Biomarkers For Rheumatoid Arthritis Research Articles

Role of CXCL-10 as a Biomarker for Rheumatoid Arthritis

Abstract Background CXCL-10 has been detected within sera, synovial fluid, and synovial tissue in patients with RA and identified as a pro-inflammatory chemokine that mediate leukocyte trafficking and modulate innate and adaptive immune responses. It plays a critical role in the inflammatory response and is involved in several biological processes. Objective To evaluate the predicative significance of chemokine CXCL10 in rheumatoid arthritis (RA) and to focus on its relation to disease activity. Patients and Methods A case control study included 30 patients; were diagnosed with RA, and 30 healthy controls. Clinical examination was done for all patients. Measurement of serum CXCL10 level was done by ELISA, while assessment of disease activity in patients was done using disease activity score (DAS-28). Serum levels of CXCL10 were significantly higher in RA patients than controls. Results Serum level of CXCL-10 was ranging from 349–1380 pg/ml with a mean of 784.6 ± 152.2 in RA patients, which was very high level compared to control group the serum level was ranging from 137 – 476.8 pg/ml with a mean of 338.1 ± 125.5. The difference was statistically highly significant (p < 0.001). AUC for CXCL-10 was 0.951 and the 95% CI was ≤ 1. The cutoff point of CXCL-10 was 450 pg/mL were able to differentiate between RA patients and control group. Most of RA patients (70%) were on methotrexate with dose ranged from 12.5–25 mg per week. Thirty-five (58.3%) patients were on steroids with dose 5-20 mg per day. Comparison of CXCL-10 levels in patients with different grades of disease activity had a statistically significant difference between different grades. Conclusion Our study supports the critical role of CXCL10 in the RA inflammatory cascade. The correlation of CXCL10 with clinical disease activity suggests that CXCL10 plays a central role in RA inflammation and may serve as a disease activity marker in RA. CXCL10 has a significant role as a biomarker for prediction of RA.

Do Ultrasound Lung Abnormalities Correlate to Biomarkers and Male Gender in Rheumatoid Arthritis Patients? A Monocentric Cross-Sectional Study.

Background: Lung ultrasound (LUS) is a tool of growing interest in Rheumatoid Arthritis (RA) oligo- symptomatic ILD to avoid. Objective: We aimed to evaluate (i) the prevalence of pleural (PLUS) and parenchymal (PAUS) abnormalities in LUS in the RA population and their possible correlation to biomarkers; (ii) the predictivity of gender, smoking habits, previous infections (past COVID-19 tuberculosis), and treatments; (iii) the differences in LUS between sexes. Methods: We collected the data of 155 (15 early and 140 late) RA patients with mild respiratory symptoms, evaluating PLUS and PAUS, in fourteen lung areas and also summing the scores (LUS-T). Results: Only 13/155 (8.4%) were completely negative; LUS correlated to age (all parameters p 0.0001), rheumatoid factor IgM (PLUS p 0.0006, PAUS p 0.02, LUS-T p 0.001) and ACPA (p 0.001, 0.006, 0.001, respectively), and PLUS also correlated to IL6 (p 0.02). The male gender was predictive of all LUS evaluations (p 0.001, 0.05, 0.001, respectively), which were higher than in women (p 0.001, 0.01, 0.001, respectively). Other potential risk factors were independent, except biological treatments, which showed a low predictivity to PLUS (p < 0.05). Conclusions: We can conclude that LUS is a useful technique in RA low respiratory symptoms and correlates with age, the most important RA biomarkers, and male sex.

Sinomenine treats rheumatoid arthritis by inhibiting MMP9 and inflammatory cytokines expression: bioinformatics analysis and experimental validation

