR-486-5p) and SRSF4 could be valuable biomarkers for RA.

WDR43 is a potential diagnostic biomarker and therapeutic target for osteoarthritis complicated with Parkinson’s disease

Osteoarthritis (OA) and Parkinson’s disease (PD) are on the rise and greatly impact the quality of individuals’ lives. Although accumulating evidence indicates a relationship between OA and PD, the particular interactions connecting the two diseases have not been thoroughly examined. Therefore, this study explored the association through genetic characterization and functional enrichment. Four datasets (GSE55235, GSE12021, GSE7621, and GSE42966) were chosen for assessment and validation from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was implemented to determine the most relevant genes for clinical features. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to explore the biological processes of common genes, and to display the interrelationships between common genes, the STRING database and the application Molecular Complex Detection Algorithm (MCODE) of Cytoscape software were leveraged to get hub genes. By intersecting the common genes with the differentially expressed genes (DEGs) acquired from GSE12021 and GSE42966, the hub genes were identified. Finally, we validated the diagnostic efficacy of hub genes and explored their correlation with 22 immune infiltrating cells. As a consequence, we discovered 71 common genes, most of which were functionally enriched in antigen processing and presentation, mitochondrial translation, the mRNA surveillance pathway, and nucleocytoplasmic transport. Furthermore, WDR43 was found by intersecting eight hub genes with 28 DEGs from the two validation datasets. Receiver Operating Characteristic (ROC) implied the diagnostic role of WDR43 in OA and PD. Immune infiltration research revealed that T-cell regulatory (Tregs), monocytes, and mast cells resting were associated with the pathogenesis of OA and PD. WDR43 may provide key insights into the relationship between OA and PD.

Identification and Validation of Hub Genes for Predicting Treatment Targets and Immune Landscape in Rheumatoid Arthritis

Background

Rheumatoid arthritis (RA) is recognized as a chronic inflammatory disease featured by pathological synovial inflammation. Currently, the underlying pathophysiological mechanisms of RA remain unclear. In the study, we attempted to explore the underlying mechanisms of RA and provide potential targets for the therapy of RA via bioinformatics analysis.

Methods

We downloaded four microarray datasets (GSE77298, GSE55235, GSE12021, and GSE55457) from the GEO database. Firstly, GSE77298 and GSE55457 were identified DEGs by the “limma” and “sva” packages of R software. Then, we performed GO, KEGG, and GSEA enrichment analyses to further analyze the function of DEGs. Hub genes were screened using LASSO analysis and SVM-RFE analysis. To further explore the differences of the expression of hub genes in healthy control and RA patient synovial tissues, we calculated the ROC curves and AUC. The expression levels of hub genes were verified in synovial tissues of normal and RA rats by qRT-PCR and western blot. Furthermore, the CIBERSORTx was implemented to assess the differences of infiltration in 22 immune cells between normal and RA synovial tissues. We explored the association between hub genes and infiltrating immune cells.

Results

CRTAM, CXCL13, and LRRC15 were identified as RA's potential hub genes by machine learning and LASSO algorithms. In addition, we verified the expression levels of three hub genes in the synovial tissue of normal and RA rats by PCR and western blot. Moreover, immune cell infiltration analysis showed that plasma cells, T follicular helper cells, M0 macrophages, M1 macrophages, and gamma delta T cells may be engaged in the development and progression of RA.

Conclusions

In brief, our study identified and validated that three hub genes CRTAM, CXCL13, and LRRC15 might involve in the pathological development of RA, which could provide novel perspectives for the diagnosis and treatment with RA.

FOXC1‑mediated TRIM22 regulates the excessive proliferation and inflammation of fibroblast‑like synoviocytes in rheumatoid arthritis via NF‑κB signaling pathway

Rheumatoid arthritis (RA) is a common systemic autoimmune disorder of unknown etiology, which threatens public health. The regulatory role of tripartite motif-containing 22 (TRIM22) has been reported in multiple types of cancers and disease, but not in RA. The aim of the present study was therefore to elucidate the potential roles and underlying mechanisms of TRIM22 in fibroblast-like synoviocytes (FLSs) in RA. The Gene Expression Omnibus database was used to examine TRIM22 mRNA expression levels in synovial tissue samples of patients with RA and healthy controls. TRIM22 and forkhead box C1 (FOXC1) mRNA and protein expression levels in normal FLSs and RA-FLSs were assessed using reverse transcription-quantitative PCR (RT-qPCR) and western blotting, respectively. The Cell Counting Kit-8 assay was used to assess cell proliferation. Cell apoptosis was analyzed using flow cytometry. The migratory and invasive abilities of RA-FLSs were assessed using Transwell assays. Western blotting was used to analyze the protein expression levels of apoptosis-related factors, MMP2, MMP9 and NF-κB signaling pathway-related proteins. Inflammatory factors levels were assessed via ELISA and RT-qPCR. Furthermore, the JASPAR database, chromatin immunoprecipitation and the dual-luciferase reporter assays were used to determine the interaction between FOXC1 and the TRIM22 promoter. The results of the present study demonstrated that TRIM22 expression levels were significantly elevated in the synovial tissue samples of patients with RA and RA-FLSs. Moreover, FOXC1 was also significantly overexpressed in RA-FLSs. TRIM22 knockdown significantly reduced cell proliferation, migration, invasion and the inflammatory response, whereas cell apoptosis was significantly increased. Furthermore, the results demonstrated that FOXC1 may have positively mediated TRIM22 expression via binding to the TRIM22 promoter. Moreover, FOXC1 overexpression significantly reversed the outcome of TRIM22 knockdown on the proliferation, apoptosis, migration, invasion and inflammation of RA-FLSs. FOXC1 overexpression also significantly reversed the inactivation of the NF-κB signaling pathway caused by TRIM22 knockdown. In summary, the present study demonstrated that TRIM22 was potentially activated via FOXC1, which contributed to the progression of RA via the NF-κB signaling pathway.

Identification and validation of potential hub genes in rheumatoid arthritis by bioinformatics analysis

OBJECTIVE

Rheumatoid arthritis (RA) is considered to be a chronic immune disease pathologically characterized by synovial inflammation and bone destruction. At present, the potential pathogenesis of RA is still unclear. Hub genes are recognized to play a pivotal role in the occurrence and progression of RA.

