Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis

Abstract

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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Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning

“…Mature molecular typing strategies are able to predict the optimal therapeutic strategy prior to patient treatment, thereby substantially improving patient prognosis (8). In previous reports, the molecular subtyping of diseases such as hepatocellular carcinoma, osteoarthritis, and rheumatoid arthritis, for example, has laid the foundation for their individualized treatment (9)(10)(11). Therefore, the development of molecular subtyping of PCD based on its molecular features is crucial for improving the accuracy of clinical treatment decisions and deepening our understanding of PCD.…”

Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis

“…The study of RA pathology has utilized gene chip technology, which has led to the discovery of diagnostic biomarkers for RA through programmed death-related genes ( Fan et al, 2023 ; Jiang et al, 2023 ; Li et al, 2023 ). This study screened DE-ARGs between normal and RA samples by differential analysis based on abundant public datasets.…”