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease marked by inflammatory cell infiltration and joint damage. The Chinese government has approved the prescription medication sinomenine (SIN), an effective anti-inflammation drug, for treating RA. This study evaluated the possible anti-inflammatory actions of SIN in RA based on bioinformatics analysis and experiments. Six microarray datasets were acquired from the gene expression omnibus (GEO) database. We used R software to identify differentially expressed genes (DEGs) and perform function evaluations. The CIBERSORT was used to calculate the abundance of 22 infiltrating immune cells. The weighted gene co-expression network analysis (WGCNA) was used to discover genes associated with M1 macrophages. Four public datasets were used to predict the genes of SIN. Following that, function enrichment analysis for hub genes was performed. The cytoHubba and least absolute shrinkage and selection operator (LASSO) were employed to select hub genes, and their diagnostic effectiveness was predicted using the receiver operator characteristic (ROC) curve. Molecular docking was undertaken to confirm the affinity between the SIN and hub gene. Furthermore, the therapeutic efficacy of SIN was validated in LPS-induced RAW264.7 cells line using Western blot and Enzyme-linked immunosorbent assay (ELISA). The matrix metalloproteinase 9 (MMP9) was identified as the hub M1 macrophages-related biomarker in RA using bioinformatic analysis and molecular docking. Our study indicated that MMP9 took part in IL-17 and TNF signaling pathways. Furthermore, we found that SIN suppresses the MMP9 protein overexpression and pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the LPS-induced RAW264.7 cell line. In conclusion, our work sheds new light on the pathophysiology of RA and identifies MMP9 as a possible RA key gene. In conclusion, the above findings demonstrate that SIN, from an emerging research perspective, might be a potential cost-effective anti-inflammatory medication for treating RA.

Safety of Tocilizumab on Rheumatoid Arthritis in Patients with Interstitial Lung Disease.

The prognosis of rheumatoid arthritis (RA) with interstitial lung disease (ILD) is particularly poor. Although drugs that do not contribute to the progression of ILD should be used in RA treatment, none have been established. This study evaluated the safety of tocilizumab in terms of ILD activity. This study prospectively enrolled all 55 patients with RA complicated by ILD who were treated with tocilizumab at Dokkyo Medical University Saitama Medical Center from April 2014 to June 2022. The outcome measures were MMP-3 and KL-6 as biomarkers of RA and ILD activity, respectively, and the relationship between them was analyzed. Both MMP-3 and KL-6 were significantly improved at 6 months of treatment (P < 0.001 and P < 0.05, respectively), and a weak correlation between MMP-3 and KL-6 was observed (R2 = 0.086, P = 0.087). The group with increased MMP-3 due to RA progression had significantly higher KL-6 at 6 months compared with the group with RA improvement (P < 0.05). Also, the group with ILD progression on computed tomography had significantly higher MMP-3 compared with the groups with improvement or no change of ILD (P < 0.05 and P < 0.01, respectively). The mortality rate was 0% at 6 months, 2.0% at 1 year, 16.7% at 2 years, and 32.4% at 3 years, and mortality from acute exacerbation of ILD due to respiratory infection increased over time. RA activity and ILD activity were found to be related at 6 months of treatment. Tocilizumab does not seem to affect the mechanism of ILD progression, as most patients showed improvement in both MMP-3 and KL-6 with tocilizumab within 6 months, when this drug would be expected to affect the lungs directly. However, respiratory infection exacerbated ILD from 1 year after the start of treatment. As immunosuppressive drugs, including tocilizumab, have a risk of respiratory infection, it is important to identify early signs of infection.

Combination of scavenger receptor-A with anti-cyclic citrullinated peptide antibody for the diagnosis of rheumatoid arthritis.

The routine biomarkers for rheumatoid arthritis (RA), including anticyclic citrullinated peptide antibody (anti-CCP), rheumatoid factor (RF), immunoglobulin M (IgM), erythrocyte sedimentation rate (ESR), and C-reaction protein (CRP) have limited sensitivity and specificity. Scavenger receptor-A (SR-A) is a novel RA biomarker identified by our group recently, especially for seronegative RA. Here, we performed a large-scale multicentre study to further assess the diagnostic value of SR-A in combination with other biomarkers for RA. The performance of SR-A in combination with other biomarkers for RA diagnosis was first revealed by a pilot study, and was further elucidated by a large-scale multicentre study. A total of 1129 individuals from 3 cohorts were recruited in the study, including RA patients, healthy controls, and patients with other common rheumatic diseases. Diagnostic properties were evaluated by the covariate-adjusted receiver-operating characteristic (AROC) curve, sensitivity, specificity and clinical association, respectively. Large-scale multicentre analysis showed that SR-A and anti-CCP dual combination was the optimal method for RA diagnosis, increasing the sensitivity of anti-CCP by 13% (87% vs 74%) while maintaining a specificity of 90%. In early RA patients, SR-A and anti-CCP dual combination also showed promising diagnostic value, increasing the sensitivity of anti-CCP by 7% (79% vs 72%) while maintaining a specificity of 94%. Moreover, SR-A and anti-CCP dual combination was correlated with ESR, IgM, and autoantibodies of RA patients, further revealing its clinical significance. SR-A and anti-CCP dual combination could potentially improve early diagnosis of RA, thus improving the prognosis and reducing mortality.