METHODS

Firstly, we attempted to screen hub genes that are associated with RA, to clarify the underlying pathological mechanisms of RA, and to offer potential treatment methods for RA. We acquired these datasets (GSE12021, GSE55235, and GSE55457) of RA patients and healthy samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were recognized via R software. Then, Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were utilized to deeply explore the underlying biological functions and pathways closely associated with RA. In addition, a protein-protein interaction (PPI) network was built to further evaluate and screen for hub genes. Finally, on the basis of the results of PPI analysis, we confirmed the mRNA expression levels of five hub genes in the synovial tissue of rats modeled with RA.

RESULTS

In the human microarray datasets, LCK, JAK2, SOCS3, STAT1, and EGFR were identified as hub genes associated with RA by bioinformatics analysis. Furthermore, we verified the differential expression levels of hub genes in rat synovial tissues via qRT-PCR (P < 0.05).

CONCLUSIONS

Our findings suggest that the hub genes LCK, JAK2, SOCS3, STAT1, and EGFR might have vital roles in the progression of RA and may offer novel therapeutic treatments for RA.

Unbiased transcriptome mapping and modeling identify candidate genes and compounds of osteoarthritis

Osteoarthritis (OA) is a chronic degenerative joint disease characterized by progressive cartilage loss, subchondral bone remodeling, and synovial inflammation. Given that the current therapies for advanced OA patients are limited, the understanding of mechanisms and novel therapies are urgently needed. In this study, we employed the weighted gene co-expression network (WGCNA) method and the connectivity map (CMap) database to identify the candidate target genes and potential compounds. Four groups of co-expressing genes were identified as the OA-related modules. The biological annotations of these modules indicated some critical hallmarks of OA and aging, such as mitochondrial dysfunctions and abnormal energy metabolism, and the signaling pathways, such as MAPK, TNF, and PI3K/Akt signaling pathways. Some genes, such as RELA and GADD45B, were predicted to extensively involve these critical pathways, indicating their potential functions in OA mechanisms. Moreover, we constructed the co-expressing networks of modules and identified the hub genes based on network topology. GADD45B, MAFF, and MYC were identified and validated as the hub genes. Finally, anisomycin and MG-262 were predicted to target these OA-related modules, which may be the potential drugs for OA therapy. In conclusion, this study identified the significant modules, signaling pathways, and hub genes relevant to OA and highlighted the potential clinical value of anisomycin and MG-262 as novel therapies in OA management.

Potential biomarkers that discriminate rheumatoid arthritis and osteoarthritis based on the analysis and validation of datasets

Background

Rheumatoid arthritis (RA) and osteoarthritis (OA) share some similar arthritic symptoms, but different mechanisms underlie the pathogenesis of these two diseases. Analysis of differentially expressed molecules in rheumatoid arthritis and osteoarthritis may assist in improving diagnosis and treatment strategies in clinical practice.

Methods

Microarray and RNA-seq data were acquired from the gene expression omnibus database. Differentially expressed genes (DEGs) were identified using Bioconductor packages. Receiver operating characteristic curves were plotted to assess performance. Gene ontology enrichment analysis was conducted using the clusterProfiler application. During validation, synovial fluid was harvested from patients who had undergone in-hospital joint replacement, in which the expression of proteins was measured using enzyme-linked immunosorbent assays.

Results

Compared with OA samples, RA samples showed 14 genes to be upregulated and 3 to be downregulated. Gene ontology analysis indicated that DEGs principally included molecules responsible for the regulation of a synovial tissue inflammatory response. Seven genes displayed a good discriminatory power with an AUC higher than 0.90. ADAMDEC1 was the biomarker that most clearly discriminated RA from OA in the database, exhibiting an AUC of 0.999, a sensitivity of 100%, and a specificity of 97.8%. Following validation, the expression levels of ADAMDEC1 in the synovial fluid from RA patients were significantly higher than those in the synovial fluid from OA patients (P < 0.05). At the cut-off value of 1957 pg/mL, ADAMDEC1 expression in the synovial fluid discriminated RA from OA with an AUC of 0.951, a specificity of 88.6%, and a sensitivity of 92.9%.

Conclusion

The differential expression of genes in RA compared with OA indicates potential targets for molecular diagnosis and treatment. The presence of ADAMDEC1 in synovial fluid is a good biomarker of RA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05277-x.

Comprehensive Analysis of Key Genes, Signaling Pathways and miRNAs in Human Knee Osteoarthritis: Based on Bioinformatics

Background: Osteoarthritis (OA) is one of the main causes of disability in the elderly population, accompanied by a series of underlying pathologic changes, such as cartilage degradation, synovitis, subchondral bone sclerosis, and meniscus injury. The present study aimed to identify key genes, signaling pathways, and miRNAs in knee OA associated with the entire joint components, and to explain the potential mechanisms using computational analysis.

Methods: The differentially expressed genes (DEGs) in cartilage, synovium, subchondral bone, and meniscus were identified using the Gene Expression Omnibus 2R (GEO2R) analysis based on dataset from GSE43923, GSE12021, GSE98918, and GSE51588, respectively and visualized in Volcano Plot. Venn diagram analyses were performed to identify the overlapping DEGs (overlapping DEGs) that expressed in at least two types of tissues mentioned above. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and module analysis were conducted. Furthermore, qRT-PCR was performed to validate above results using our clinical specimens.

Results: As a result, a total of 236 overlapping DEGs were identified, of which 160 were upregulated and 76 were downregulated. Through enrichment analysis and constructing the PPI network and miRNA-mRNA network, knee OA-related key genes, such as HEY1, AHR, VEGFA, MYC, and CXCL12 were identified. Clinical validation by qRT-PCR experiments further supported above computational results. In addition, knee OA-related key miRNAs such as miR-101, miR-181a, miR-29, miR-9, and miR-221, and pathways such as Wnt signaling, HIF-1 signaling, PI3K-Akt signaling, and axon guidance pathways were also identified. Among above identified knee OA-related key genes, pathways and miRNAs, genes such as AHR, HEY1, MYC, GAP43, and PTN, pathways like axon guidance, and miRNAs such as miR-17, miR-21, miR-155, miR-185, and miR-1 are lack of research and worthy for future investigation.

Conclusion: The present informatic study for the first time provides insight to the potential therapeutic targets of knee OA by comprehensively analyzing the overlapping genes differentially expressed in multiple joint components and their relevant signaling pathways and interactive miRNAs.

Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis

Background

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis.

Methods

Five microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by "Batch correction" and "RobustRankAggreg" methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman's rank correlation analysis in R software.

Results

Ten significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package "UpSet". CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells.

Conclusions

CCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.

Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis

INTRODUCTION/OBJECTIVES

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two common joint diseases with similar clinical manifestations. Our study aimed to identify differential gene biomarkers in the synovial tissue between RA and OA using bioinformatics analysis and validation.

METHOD

GSE36700, GSE1919, GSE12021, GSE55235, GSE55584, and GSE55457 datasets were downloaded from the Gene Expression Omnibus database. A total of 57 RA samples and 46 OA samples were included. The differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Protein-protein interaction (PPI) network of DEGs and the hub genes were constructed and visualized via Search Tool for the Retrieval of Interacting Genes/Proteins, Cytoscape, and R. Selected hub genes were validated via reverse transcription-polymerase chain reaction.

RESULTS

A total of 41 DEGs were identified. GO functional enrichment analysis showed that DEGs were enriched in immune response, signal transduction, regulation of immune response for biological process, in plasma membrane and extracellular region for cell component, and antigen binding and serine-type endopeptidase activity for molecular function. KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. PPI network analysis established 70 nodes and 120 edges and 15 hub genes were identified. The expression of CXCL13, CXCL10, and ADIPOQ was statistically different between RA and OA synovial tissue.

CONCLUSION

Differential expression of CXCL13, CXCL10, and ADIPOQ between RA and OA synovial tissue may provide new insights for understanding the RA development and difference between RA and OA. Key Points • Bioinformatics analysis was used to identify the differentially expressed genes in the synovial tissue between rheumatoid arthritis and osteoarthritis. • CXCL13, CXCL10, and ADIPOQ might provide new insight for understanding the differences between RA and OA.

HTR2B and SLC5A3 Are Specific Markers in Age-Related Osteoarthritis and Involved in Apoptosis and Inflammation of Osteoarthritis Synovial Cells

Objective: Osteoarthritis (OA) is a heterogeneous age-related disease, which is badly difficult to cure due to its complex regulatory networks of pathogenesis. This study explored OA-specific genes in synovial tissues and validated their roles on apoptosis and inflammation of OA synovial cells.

Methods: Weighted correlation network analysis (WGCNA) was employed to explore OA-related co-expression modules in the GSE55235 and GSE55457 datasets. Then, this study screened OA-specific genes. After validation of these genes in the GSE12021 and GSE32317 datasets, HTR2B and SLC5A3 were obtained. Their expression was detected in human OA and healthy synovial tissues by RT-qPCR and western blot. OA rat models were constructed by anterior cruciate ligament transection (ACLT) operation. In OA synovial cells, HTR2B and SLC5A3 proteins were examined via western blot. After transfection with sh-HTR2B or sh-SLC5A3, apoptosis and inflammation of OA synovial cells were investigated by flow cytometry and western blot.

Results: A total of 17 OA-specific DEGs were identified, which were significantly enriched in inflammation pathways. Among them, HTR2B and SLC5A3 were highly expressed in end-than early-stage OA. Their up-regulation was validated in human OA synovial tissues and ACLT-induced OA synovial cells. Knockdown of HTR2B and SLC5A3 restrained apoptosis and increased TGF-β and IL-4 expression as well as reduced TNF-α and IL-1β expression in OA synovial cells.

Conclusion: Collectively, this study identified two OA-specific markers HTR2B and SLC5A3 and their knockdown ameliorated apoptosis and inflammation of OA synovial cells.

Identification of key regulators responsible for dysregulated networks in osteoarthritis by large-scale expression analysis

BACKGROUND

Osteoarthritis (OA) is a worldwide musculoskeletal disorder. However, disease-modifying therapies for OA are not available. Here, we aimed to characterize the molecular signatures of OA and to identify novel therapeutic targets and strategies to improve the treatment of OA.

METHODS

We collected genome-wide transcriptome data performed on 132 OA and 74 normal human cartilage or synovium tissues from 7 independent datasets. Differential gene expression analysis and functional enrichment were performed to identify genes and pathways that were dysregulated in OA. The computational drug repurposing method was used to uncover drugs that could be repurposed to treat OA.

RESULTS

We identified several pathways associated with the development of OA, such as extracellular matrix organization, inflammation, bone development, and ossification. By protein-protein interaction (PPI) network analysis, we prioritized several hub genes, such as JUN, CDKN1A, VEGFA, and FOXO3. Moreover, we repurposed several FDA-approved drugs, such as cardiac glycosides, that could be used in the treatment of OA.

CONCLUSIONS

We proposed that the hub genes we identified would play a role in cartilage homeostasis and could be important diagnostic and therapeutic targets. Drugs such as cardiac glycosides provided new possibilities for the treatment of OA.

Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high-throughput data

Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA.

Methods: The gene expression profile of GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in GSE55235 and GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA‐ and RA‐specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database.

Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA‐ and OA‐specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression.

Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.

Identification of biomarkers associated with synovitis in rheumatoid arthritis by bioinformatics analyses

OBJECTIVES

Rheumatoid arthritis (RA) is the most common inflammatory arthritis in the world, but its underlying mechanism is still unclear. The present study aims to screen and verify the potential biomarkers of RA.

METHODS

We searched the Gene Expression Omnibus (GEO) database for synovial expression profiling from different RA microarray studies to perform a systematic analysis. Functional annotation of differentially expressed genes (DEGs) was conducted, including GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The expression levels of the hub genes in normal membranes and RA synovium were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot system.

RESULTS

A total of 444 differential expression genes were identified, including 172 up-regulated and 272 down-regulated genes in RA synovium compared with normal controls. The top ten hub genes; protein tyrosine phosphatase receptor type C (PTPRC), LCK proto-oncogene (LCK), cell division cycle 20 (CDC20), Jun proto-oncogene (JUN), cyclin-dependent kinase 1 (CDK1), kinesin family member 11 (KIF11), epidermal growth factor receptor (epidermal growth factor receptor (EGFR), vascular endothelial growth factor A (VEGFA), mitotic arrest deficient 2 like 1 (MAD2L1), and signal transducer and activator of transcription 1 (STAT1) were identified from the PPI network, and the expression level of VEGFA and EGFR was significantly increased in RA membranes (P<0.05).

CONCLUSION

Our results indicate that the hub genes VEGFA and EGFR may have essential effects during the development of RA and can be used as potential biomarkers of RA.

