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A Novel Autophagy-Related Marker for Improved Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

ABSTRACT: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two most common rheumatic diseases in the world. Although there are standard methods for the diagnosis of both RA and OA, the differentials in some cases are poor. With deepening research, the role of autophagy in maintaining cell homeostasis and thus enabling cells adapt to external environments has become increasingly prominent. Both RA and OA, two diseases with inherent differences in pathogenesis, gradually show differences in autophagy levels. Our study therefore aims to further understand differences in pathogenesis of RA and OA through in-depth studies of autophagy in RA and OA. We also define appropriate autophagy-related markers as recognition indicators. Differences in autophagy levels between RA and OA were found based on analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment (ssGSEA). These differences were mainly caused by 134 differentially expressed genes (DEGs). In two autophagy-related genes, CXCR4 and SERPINA1, there existed significant statistical difference between RA and OA. An autophagy related index (ARI) was thus successfully constructed based on CXCR4 and SERPINA by binary logistic regression of the generalized linear regression (GLR) algorithm. Pearson analysis indicated that the expression of CXCR4, SERPINA1, and ARI were closely correlated with autophagy scores and immune infiltration. Moreover, ARI showed high disease identification through receiver operating characteristic (ROC) analysis (AUCtesting cohort = 0.956, AUCtraining cohort = 0.867). These results were then verified in GSE12021 independent cohort. In conclusion, ARI associated with autophagy and immune infiltration was successfully constructed for accurately identifying OA and RA. The index, thus, has great potential in clinical applications.

SUBMITTER: Huang R

PROVIDER: S-EPMC8546229 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Rheumatoid arthritis and osteoarthritis are two common musculoskeletal disorders that affect the joints. Despite high prevalence rates, etiological factors involved in these disorders remain largely unknown. Dissecting the molecular aspects of these disorders will significantly contribute to improving their diagnosis and clinical management. In order to identify proteins that are differentially expressed between these two conditions, a quantitative proteomic profiling of synovial fluid obtained from rheumatoid arthritis and osteoarthritis patients was carried out by using iTRAQ labeling followed by high resolution mass spectrometry analysis. RESULTS:We have identified 575 proteins out of which 135 proteins were found to be differentially expressed by ?3-fold in the synovial fluid of rheumatoid arthritis and osteoarthritis patients. Proteins not previously reported to be associated with rheumatoid arthritis including, coronin-1A (CORO1A), fibrinogen like-2 (FGL2), and macrophage capping protein (CAPG) were found to be upregulated in rheumatoid arthritis. Proteins such as CD5 molecule-like protein (CD5L), soluble scavenger receptor cysteine-rich domain-containing protein (SSC5D), and TTK protein kinase (TTK) were found to be upregulated in the synovial fluid of osteoarthritis patients. We confirmed the upregulation of CAPG in rheumatoid arthritis synovial fluid by multiple reaction monitoring assay as well as by Western blot. Pathway analysis of differentially expressed proteins revealed a significant enrichment of genes involved in glycolytic pathway in rheumatoid arthritis. CONCLUSIONS:We report here the largest identification of proteins from the synovial fluid of rheumatoid arthritis and osteoarthritis patients using a quantitative proteomics approach. The novel proteins identified from our study needs to be explored further for their role in the disease pathogenesis of rheumatoid arthritis and osteoarthritis.Sartaj Ahmad and Raja Sekhar Nirujogi contributed equally to this article.

Project description: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.

Project description:BackgroundRheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets.MethodWe downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software "limma" package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines.ResultCHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A.ConclusionIn summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.

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A Novel Autophagy-Related Marker for Improved Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

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