Differences in the expression of long noncoding RNAs in peripheral blood mononuclear cells indicate potential biomarkers for rheumatoid arthritis

ObjectiveLong noncoding RNAs (lncRNAs) play an increasingly important role in various autoimmune diseases. We aimed to characterize the expression profiles of lncRNAs in peripheral blood mononuclear cells (PBMCs) from RA patients and to assess the potential of these lncRNAs as RA biomarkers. MethodsWhole-transcriptome sequencing was used to establish a lncRNA expression profile. A total of 155 RA patients, 145 healthy controls, 59 systemic lupus erythematosus (SLE) patients and 59 primary Sjögren’s syndrome (pSS) patients were recruited for this study. Four candidate lncRNAs (linc00152, lnc-ADM-1, ITSN1-2, and lnc-FTH1-7) were validated via qRT-PCR in independent samples, and their expression, association with RA clinical features and value as RA biomarkers were evaluated. ResultsLinc00152 and lnc-ADM-1 exhibited upregulated expression (p = 0.001, p = 0.014, respectively), while ITSN1-2 and lnc-FTH1-7 exhibited downregulated expression (both p < 0.001, respectively) in RA patients compared to controls. Lnc-ADM-1 and lnc-FTH1-7 expression correlated positively with the C4 level (p = 0.016 and p = 0.012, respectively). ITSN1-2 levels were negatively associated with CRP levels (p = 0.024). Linc00152, lnc-ADM-1, ITSN1-2, and lnc-FTH1-7 showed potential as RA biomarkers, with the four-lncRNA panel distinguishing RA patients from controls, SLE patients, or pSS patients (AUC = 0.886, 0.746, and 0.749, respectively). ConclusionThe altered expression of linc00152, lnc-ADM-1, ITSN1-2 and lnc-FTH1-7 in RA patients suggested that these genes may serve as potential biomarkers for RA and could be involved in its pathogenesis.

SLAMF8 as a potential biomarker for rheumatoid arthritis identified by comparing peripheral blood mononuclear cells, fibroblast‐like synoviocytes, and synovial tissue using bioinformatics analysis

AbstractBackgroundRheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease. Its pathogenesis is not fully understood, and early diagnosis is challenging owing to the lack of effective biomarkers. This study aimed to analyze different samples to identify potential biomarkers and therapeutic targets.MethodsMicroarray datasets of RA, osteoarthritis (OA), and healthy control (HC) were downloaded from the Gene Expression Omnibus database. R software was used to identify differentially expressed genes (DEGs), which were visualized using volcano and heat maps. Venn diagrams, principal component analysis, gene set enrichment analysis, gene ontology, and Kyoto Encyclopedia of genes and genomes were used to analyze the data. A protein–protein interaction network was constructed, and synovial tissues from patients with RA and OA were collected for verification using the collagen‐induced arthritis mouse model.ResultsMore DEGs were found in synovial tissues than in peripheral blood mononuclear cells or fibroblast‐like synoviocytes. Principal component analysis revealed significant differences between the RA and OA samples, highlighting the unique advantages of synovial tissue. Enrichment analysis revealed that metabolic and cytokine signaling pathways play crucial roles in the development of RA. Further analysis of the four synovial datasets identified 54 DEGs, of which signaling lymphocytic activation molecule family (SLAMF) 8 was identified as the key molecule. SLAMF8 levels were increased in the synovial tissue of patients with RA compared to those of patients with OA (0.38 ± 0.19 vs. 12.40 ± 1.66), and SLAMF8 levels were similarly elevated in collagen‐induced arthritis model mice compared with those in the healthy mice (1.13 ± 0.47 vs. 9.05 ± 2.5).ConclusionsThis study established the unique advantages of synovial tissue for RA research and identified metabolic and cytokine signaling pathways as important for RA development. Thus, SLAMF8 may be a potential therapeutic target for RA.

LncRNA SNHG3 discriminates rheumatoid arthritis from healthy individuals and regulates inflammatory response and oxidative stress via modulating miR-128-3p.