Screening and verification of hub genes involved in osteoarthritis using bioinformatics

Osteoarthritis (OA) is one of the most common causes of disability and its development is associated with numerous factors. A major challenge in the treatment of OA is the lack of early diagnosis. In the present study, a bioinformatics method was employed to filter key genes that may be responsible for the pathogenesis of OA. From the Gene Expression Omnibus database, the datasets GSE55457, GSE12021 and GSE55325 were downloaded, which comprised 59 samples. Of these, 30 samples were from patients diagnosed with osteoarthritis and 29 were normal. Differentially expressed genes (DEGs) were obtained by downloading and analyzing the original data using bioinformatics. The Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathways were analyzed using the Database for Annotation, Visualization and Integrated Discovery online database. Protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/proteins online database. BSCL2 lipid droplet biogenesis associated, seipin, FOS-like 2, activator protein-1 transcription factor subunit (FOSL2), cyclin-dependent kinase inhibitor 1A (CDKN1A) and kinectin 1 (KTN1) genes were identified as key genes by using Cytoscape software. Functional enrichment revealed that the DEGs were mainly accumulated in the ErbB, MAPK and PI3K-Akt pathways. Reverse transcription-quantitative PCR analysis confirmed a significant reduction in the expression levels of FOSL2, CDKN1A and KTN1 in OA samples. These genes have the potential to become novel diagnostic and therapeutic targets for OA.

Identification of New, Functionally Relevant Mutations in the Coding Regions of the Human Fos and Jun Proto-Oncogenes in Rheumatoid Arthritis Synovial Tissue

In rheumatoid arthritis (RA), the expression of many pro-destructive/pro-inflammatory proteins depends on the transcription factor AP-1. Therefore, our aim was to analyze the presence and functional relevance of mutations in the coding regions of the AP-1 subunits of the fos and jun family in peripheral blood (PB) and synovial membranes (SM) of RA and osteoarthritis patients (OA, disease control), as well as normal controls (NC). Using the non-isotopic RNAse cleavage assay, one known polymorphism (T252C: silent; rs1046117; present in RA, OA, and NC) and three novel germline mutations of the cfos gene were detected: (i) C361G/A367G: Gln121Glu/Ile123Val, denoted as “fos121/123”; present only in one OA sample; (ii) G374A: Arg125Lys, “fos125”; and (iii) C217A/G374A: Leu73Met/Arg125Lys, “fos73/125”, the latter two exclusively present in RA. In addition, three novel somatic cjun mutations (604–606ΔCAG: ΔGln202, “jun202”; C706T: Pro236Ser, “jun236”; G750A: silent) were found exclusively in the RA SM. Tansgenic expression of fos125 and fos73/125 mutants in NIH-3T3 cells induced an activation of reporter constructs containing either the MMP-1 (matrix metalloproteinase) promoter (3- and 4-fold, respectively) or a pentameric AP-1 site (approximately 5-fold). Combined expression of these two cfos mutants with cjun wildtype or mutants (jun202, jun236) further enhanced reporter expression of the pentameric AP-1 construct. Finally, genotyping for the novel functionally relevant germline mutations in 298 RA, 288 OA, and 484 NC samples revealed no association with RA. Thus, functional cfos/cjun mutants may contribute to local joint inflammation/destruction in selected patients with RA by altering the transactivation capacity of AP-1 complexes.

The Rheumatoid Arthritis Gene Expression Signature Among Women Who Improve or Worsen During Pregnancy: A Pilot Study

OBJECTIVE

To assess whether gene expression signatures associated with rheumatoid arthritis (RA) before pregnancy differ between women who improve or worsen during pregnancy, and to determine whether these expression signatures are altered during pregnancy when RA improves or worsens.

METHODS

Clinical data and blood samples were collected before pregnancy (T0) and at the third trimester (T3) from 11 women with RA and 5 healthy women. RA disease activity was assessed using the Clinical Disease Activity Index (CDAI). At each timepoint, RA-associated gene expression signatures were identified using differential expression analysis of RNA sequencing profiles between women with RA and healthy women.

RESULTS

Of the women with RA, 6 improved by T3 (RA_improved), 3 worsened (RA_worsened),and 2 were excluded. At T0, mean CDAI scores were similar in both groups (RA_improved 11.2 ± 9.8; RA_worsened 13.8 ± 6.7; Wilcoxon rank-sum test: P = 0.6). In the RA_improved group, 89 genes were differentially expressed at T0 (q < 0.05 and fold change ≥ 2) compared to healthy women. When RA improved at T3, 65 of 89 (73%) of these genes no longer displayed RA-associated expression. In the RA_worsened group, a largely different RA gene expression signature (429 genes) was identified at T0. When RA disease activity worsened at T3, 207 of 429 (48%) genes lost their differential expression, while an additional 151 genes became newly differentially expressed.

CONCLUSION

In our pilot dataset, pre-pregnancy RA expression signatures differed between women who subsequently improved or worsened during pregnancy, suggesting that inherent genomic differences may influence how pregnancy affects disease activity. Further, these RA signatures were altered during pregnancy as disease activity changed.

Systematic Postoperative Assessment of a Minimally-Invasive Sheep Model for the Treatment of Osteochondral Defects

To assess the clinical course of a sheep stifle joint model for osteochondral (OC) defects, medial femoral condyles (MFC) were exposed without patella luxation using medial parapatellar skin (3–4 cm) and deep incisions (2–3 cm). Two defects (7 mm diameter; 10 mm depth; OC punch) were left empty or refilled with osteochondral autologous transplantation cylinders (OATS) and explanted after six weeks. Incision-to-suture time, anesthesia time, and postoperative wound or impairment scores were compared to those in sham-operated animals. Implant performance was assessed by X-ray, micro-computed tomography, histology, and immunohistology (collagens 1, 2; aggrecan). There were no surgery-related infections or patellar luxations. Operation, anesthesia, and time to complete stand were short (0.5, 1.4, and 1.5 h, respectively). The wound trauma score was low (0.4 of maximally 4; day 7). Empty-defect and OATS animals reached an impairment score of 0 significantly later than sham animals (7.4 and 4.0 days, respectively, versus 1.5 days). Empty defects showed incomplete healing and dedifferentiation/heterotopic differentiation; OATS-filled defects displayed advanced bone healing with remaining cartilage gaps and orthotopic expression of bone and cartilage markers. Minimally-invasive, medial parapatellar surgery of OC defects on the sheep MFC allows rapid and low-trauma recovery and appears well-suited for implant testing.

