Chaperone/scaffolding/adaptor protein 14-3-3η (eta): A diagnostic marker of rheumatoid arthritis

Does Eta Protein Differentiate Rheumatoid Arthritis from Psoriatic Arthritis?

AIM

The clinical symptoms and laboratory markers of Rheumatoid Arthritis (RA) and Psoriatic Arthritis (PsA) can be very similar, so making a differential diagnosis between these two diseases is often difficult. Serological parameters to be used in differential diagnosis can guide the clinician. This study aimed to investigate the usability of 14-3-3η (eta) protein as a biomarker in the differential diagnosis of PsA and RA, and the relationships between eta protein and disease activity scores and joint erosions in PsA and RA.

METHODS

54 PsA patients, 53 RA patients, and 56 healthy individuals were included in this study. The ELISA (Enzyme-Linked ImunoSorbent Assay) kit was used as a quantitative sandwich enzyme immunoassay technique to detect human eta protein levels. Receiver- operating Characteristic (ROC) curves analysis was used to determine the sensitivity and specificity of the eta protein.

RESULTS

Eta protein levels were found to be significantly higher in the RA group than in the PsA [B: -0.341, OR (95% CI): 0.711 (0.556-0.909), p: 0.007] and control [B: -0.225, OR (95% CI): 0.798 (0.641-0.995), p: 0.045] groups. Eta protein median values were significantly higher in patients with joint erosion than in those without [β= 0.151, OR (95% CI): 1.163 (1.003-1.349), p: 0.046].

CONCLUSION

Eta protein levels are higher in the serum of RA patients than PsA and are associated with joint erosion. Eta protein may be a potential biomarker in the differential diagnosis of RA and PsA. It may represent a possible therapeutic step in the pathophysiological pathways in the development of joint erosion.

Linkage of calprotectin with inflammation, activity and treatment response of rheumatoid arthritis: a meta-analysis

Aim: This meta-analysis aimed to investigate the clinical value of calprotectin in rheumatoid arthritis (RA) patients. Methods: The data regarding blood calprotectin levels in RA patients were retrieved from PubMed, Embase, Web of Science and Cochrane databases. Results: Thirty-one eligible articles were included. Calprotectin was increased in RA patients compared with healthy controls (mean difference [MD] = 1.48, 95% CI: 1.16-1.79). Calprotectin was positively associated with C-reactive protein (correlation coefficient [CC] = 0.58, 95% CI: 0.53-0.63) and disease activity score (CC = 0.48, 95% CI: 0.38-0.58) in RA patients. Interestingly, calprotectin showed an increased trend in RA responders compared with nonresponders, but without statistical significance (MD = 0.38, 95% CI: -0.09-0.85). Conclusion: Blood calprotectin relates to disease risk, inflammation and activity in RA patients.

Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection

Ankylosing spondylitis (AS) is a common inflammatory spondyloarthritis affecting the spine and sacroiliac joint that finally results in sclerosis of the axial skeleton. Aside from human leukocyte antigen B27, transcriptomic biomarkers in blood for AS diagnosis still remain unknown. Hence, this study aimed to investigate credible AS-specific mRNA biomarkers from the whole blood of AS patients by analyzing an mRNA expression profile (GSE73754) downloaded Gene Expression Omnibus, which includes AS and healthy control blood samples. Weighted gene co-expression network analysis was performed and revealed three mRNA modules associated with AS. By performing gene set enrichment analysis, the functional annotations of these modules revealed immune biological processes that occur in AS. Several feature mRNAs were identified by analyzing the hubs of the protein-protein interaction network, which was based on the intersection between differentially expressed mRNAs and mRNA modules. A machine learning-based feature selection method, SVM-RFE, was used to further screen out 13 key feature mRNAs. After verifying by qPCR, IL17RA, Sqstm1, Picalm, Eif4e, Srrt, Lrrfip1, Synj1 and Cxcr6 were found to be significant for AS diagnosis. Among them, Cxcr6, IL17RA and Lrrfip1 were correlated with severity of AS symptoms. In conclusion, our findings provide a framework for identifying the key mRNAs in whole blood of AS that is conducive for the development of novel diagnostic markers for AS.