This study evaluated the expression and significance of SNHG3 in Rheumatoid arthritis (RA) aiming to explore a biomarker and regulator for RA. The expression of SNHG3 in serum and synovial tissue was compared between RA patients and healthy individuals using PCR. The RA animal models were induced by the porcine type II collagen with Wistar rats and validated by the foot volume and AI score. The human fibroblast-like synoviocytes (H-FLS) were treated with LPS to mimic the injury during RA onset and the cell growth was assessed by CCK8 assay. SNHG3 was significantly downregulated in the serum and synovial tissue of RA patients compared with healthy individuals. Downregulated SNHG3 could discriminate RA patients from healthy individuals with high sensitivity (0.875) and specificity (0.844). Porcine type II collagen induced increasing foot volume and AI scores of rats and SNHG3 was downregulated in RA rats. In LPS-induced H-FLS, SNHG3 negatively regulated miR-128-3p, and the alleviated effect of SNHG3 overexpression on cellular inflammation and oxidative stress was reversed by miR-128-3p upregulation. Serum SNHG3 was considered a potential diagnostic biomarker for RA from healthy individuals. SNHG3 regulated inflammatory response and oxidative stress via negatively modulating miR-128-3p.

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis.

Seronegative rheumatoid arthritis (RA) is defined as RA without circulating autoantibodies such as rheumatoid factor and anti-citrullinated protein antibodies; thus, early diagnosis of seronegative RA can be challenging. Here, we aimed to identify diagnostic biomarkers for seronegative RA by performing lipidomic analyses of sera and urine samples from patients with RA. We performed untargeted lipidomic analysis of sera and urine samples from 111 RA patients, 45 osteoarthritis (OA) patients, and 25 healthy controls (HC). These samples were divided into a discovery cohort (n = 97) and a validation cohort (n = 84). Serum samples from 20 patients with systemic lupus erythematosus (SLE) were also used for validation. The serum lipidome profile of RA was distinguishable from that of OA and HC. We identified a panel of ten serum lipids and three urine lipids in the discovery cohort that showed the most significant differences. These were deemed potential lipid biomarker candidates for RA. The serum lipid panel was tested using a validation cohort; the results revealed an accuracy of 79%, a sensitivity of 71%, and a specificity of 86%. Both seropositive and seronegative RA patients were differentiated from patients with OA, SLE, and HC. Three urinary lipids showing differential expression between RA from HC were identified with an accuracy of 84%, but they failed to differentiate RA from OA. There were five lipid pathways that differed between seronegative and seropositive RA. Here, we identified a panel of ten serum lipids as potential biomarkers that can differentiate RA from OA and SLE, regardless of seropositivity. In addition, three urinary lipids had diagnostic utility for differentiating RA from HC.

Autoimmune biomarkers in Rheumatoid arthritis: A clinical and environmental approach

Autoimmune biomarkers in Rheumatoid arthritis: A clinical and environmental approach

Clinical Phenotypes, Serological Biomarkers, and Synovial Features Defining Seropositive and Seronegative Rheumatoid Arthritis: A Literature Review.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder which can lead to long-term joint damage and significantly reduced quality of life if not promptly diagnosed and adequately treated. Despite significant advances in treatment, about 40% of patients with RA do not respond to individual pharmacological agents and up to 20% do not respond to any of the available medications. To address this large unmet clinical need, several recent studies have focussed on an in-depth histological and molecular characterisation of the synovial tissue to drive the application of precision medicine to RA. Currently, RA patients are clinically divided into "seropositive" or "seronegative" RA, depending on the presence of routinely checked antibodies. Recent work has suggested that over the last two decades, long-term outcomes have improved significantly in seropositive RA but not in seronegative RA. Here, we present up-to-date differences in epidemiology, clinical features, and serological biomarkers in seronegative versus seropositive RA and discuss how histological and molecular synovial signatures, revealed by recent large synovial biopsy-based clinical trials, may be exploited to refine the classification of RA patients, especially in the seronegative group.

Application of Salivary Alpha-1 Antitrypsin in the Diagnosis of Rheumatoid Arthritis: A Pilot Study.