Integrated network analysis of symptom clusters across disease conditions

Identifying the symptom clusters (two or more related symptoms) with shared underlying molecular mechanisms has been a vital analysis task to promote the symptom science and precision health. Related studies have applied the clustering algorithms (e.g. k-means, latent class model) to detect the symptom clusters mostly from various kinds of clinical data. In addition, they focused on identifying the symptom clusters (SCs) for a specific disease, which also mainly concerned with the clinical regularities for symptom management. Here, we utilized a network-based clustering algorithm (i.e., BigCLAM) to obtain 208 typical SCs across disease conditions on a large-scale symptom network derived from integrated high-quality disease-symptom associations. Furthermore, we evaluated the underlying shared molecular mechanisms for SCs, i.e., shared genes, protein-protein interaction (PPI) and gene functional annotations using integrated networks and similarity measures. We found that the symptoms in the same SCs tend to share a higher degree of genes, PPIs and have higher functional homogeneities. In addition, we found that most SCs have related symptoms with shared underlying molecular mechanisms (e.g. enriched pathways) across different disease conditions. Our work demonstrated that the integrated network analysis method could be used for identifying robust SCs and investigate the molecular mechanisms of these SCs, which would be valuable for symptom science and precision health.

Metformin, an AMPK Activator, Inhibits Activation of FLSs but Promotes HAPLN1 Secretion

AMP-activated protein kinase (AMPK) is essential for maintaining energy balance and has a crucial role in various inflammatory pathways. In this study, AMPK levels positively correlated with many inflammatory indexes in rheumatoid arthritis (RA) patients, especially in the affected synovium. In RA sera, a positive correlation between phosphorylated (p-)AMPK-α1 levels and DAS28 (disease activity score 28) activity (r = 0.270, p < 0.0001) was found. Similarly, a positive correlation was observed between AMPK-α1 and tumor necrosis factor α (TNF-α) levels (r = 0.460, p = 0.0002). Differentially expressed genes between osteoarthritis (OA) and RA synovium from NCBI GEO profiles and our RNA sequencing data suggested activation of metabolic pathways specific to RA-fibroblast-like synoviocytes (FLSs). AMPK-α1 was highly expressed in the synovium of RA but not OA patients. An AMPK activator, metformin, inhibited FLS proliferation at higher but not lower concentrations, whereas the inhibitor dorsomorphin promoted the proliferation of RA-FLSs. Interestingly, both metformin and dorsomorphin inhibited the migration of RA-FLSs. After metformin treatment, expression of interleukin 6 (IL-6), TNF-α, and IL-1β were significantly downregulated in RA-FLSs; however, increased expression of p-AMPK-α1, protein kinase A (PKA)-α1, and HAPLN1 (hyaluronan and proteoglycan link protein 1) was observed. Increased levels of HAPLN1 in RA-FLSs by an AMPK activator could potentially be beneficial in protecting the joints. Hence, our results demonstrate the potential of an AMPK activator as a therapeutic for RA.

Discrepancy between Jun/Fos Proto-Oncogene mRNA and Protein Expression in the Rheumatoid Arthritis Synovial Membrane

Rheumatoid arthritis (RA) is a chronic inflammatory and destructive joint disease characterized by overexpression of pro-inflammatory/pro-destructive mediators, whose regulation has been the focus of our previous studies. Since the expression of these proteins commonly depends on AP-1, the expression of the AP-1-forming subunits cJun, JunB, JunD, and cFos was assessed in synovial membrane (SM) samples of RA, osteoarthritis (OA), joint trauma (JT), and normal controls (NC) using ELISA and qRT-PCR. With respect to an observed discrepancy between mRNA and protein levels, the expression of the mRNA stability-modifying factors AU-rich element RNA-binding protein (AUF)-1, tristetraprolin (TTP), and human antigen R (HuR) was measured. JunB and JunD protein expression was significantly higher in RA-SM compared to OA and/or NC. By contrast, jun/fos mRNA expression was significantly (cjun) or numerically decreased (junB, junD, cfos) in RA and OA compared to JT and/or NC. Remarkably, TTP and HuR were also affected by discrepancies between their mRNA and protein levels, since they were significantly decreased at the mRNA level in RA versus NC, but significantly or numerically increased at the protein level when compared to JT and NC. Discrepancies between the mRNA and protein expression for Jun/Fos and TTP/HuR suggest broad alterations of post-transcriptional processes in the RA-SM. In this context, increased levels of mRNA-destabilizing TTP may contribute to the low levels of jun/fos and ttp/hur mRNA, whereas abundant mRNA-stabilizing HuR may augment translation of the remaining mRNA into protein with potential consequences for the composition of the resulting AP-1 complexes and the expression of AP-1-dependent genes in RA.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

BACKGROUND

Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown.

OBJECTIVE

To develop a gene signature in OA.

METHOD

In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA.

RESULTS AND DISCUSSION

Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8).

CONCLUSION

The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

Pro-inflammatory Cytokines Drive Deregulation of Potassium Channel Expression in Primary Synovial Fibroblasts

The synovium secretes synovial fluid, but is also richly innervated with nociceptors and acts as a gateway between avascular joint tissues and the circulatory system. Resident fibroblast-like synoviocytes' (FLS) calcium-activated potassium channels (K _Ca) change in activity in arthritis models and this correlates with FLS activation.

Objective

To investigate this activation in an in vitro model of inflammatory arthritis; 72 h treatment with cytokines TNFα and IL1β.

Methods

FLS cells were isolated from rat synovial membranes. We analyzed global changes in FLS mRNA by RNA-sequencing, then focused on FLS ion channel genes and the corresponding FLS electrophysiological phenotype and finally modeling data with ingenuity pathway analysis (IPA) and MATLAB.

Results

IPA showed significant activation of inflammatory, osteoarthritic and calcium signaling canonical pathways by cytokines, and we identified ∼200 channel gene transcripts. The large K _Ca (BK) channel consists of the pore forming Kcnma1 together with β-subunits. Following cytokine treatment, a significant increase in Kcnma1 RNA abundance was detected by qPCR and changes in several ion channels were detected by RNA-sequencing, including a loss of BK channel β-subunit expression Kcnmb1/2 and an increase in Kcnmb3. In electrophysiological experiments, there was a decrease in over-all current density at 20 mV without change in chord conductance at this potential.

Conclusion

TNFα and IL1β treatment of FLS in vitro recapitulated several common features of inflammatory arthritis at the transcriptomic level, including increase in Kcnma1 and Kcnmb3 gene expression.

