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

Clinical phenotypes of comorbidities in end-stage knee osteoarthritis: a cluster analysis

OBJECTIVES

Comorbidities, as components of these heterogeneous features, often coexist with knee osteoarthritis, and are particularly prevalent in end-stage knee osteoarthritis. Here, we attempted to identify the different clinical phenotypes of comorbidities in patients with end-stage knee osteoarthritis by cluster analysis.

METHODS

A total of 421 inpatients diagnosed with end-stage knee osteoarthritis who underwent inpatient surgery were included in this cross-sectional study. 23 demographic, comorbidity, inflammatory immune and evaluation scale variables were collected. Systematic clustering after factor analysis and separate two-step cluster analysis were performed for individual comorbidity variables and all variables, respectively, to objectively identify the different clinical phenotypes of the study patients.

RESULTS

Four clusters were finally identified. Cluster 1 had the largest proportion of obese patients (93.8%) and hypertension was common (71.2%). Almost all patients in cluster 2 were depressed (95.8%) and anxiety disorders (94.7%). Cluster 3 combined patients with isolated end-stage knee osteoarthritis and a few comorbidities. Cluster 4 had the highest proportion of patients with rheumatoid arthritis (58.8%).

CONCLUSIONS

Patients with end-stage knee osteoarthritis may be classified into four different clinical phenotypes: "isolated end-stage knee osteoarthritis"; "obesity + hypertension"; "depression + anxiety"; and "rheumatoid arthritis", which may help guide individualized patient care and treatment strategies.

The m6A/m1A/m5C-Related Methylation Modification Patterns and Immune Landscapes in Rheumatoid Arthritis and Osteoarthritis Revealed by Microarray and Single-Cell Transcriptome

Purpose

The goal of this study was to explore the expression characteristics of RNA modification-related genes, reveal immune landscapes and identify novel potential diagnostic biomarkers in osteoarthritis (OA) and rheumatoid arthritis (RA) patients.

Patients and Methods

RNA microarray and single-cell sequencing (scRNA-seq) data were downloaded from gene expression omnibus (GEO) database. Differentially expressed RNA modification-related genes were identified and then functionally annotated. Univariate logistic regression and lasso regression analysis were used to identify primary disease genes for OA and RA. Validation was done using scRNA-seq analysis and immunohistochemistry (IHC) in human knee synovial tissues and a murine destabilization of the medial meniscus (DMM) model. Through WGCNA analysis, genes associated with cell pyroptosis or autophagy in OA and RA were identified, which were then combined with differentially expressed RNA modification-related genes to construct a PPI interaction network. Furthermore, hub genes were selected for ceRNA interaction network analysis, correlation analysis with OA and RA molecular subtypes, as well as correlation analysis with 22 immune cells.

Results

Six RNA modification-related genes (ADAMDEC1, IGHM, OGN, TNFRSF11B, SCARA3 and PTN) were identified as potential OA and RA pathogenesis biomarkers. Their expression was validated in human knee synovial tissues and a murine DMM model. Functional enrichment of differentially expressed RNA modification-related genes between RA and OA was analyzed using GO, KEGG, GSEA, and GSVA. Based on WGCNA and PPI analysis, the six hub genes related to pyroptosis and RNA modification (CXCL10, CXCL9, CCR7, CCL5, CXCL1, and CCR2) were identified as central nodes for ceRNA interaction, correlation with OA and RA molecular subtypes, and association with 22 immune cells.

Conclusion

Our research revealed the significance of RNA modification-related genes in the development of OA and RA pathogenesis, thereby providing a novel research direction for understanding the mechanisms, diagnosis, and treatment of OA and RA.

RNA-Seq Reveals the mRNAs, miRNAs, and lncRNAs Expression Profile of Knee Joint Synovial Tissue in Osteoarthritis Patients

Osteoarthritis (OA) is a chronic disease common in the elderly population and imposes significant health and economic burden. Total joint replacement is the only currently available treatment but does not prevent cartilage degeneration. The molecular mechanism of OA, especially the role of inflammation in disease progression, is incompletely understood. We collected knee joint synovial tissue samples of eight OA patients and two patients with popliteal cysts (controls), measured the expression levels of lncRNAs, miRNAs, and mRNAs in these tissues by RNA-seq, and identified differentially expressed genes (DEGs) and key pathways. In the OA group, 343 mRNAs, 270 lncRNAs, and 247 miRNAs were significantly upregulated, and 232 mRNAs, 109 lncRNAs, and 157 miRNAs were significantly downregulated. mRNAs potentially targeted by lncRNAs were predicted. Nineteen overlapped miRNAs were screened based on our sample data and GSE 143514 data. Pathway enrichment and functional annotation analyses showed that the inflammation-related transcripts CHST11, ALDH1A2, TREM1, IL-1β, IL-8, CCL5, LIF, miR-146a-5p, miR-335-5p, lncRNA GAS5, LINC02288, and LOC101928134 were differentially expressed. In this study, inflammation-related DEGs and non-coding RNAs were identified in synovial samples, suggesting that competing endogenous RNAs have a role in OA. TREM1, LIF, miR146-5a, and GAS5 were identified to be OA-related genes and potential regulatory pathways. This research helps elucidate the pathogenesis of OA and identify novel therapeutic targets for this disorder.

Vascular Age, Metabolic Panel, Cardiovascular Risk and Inflammaging in Patients With Rheumatoid Arthritis Compared With Patients With Osteoarthritis

Introduction

The risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) is 1.5–2 times higher than the general population. The fundamental risk factor for CVD is age, related to alterations at the arterial level. The aim of the study was to compare vascular age (VA) in RA patients under a strict treat-to-target (T2T) strategy with Osteoarthritis (OA) patients without strict follow up and to assess the influence of inflammaging (chronic, sterile, low-grade inflammation related to aging) and metabolic markers on VA.

Materials and Methods

This was an analytical cross-sectional study. Patients with RA (under a strict a T2T strategy) and OA patients without strict clinical follow-up were included. Patients with a history of uncontrolled hypertension, CVD, and/or current smoking were excluded. Sociodemographic, physical activity, and toxic exposure data were obtained. Waist-hip ratio and body mass index (BMI) were measured. DAS-28 (RA) and inflammatory markers, lipid profile, and glycaemia were analyzed. Pulse wave velocity (PWV) was measured (oscillometric method, Arteriograph-TensioMed®). VA was calculated based on PWV. Eleven components of inflammaging [six interleukins, three metalloproteinases (MMP), and two tissue inhibitors of metalloproteinases (TIMP)] were evaluated (Luminex® system). Univariate and bivariate analyzes (Mann Whitney U and chi-square) and correlations (Spearmans Rho) were done to compare the two groups.

Results

A total of 106 patients (74% women) were included, 52/RA and 54/OA. The mean age was 57 (Interquartile range - IQR 9 years). The BMI, waist circumference, and weight were higher in patients with OA (p < 0.001). RA patients had low disease activity (DAS-28-CRP). There were no differences in VA, inflammaging nor in PWV between the two groups. VA had a positive, but weak correlation, with age and LDL. In group of RA, VA was higher in those who did not receive methotrexate (p = 0.013). LDL levels correlated with MMP1, TIMP1, and TIMP2.

Conclusions

When comparing RA patients with low levels of disease activity with OA patients with poor metabolic control, there are no differences in VA. Furthermore, methotrexate also influences VA in RA patients. This shows that implemented therapies may have an impact on not only the inflammatory state of the joint but also CVD risk.