Background and Objective: Rheumatoid arthritis (RA) is an autoimmune disease in which joints are gradually destroyed. Early diagnosis and treatment before joint deformation or destruction is important. The detection of novel RA biomarkers in saliva may facilitate early detection of RA before disease onset. This study aimed to evaluate salivary concentration of α1-antitrypsin (A1AT) in healthy patients and those with RA, and to assess the diagnostic value of salivary A1AT. Materials and Methods: In total, 80 participants were included: 20 healthy participants, and 60 patients with RA. Saliva and serum samples were obtained from all the patients. Levels of A1AT and cytokines, including interleukin-1 beta (IL-1β), IL-6, and IL-10 in saliva and serum, were evaluated using an enzyme-linked immunosorbent assay kit and Luminex assay. Data were analyzed using SPSS for Windows. Results: There was a higher level of A1AT in the saliva of patients with RA (median: 2388.66 ng/mL) than that in healthy controls (1579.06 ng/mL). There was a positive mild-to-moderate accuracy (area under the curve: 0.57-0.85) of A1AT in saliva to diagnose RA. The cut-off level (ng/mL) of A1AT in saliva for detecting RA was 1689.0. Conclusions: The obtained data can promote the application of the measurements of A1AT in saliva to diagnose RA.

Computational inhibition of S100A8 (calgranulin A) as a potential non-invasive biomarker for rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic joint pain and inflammation, loss of mobility, which affects the quality of life. The etiology of RA is unknown, and there is no isolated cause for the development of this illness. Synovial inflammation, autoantibody generation and bone degradation leading to deformity are classic characteristics of RA. The search for a non-invasive biomarker is crucial for helping patients receive standard therapies leading to faster treatments, as diagnosis depends on invasive biopsies. Therefore, in the present investigation, using transcriptomics and proteomic approaches, potential genes involved in crucial networks in RA were identified. Gene expression datasets of two tissue types i.e. whole blood and synovial tissue were retrieved from the GEO database and used for transcriptomic analyses. Using SWATH-MS analysis, differentially expressed proteins were identified from collected saliva of RA patients. Through bioinformatics analysis, S100A8, also known as Calprotectin (a complex of S100A8/A9) or Calgranulin A, was found to be common among all tissue types. S100A8 was then further quantified in saliva of healthy volunteers and RA patients, where it was found that the protein's level in healthy controls was lowered when compared to RA patients. Through in-silico characterization, potential plant-based inhibitors of S100A8 were also identified, to reduce its pro-inflammatory effects. Thus, it may be important in the pathophysiology of RA and, in the future, might help guide targeted treatment. as well as act as a non-invasive diagnostic biomarker platform.

Serum miR-124a and miR-34a as Potential Biomarkers for Rheumatoid Arthritis

Abstract Background: MicroRNAs (miRs) are defined as noncoding small RNAs that are involved in the regulation of various immune functions, indicating they could be possible biomarkers for immune-mediated disorders. We aimed to evaluate miR-124a and miR-34a levels in serum as diagnostic biomarkers for rheumatoid arthritis (RA) and to investigate their correlation with the disease activity in RA patients. Methods: Our study consisted of 40 patients with RA and 40 controls. The disease activity for the RA patients was evaluated using the Disease Activity Score in 28 joints (DAS28). Relative quantification of miR-124a and miR-34a expressions in serum was conducted by reverse transcriptase quantitative real-time polymerase chain reaction. Results: Expression levels of miR-124a and miR-34a in serum were significantly lower in RA patients (median: 0.64 and 0.30, respectively) compared to controls (median: 3.12 and 1.88, respectively), P &lt; 0.001. Their serum levels were negatively associated with disease activity and inversely correlated with DAS28, C-reactive protein, and erythrocyte sedimentation rate (P &lt; 0.001). Conclusion: Serum miR-124a and miR-34a could serve as potential noninvasive biomarkers for RA diagnosis and reflect the disease activity in RA patients.

Comparison of Low-Dose Interleukin 2 Therapy in Conjunction With Standard Therapy in Patients With Systemic Lupus Erythematosus vs Rheumatoid Arthritis: A Systematic Review.

This systematic review aims to compare the efficacy and safety of a novel immunotherapy with low-dose interleukin 2 (IL2) across two of the most prevalent autoimmune diseases i.e. systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Contemporary therapeutic practices have not been able to achieve complete remission from these autoimmune disorders. In contrast, low-dose IL2has shown promise in achieving this therapeutic goal via inducing self-tolerance in patients with autoimmune diseases; however, due to variable irregularities among autoimmune processes of variable diseases, the benefit of low-dose IL2 could not be determined among different autoimmune diseases. Therefore, we conducted a study to compare low-dose IL2 therapy effects on SLE and RA. We systematically screened four databases: PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed Central (PMC), and Google Scholar. Inclusion and exclusion criteria were implemented. Quality appraisal of studies chosen for the review was done using the Cochrane Risk-of-Bias (RoB) assessment tool for randomized controlled trials, and the Newcastle-Ottawa Scale (NOS) and JBI critical appraisal tool for non-randomized clinical trials. Information was gathered from seven articles: three randomized controlled trials and four non-randomized clinical trials. Our review concluded that low-dose IL2 therapy in conjunction with respective standard therapies for SLE and RA has a higher efficacy and safety profile as compared to standard therapy alone and the therapeutic effects were comparable in both SLE and RA patients treated with low-dose IL2; however, this novel intervention does not seem to have a significant corrective effect on the biomarkers of RA as it does for SLE biomarkers.