The Immune Cell Landscape in Different Anatomical Structures of Knee in Osteoarthritis: A Gene Expression-Based Study

Background

Immunological mechanisms play a vital role in the pathogenesis of knee osteoarthritis (KOA). Moreover, the immune phenotype is a relevant prognostic factor in various immune-related diseases. In this study, we used CIBERSORT for deconvolution of global gene expression data to define the immune cell landscape of different structures of knee in osteoarthritis. Methods and Findings. By applying CIBERSORT, we assessed the relative proportions of immune cells in 76 samples of knee cartilage, 146 samples of knee synovial tissue, 40 samples of meniscus, and 50 samples of knee subchondral bone. Enumeration and activation status of 22 immune cell subtypes were provided by the obtained immune cell profiles. In synovial tissues, the differences in proportions of plasma cells, M1 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, and eosinophils between normal tissues and osteoarthritic tissues were statistically significant (P < 0.05). The area under the curve was relatively large in resting mast cells, dendritic cells, and M2 macrophages in receiver operating characteristic analyses. In subchondral bones, the differences in proportions of resting master cells and neutrophils between normal tissues and osteoarthritic tissues were statistically significant (P < 0.05). In subchondral bones, the proportions of immune cells, from the principle component analyses, displayed distinct group-bias clustering. Resting mast cells and T cell CD8 were the major component of first component. Moreover, we revealed the potential interaction between immune cells. There was almost no infiltration of immune cells in the meniscus and cartilage of the knee joint.

Conclusions

The immune cell composition in KOA differed substantially from that of healthy joint tissue, while it also differed in different anatomical structures of the knee. Meanwhile, activated mast cells were mainly associated with high immune cell infiltration in OA. Furthermore, we speculate M2 macrophages in synovium and mast cells in subchondral bone may play an important role in the pathogenesis of OA.

Identification and functional analysis of long non-coding RNAs in the synovial membrane of osteoarthritis patients

Osteoarthritis (OA), the most common chronic joint disease in the elderly, has become a significant economic burden for families and societies worldwide. Although treatments are continually improving, current drugs only target joint pain, with no effective therapies modifying OA progression. Long noncoding RNAs (lncRNAs), which have received increasing attention in recent years, are abnormally expressed in OA cartilage. In the present study, weighted coexpression network analysis (WGCNA) was applied to identify modules related to certain OA clinical traits. In total, 4404 coding genes and 161 lncRNAs were differentially expressed based on two OA expression profile data sets and normal control samples. Subsequently, 11 independent modules were acquired, and the green module, with a total of 49 hub genes, was identified as the most relevant to OA. These hub genes were validated using the GSE12021 data set. There was only one lncRNA among the hub genes, namely, NONHSAG034351. Thus, we further explored the function of NONHSAG034351‐related genes in the network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that NONHSAG034351‐associated genes are involved in the response to lipopolysaccharide, angiogenesis, tumour necrosis factor (TNF) signalling, and mitogen‐activated protein kinase (MAPK) signalling pathways. In conclusion, we identified modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA, was downregulated in OA synovial tissue and might play a significant role in the pathological progression of this disease. Our findings have important clinical implications and could provide novel biomarkers that indicate the molecular mechanisms of OA and act as potential therapeutic targets.

Significance of this study

Long noncoding RNAs (lncRNAs) have been reported to be abnormally expressed in osteoarthritis (OA), which is the most common chronic joint disease among the elderly. In the present study, we report the expression profiles of lncRNAs in OA and the identification of modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA identified to be downregulated in the synovial tissue of OA patients, might play a significant role in the pathological progression of OA. Furthermore, our findings provide novel biomarkers associated with the molecular mechanisms underlying OA pathogenesis, thus implying potential therapeutic targets with important clinical implications.

Mitochondrial dysfunction in rheumatoid arthritis: A comprehensive analysis by integrating gene expression, protein-protein interactions and gene ontology data

Several studies have reported mitochondrial dysfunction in rheumatoid arthritis (RA). Many nuclear DNA (nDNA) encoded proteins translocate to mitochondria, but their participation in the dysfunction of this cell organelle during RA is quite unclear. In this study, we have carried out an integrative analysis of gene expression, protein-protein interactions (PPI) and gene ontology data. The analysis has identified potential implications of the nDNA encoded proteins in RA mitochondrial dysfunction. Firstly, by analysing six synovial microarray datasets of RA patients and healthy controls obtained from the gene expression omnibus (GEO) database, we found differentially expressed nDNA genes that encode mitochondrial proteins. We uncovered some of the roles of these genes in RA mitochondrial dysfunction using literature search and gene ontology analysis. Secondly, by employing gene co-expression from microarrays and collating reliable PPI from seven databases, we created the first mitochondrial PPI network that is specific to the RA synovial joint tissue. Further, we identified hubs of this network, and moreover, by integrating gene expression and network analysis, we found differentially expressed neighbours of the hub proteins. The results demonstrate that nDNA encoded proteins are (i) crucial for the elevation of mitochondrial reactive oxygen species (ROS) and (ii) involved in membrane potential, transport processes, metabolism and intrinsic apoptosis during RA. Additionally, we proposed a model relating to mitochondrial dysfunction and inflammation in the disease. Our analysis presents a novel perspective on the roles of nDNA encoded proteins in mitochondrial dysfunction, especially in apoptosis, oxidative stress-related processes and their relation to inflammation in RA. These findings provide a plethora of information for further research.

Dysregulated integrin αVβ3 and CD47 signaling promotes joint inflammation, cartilage breakdown, and progression of osteoarthritis

Osteoarthritis (OA) is the leading cause of joint failure, yet the underlying mechanisms remain elusive, and no approved therapies that slow progression exist. Dysregulated integrin function was previously implicated in OA pathogenesis. However, the roles of integrin αVβ3 and the integrin-associated receptor CD47 in OA remain largely unknown. Here, transcriptomic and proteomic analyses of human and murine osteoarthritic tissues revealed dysregulated expression of αVβ3, CD47, and their ligands. Using genetically deficient mice and pharmacologic inhibitors, we showed that αVβ3, CD47, and the downstream signaling molecules Fyn and FAK are crucial to OA pathogenesis. MicroPET/CT imaging of a mouse model showed elevated ligand-binding capacities of integrin αVβ3 and CD47 in osteoarthritic joints. Further, our in vitro studies demonstrated that chondrocyte breakdown products, derived from articular cartilage of individuals with OA, induced αVβ3/CD47-dependent expression of inflammatory and degradative mediators, and revealed the downstream signaling network. Our findings identify a central role for dysregulated αVβ3 and CD47 signaling in OA pathogenesis and suggest that activation of αVβ3 and CD47 signaling in many articular cell types contributes to inflammation and joint destruction in OA. Thus, the data presented here provide a rationale for targeting αVβ3, CD47, and their signaling pathways as a disease-modifying therapy.