High Growth Differentiation Factor-15 (GDF-15) in Rheumatoid Arthritis Patients Potential Risk for Cardiovascular Disease

Rheumatoid arthritis (RA) is a chronic autoimmune disorder primarily affecting joints, leading to pain, stiffness, and functional disability. The inflammatory mechanisms underlying RA also impact various organ systems, with significant implications for morbidity and mortality. In this study, we analyzed the growth differentiation factor-15 (GDF-15), a cytokine implicated in inflammation and associated with RA and cardiovascular diseases. We conducted a case-control study involving 150 RA patients and 150 healthy individuals, assessing various biomarkers (ACPA, CRP, ESR, and RF) including GDF-15, lipid profile, and inflammatory markers. Our results demonstrated significantly elevated levels of GDF-15 in RA patients (309.44 pg/ml) compared to controls (64.40 pg/ml), indicating its potential role as a biomarker for RA and cardiovascular risk (p&lt;0.001). Furthermore, RA patients exhibited dyslipidemia characterized by elevated total cholesterol, triglycerides, LDL cholesterol, and atherogenic indices, along with decreased HDL cholesterol levels, predisposing them to a higher risk of atherosclerosis and cardiovascular complications (p&lt;0.001). Correlation analyses revealed associations between GDF-15 levels, lipid profile parameters, and disease severity markers, highlighting the intricate interplay between inflammation, lipid metabolism, and RA progression. These findings demonstrated the importance of early detection and management of dyslipidemia in RA patients to mitigate cardiovascular risk. Overall, our study contributes to understanding the pathophysiology of RA and identifies potential biomarkers for disease monitoring and risk stratification.

Identification of clinical characteristics biomarkers for rheumatoid arthritis through targeted DNA methylation sequencing

ObjectivesRheumatoid arthritis (RA) is a complex disease with a challenging diagnosis, especially in seronegative patients. The aim of this study is to investigate whether the methylation sites associated with the overall immune response in RA can assist in clinical diagnosis, using targeted methylation sequencing technology on peripheral venous blood samples. MethodsThe study enrolled 241 RA patients, 30 osteoarthritis patients (OA), and 30 healthy volunteers control (HC). Fifty significant cytosine guanine (CG) sites between undifferentiated arthritis and RA were selected and analyzed using targeted DNA methylation sequencing. Logistic regression models were used to establish diagnostic models for different clinical features of RA, and six machine learning methods (logit model, random forest, support vector machine, adaboost, naive bayes, and learning vector quantization) were used to construct clinical diagnostic models for different subtypes of RA. Least absolute shrinkage and selection operator regression and detrended correspondence analysis were utilized to screen for important CGs. Spearman correlation was used to calculate the correlation coefficient. ResultsThe study identified 16 important CG sites, including tumor necrosis factort receptor associated factor 5 (TRAF5) (chr1:211500151), mothers against decapentaplegic homolog 3 (SMAD3) (chr15:67357339), tumor endothelial marker 1 (CD248) (chr11:66083766), lysosomal trafficking regulator (LYST) (chr1:235998714), PR domain zinc finger protein 16 (PRDM16) (chr1:3307069), A-kinase anchoring protein 10 (AKAP10) (chr17:19850460), G protein subunit gamma 7 (GNG7) (chr19:2546620), yes1 associated transcriptional regulator (YAP1) (chr11:101980632), PRDM16 (chr1:3163969), histone deacetylase complex subunit sin3a (SIN3A) (chr15:75747445), prenylated rab acceptor protein 2 (ARL6IP5) (chr3:69134502), mitogen-activated protein kinase kinase kinase 4 (MAP3K4) (chr6:161412392), wnt family member 7A (WNT7A) (chr3:13895991), inhibin subunit beta B (INHBB) (chr2:121107018), deoxyribonucleic acid replication helicase/nuclease 2 (DNA2) (chr10:70231628) and chromosome 14 open reading frame 180 (C14orf180) (chr14:105055171). Seven CG sites showed abnormal changes between the three groups (P < 0.05), and 16 CG sites were significantly correlated with common clinical indicators (P < 0.05). Diagnostic models constructed using different CG sites had an area under the receiver operating characteristic curve (AUC) range of 0.64–0.78 for high-level clinical indicators of high clinical value, with specificity ranging from 0.42 to 0.77 and sensitivity ranging from 0.57 to 0.88. The AUC range for low-level clinical indicators of high clinical value was 0.63–0.72, with specificity ranging from 0.48 to 0.74 and sensitivity ranging from 0.72 to 0.88. Diagnostic models constructed using different CG sites showed good overall diagnostic accuracy for the four subtypes of RA, with an accuracy range of 0.61–0.96, a balanced accuracy range of 0.46–0.94, and an AUC range of 0.46–0.94. ConclusionsThis study identified potential clinical diagnostic biomarkers for RA and provided novel insights into the diagnosis and subtyping of RA. The use of targeted deoxyribonucleic acid (DNA) methylation sequencing and machine learning methods for establishing diagnostic models for different clinical features and subtypes of RA is innovative and can improve the accuracy and efficiency of RA diagnosis.