Clinical Applications of Synovial Biopsy

The synovial tissue is a primary target of multiple diseases characterized by different pathogenic mechanisms, including infective, deposition, neoplastic, and chronic immune-inflammatory pathologies. Synovial biopsy can have a relevant role in differential diagnosis of specific conditions in clinical practice, although its exploitation remains relatively limited. In particular, no validated synovial-tissue-derived biomarkers are currently available in the clinic to aid in the diagnosis and management in most frequent forms of chronic inflammatory arthropathies, namely rheumatoid arthritis (RA) and the spondyloarthritides (SpA). In this brief review, we will discuss the current spectrum of clinical applications of synovial biopsy in routine rheumatologic care and will provide an analysis of the perspectives for its potential exploitation in patients with chronic inflammatory arthritides.

LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies

Gene-environment association (GEA) studies are essential to understand the past and ongoing adaptations of organisms to their environment, but those studies are complicated by confounding due to unobserved demographic factors. Although the confounding problem has recently received considerable attention, the proposed approaches do not scale with the high-dimensionality of genomic data. Here, we present a new estimation method for latent factor mixed models (LFMMs) implemented in an upgraded version of the corresponding computer program. We developed a least-squares estimation approach for confounder estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several order faster than existing GEA approaches and then our previous version of the LFMM program. In addition, the new method outperforms other fast approaches based on principal component or surrogate variable analysis. We illustrate the program use with analyses of the 1000 Genomes Project data set, leading to new findings on adaptation of humans to their environment, and with analyses of DNA methylation profiles providing insights on how tobacco consumption could affect DNA methylation in patients with rheumatoid arthritis. Software availability: Software is available in the R package lfmm at https://bcm-uga.github.io/lfmm/.

Calcium-Phosphate Crystals Promote RANKL Expression via the Downregulation of DUSP1

Osteoarthritis (OA) is a naturally occurring, irreversible disorder and a major health burden. The disease is multifactorial, involving both physiological and mechanical processes, but calcium crystals have been associated intimately with its pathogenesis. This study tested the hypothesis that these crystals have a detrimental effect on the differentiation of osteoclasts and bone homeostasis. This study employed an osteoblast-osteoclast coculture system that resembles in vivo osteoblast-dependent osteoclast differentiation along with Ca2+-phosphate-coated culture dishes. The calcium-containing crystals upregulated the expression of RANKL and increased the differentiation of osteoclasts significantly as a result. On the other hand, osteoblast differentiation was unaffected. MicroRNA profiling showed that dual-specificity phosphatases 1 (DUSP1) was associated with the increased RANKL expression. DUSP1 belongs to a family of MAPK phosphatases and is known to inactivate all three groups of MAPKs, p38, JNK, and ERK. Furthermore, knockdown of DUSP1 gene expression suggested that RANKL expression increases significantly in the absence of DUSP1 regulation. Microarray analysis of the DUSP1 mRNA levels in patients with pathological bone diseases also showed that the downregulated DUSP1 expression leads to increased expression of RANKL and consequently to the destruction of the bone observed in these patients. These findings suggest that calcium-containing crystals may play a crucial role in promoting RANKL-induced osteoclastogenesis via DUSP1.

MiR-5571-3p and miR-135b-5p, derived from analyses of microRNA profile sequencing, correlate with increased disease risk and activity of rheumatoid arthritis

OBJECTIVES

This study aimed to investigate microRNA (miRNA) expression profiles in synovium tissues of rheumatoid arthritis (RA) patients by RNA sequencing and to evaluate the values of dysregulated miRNAs in RA diagnosis and monitoring.

METHODS

Thirty RA patients who underwent knee arthroscopy and 30 controls with knee trauma who underwent surgery were consecutively recruited, and synovium tissue samples of both groups were obtained during surgeries. In the exploration part, miRNA and mRNA expression profiles of 3 RA samples and 3 control samples were detected using RNA sequencing then followed by bioinformatic analyses. In the validation part, 5 candidate miRNA levels were detected by quantitative polymerase chain reaction (qPCR) in 30 RA patients and 30 control patients.

RESULTS

In the exploration part, 78 miRNAs and 1582 mRNAs were upregulated while 40 miRNAs and 1295 mRNAs were downregulated in synovium tissue samples of RA patients compared with those of controls. Furthermore, enrichment analyses revealed that these dysregulated miRNAs and mRNAs were mainly implicated in immune activities and inflammatory diseases such as leukocyte migration, complement activation, and RA. In the validation part, qPCR assay revealed that miR-5571-3p and miR-135b-5p expressions were increased in RA patients compared with those in controls and disclosed good predictive values for RA risk with high area under the curves (AUCs). Besides, both miR-5571-3p and miR-135b-5p levels were positively correlated with disease activity and inflammation level of RA.

CONCLUSIONS

Analyses of miRNA expression profiles by sequencing indicate that miR-5571-3p and miR-135b-5p correlate with increased RA risk and activity.

Identification of potential biomarkers for differential diagnosis between rheumatoid arthritis and osteoarthritis via integrative genome‑wide gene expression profiling analysis

The present study aimed to identify potential novel biomarkers in synovial tissue obtained from patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA) for differential diagnosis. The genome-wide expression profiling datasets of synovial tissues from RA and OA cohorts, including GSE55235, GSE55457 and GSE55584 datasets, were retrieved and used to identify differentially expressed genes (DEGs; P<0.05; false discovery rate <0.05 and Fold Change >2) between RA and OA using R software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs were performed to determine molecular and biochemical pathways associated with the identified DEGs, and a protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software. Significant modules in the PPI network and candidate driver genes were screened using the Molecular Complex Detection Algorithm. Potential biomarkers were evaluated by receiver operating characteristic and logistic regression analyses. Large numbers of DEGs were detected, including 273, 205 and 179 DEGs in the GSE55235, GSE55457 and GSE55584 datasets, respectively. Among them, 80 DEGs exhibited identical expression trends in all the three datasets, including 49 upregulated and 31 downregulated genes in patients with RA. DEGs in patients suffering from RA compared with patients suffering from OA were predominantly associated with the primary immunodeficiency pathway, including interleukin 7 receptor (IL7R) and signal transducer activator of transcription 1 (STAT1). The sensitivity of IL7R + STAT1 to differentiate RA from OA was 93.94% with a specificity of 80.77%. The results generated from analyses of the GSE36700 dataset were closely associated with results generated from analyses of GSE55235, GSE55457 and GSE55584 datasets, which further verified the reliability of the aforementioned results. The results of the present study suggested that increased expression of IL7R and STAT1 in synovial tissue as well as in the primary immunodeficiency may be associated with RA occurrence. These identified novel biomarkers may be used to predict disease occurrence and clinically differentiate RA from OA.