Osteoimmunology: The Crosstalk between T Cells, B Cells, and Osteoclasts in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an ongoing inflammatory condition that affects the joints and can lead to severe damage to cartilage and bones, resulting in significant disability. This condition occurs when the immune system becomes overactive, causing osteoclasts, cells responsible for breaking down bone, to become more active than necessary, leading to bone breakdown. RA disrupts the equilibrium between osteoclasts and osteoblasts, resulting in serious complications such as localized bone erosion, weakened bones surrounding the joints, and even widespread osteoporosis. Antibodies against the receptor activator of nuclear factor-κB ligand (RANKL), a crucial stimulator of osteoclast differentiation, have shown great effectiveness both in laboratory settings and actual patient cases. Researchers are increasingly focusing on osteoclasts as significant contributors to bone erosion in RA. Given that RA involves an overactive immune system, T cells and B cells play a pivotal role by intensifying the immune response. The imbalance between Th17 cells and Treg cells, premature aging of T cells, and excessive production of antibodies by B cells not only exacerbate inflammation but also accelerate bone destruction. Understanding the connection between the immune system and osteoclasts is crucial for comprehending the impact of RA on bone health. By delving into the immune mechanisms that lead to joint damage, exploring the interactions between the immune system and osteoclasts, and investigating new biomarkers for RA, we can significantly improve early diagnosis, treatment, and prognosis of this condition.

LncRNA expression profiling in exosomes derived from synovial fluid of patients with rheumatoid arthritis

ObjectiveTo identify the long non-coding RNA (lncRNA) expression profiling in exosomes derived from synovial fluid of rheumatoid arthritis (RA) patients, and carry out bioinformatics analysis on target genes of differentially expressed lncRNAs. MethodsExosomes were isolated from synovial fluid via ultracentrifugation. RNAs were extracted from exosomes by using HiPure Liquid RNA/miRNA kits, followed by lncRNA sequencing. Differentially expressed lncRNAs in RA were screened, and bioinformatics analysis of their target genes was carried out. qRT-PCR was used to verify the lncRNA expression levels. ResultsCompared with osteoarthritis (OA), 347 lncRNAs were found differentially expressed in RA. Compared with gout, 805 lncRNAs were found differentially expressed in RA. Compared with both OA and gout, 85 lncRNAs were found specially expressed in RA (65 were upregulated (including ENST00000433825.1)). Functional analysis of target genes of the specially expressed lncRNAs revealed significant enrichment of “autophagy” and “mTOR signaling pathway”. The qRT-PCR results indicated that ENST00000433825.1 was highly expressed in RA, compared with both OA and gout (P < 0.05), which matched the lncRNA sequencing results. Correlation analysis showed that the level of ENST00000433825.1 in RA patients was significantly and positively correlated with the level of C-reactive protein (CRP) (P < 0.001). ConclusionsThe lncRNA expression profiling in exosomes derived from synovial fluid of RA was significantly different from OA and gout. ENST00000433825.1 was highly and uniquely expressed in RA and significantly and positively correlated with CRP, which might provide a diagnostic and therapeutic biomarker for RA.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study.

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.