Effects of rheumatoid arthritis associated transcriptional changes on osteoclast differentiation network in the synovium

Background

Osteoclast differentiation in the inflamed synovium of rheumatoid arthritis (RA) affected joints leads to the formation of bone lesions. Reconstruction and analysis of protein interaction networks underlying specific disease phenotypes are essential for designing therapeutic interventions. In this study, we have created a network that captures signal flow leading to osteoclast differentiation. Based on transcriptome analysis, we have indicated the potential mechanisms responsible for the phenotype in the RA affected synovium.

Method

We collected information on gene expression, pathways and protein interactions related to RA from literature and databases namely Gene Expression Omnibus, Kyoto Encyclopedia of Genes and Genomes pathway and STRING. Based on these information, we created a network for the differentiation of osteoclasts. We identified the differentially regulated network genes and reported the signaling that are responsible for the process in the RA affected synovium.

Result

Our network reveals the mechanisms underlying the activation of the neutrophil cytosolic factor complex in connection to osteoclastogenesis in RA. Additionally, the study reports the predominance of the canonical pathway of NF-κB activation in the diseased synovium. The network also confirms that the upregulation of T cell receptor signaling and downregulation of transforming growth factor beta signaling pathway favor osteoclastogenesis in RA. To the best of our knowledge, this is the first comprehensive protein–protein interaction network describing RA driven osteoclastogenesis in the synovium.

Discussion

This study provides information that can be used to build models of the signal flow involved in the process of osteoclast differentiation. The models can further be used to design therapies to ameliorate bone destruction in the RA affected joints.

Integration of Gene Expression Profile Data to Screen and Verify Hub Genes Involved in Osteoarthritis

Osteoarthritis (OA) is one of the most common diseases worldwide, but the pathogenic genes and pathways are largely unclear. The aim of this study was to screen and verify hub genes involved in OA and explore potential molecular mechanisms. The expression profiles of GSE12021 and GSE55235 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 39 samples, including 20 osteoarthritis synovial membranes and 19 matched normal synovial membranes. The raw data were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The Gene Ontology (GO) and pathway enrichment of DEGs were performed by DAVID and Kyoto Encyclopedia of Genes and Genomes (KEGG) online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The top 10 hub genes VEGFA, IL6, JUN, IL1β, MYC, IL4, PTGS2, ATF3, EGR1, and DUSP1 were identified from the PPI network. Module analysis revealed that OA was associated with significant pathways including TNF signaling pathway, cytokine-cytokine receptor interaction, and osteoclast differentiation. The qRT-PCR result showed that the expression level of IL6, VEGFA, JUN, IL-1β, and ATF3 was significantly increased in OA samples (p < 0.05), and these candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of OA.

Identification of key genes associated with rheumatoid arthritis with bioinformatics approach

We aimed to identify key genes associated with rheumatoid arthritis (RA).The microarray datasets of GSE1919, GSE12021, and GSE21959 (35 RA samples and 32 normal controls) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in RA samples were identified using the t test in limma package. Functional enrichment analysis was performed using clusterProfiler package. A protein-protein interaction (PPI) network of selected DEGs was constructed based on the Human Protein Reference Database. Active modules were explored using the jActiveModules plug-in in the Cytoscape Network Modeling package.In total, 537 DEGs in RA samples were identified, including 241 upregulated and 296 downregulated genes. A total of 24,451 PPI pairs were collected, and 5 active modules were screened. Furthermore, 19 submodules were acquired from the 5 active modules. Discs large homolog 1 (DLG1) and related DEGs such as guanylate cyclase 1, soluble, alpha 2 (GUCY1A2), N-methyl d-aspartate receptor 2A subunit (GRIN2A), and potassium voltage-gated channel member 1 (KCNA1) were identified in 8 submodules. Plasminogen (PLG) and related DEGs such as chemokine (C-X-C motif) ligand 2 (CXCL2), laminin, alpha 3 (LAMA3), complement component 7 (C7), and coagulation factor X (F10) were identified in 4 submodules.Our results indicate that DLG1, GUCY1A2, GRIN2A, KCNA1, PLG, CXCL2, LAMA3, C7, and F10 may play key roles in the progression of RA and may serve as putative therapeutic targets for treating RA.

A Comprehensive Gene Expression Meta-analysis Identifies Novel Immune Signatures in Rheumatoid Arthritis Patients

Rheumatoid arthritis (RA), a symmetric polyarticular arthritis, has long been feared as one of the most disabling forms of arthritis. Identification of gene signatures associated with RA onset and progression would lead toward development of novel diagnostics and therapeutic interventions. This study was undertaken to identify unique gene signatures of RA patients through large-scale meta-profiling of a diverse collection of gene expression data sets. We carried out a meta-analysis of 8 publicly available RA patients’ (107 RA patients and 76 healthy controls) gene expression data sets and further validated a few meta-signatures in RA patients through quantitative real-time PCR (RT-qPCR). We identified a robust meta-profile comprising 33 differentially expressed genes, which were consistently and significantly expressed across all the data sets. Our meta-analysis unearthed upregulation of a few novel gene signatures including PLCG2, HLA-DOB, HLA-F, EIF4E2, and CYFIP2, which were validated in peripheral blood mononuclear cell samples of RA patients. Further, functional and pathway enrichment analysis reveals perturbation of several meta-genes involved in signaling pathways pertaining to inflammation, antigen presentation, hypoxia, and apoptosis during RA. Additionally, PLCG2 (phospholipase Cγ2) popped out as a novel meta-gene involved in most of the pathways relevant to RA including inflammasome activation, platelet aggregation, and activation, thereby suggesting PLCG2 as a potential therapeutic target for controlling excessive inflammation during RA. In conclusion, these findings highlight the utility of meta-analysis approach in identifying novel gene signatures that might provide mechanistic insights into disease onset, progression and possibly lead toward the development of better diagnostic and therapeutic interventions against RA.