Rheumatoid arthritis (RA) is a chronic, heterogeneous autoimmune disease. Its high disability rate has a serious impact on society and individuals, but there is still a lack of effective and reliable diagnostic markers and therapeutic targets for RA. In this study, we integrated RA patient information from three GEO databases for differential gene expression analysis. Additionally, we also obtained pan-cancer-related genes from the TCGA and GTEx databases. For RA-related differential genes, we performed functional enrichment analysis and constructed a weighted gene co-expression network (WGCNA). Then, we obtained 490 key genes by intersecting the significant module genes selected by WGCNA and the differential genes. After using the RanddomForest, SVM-REF, and LASSO three algorithms to analyze these key genes and take the intersection, based on the four core genes (BTN3A2, CYFIP2, ST8SIA1, and TYMS) that we found, we constructed an RA diagnosis. The nomogram model showed good reliability and validity after evaluation, and the ROC curves of the four genes showed that these four genes played an important role in the pathogenesis of RA. After further gene correlation analysis, immune infiltration analysis, and mouse gene expression validation, we finally selected CYFIP2 as the cut-in gene for pan-cancer analysis. The results of the pan-cancer analysis showed that CYFIP2 was closely related to the prognosis of patients with various tumors, the degree of immune cell infiltration, as well as TMB, MSI, and other indicators, suggesting that this gene may be a potential intervention target for human diseases including RA and tumors.
Rheumatoid arthritis (RA) is a chronic, heterogeneous autoimmune disease with a high disability rate that seriously affects society and individuals. However, there is a lack of effective and reliable diagnostic markers and therapeutic targets. In this study, we identified diagnostic markers of RA based on RNA modification and explored its role as well as degree of immune cell infiltration. We used the gene expression profile data of three synovial tissues (GSE55235, GSE55457, GSE77298) from the Gene Expression Omnibus (GEO) database and the gene of 5 RNA modification genes (including m6A, m1A, m5C, APA, A-1), combined with cluster analysis, identified four RNA modifiers closely related to RA (YTHDC1, LRPPRC, NOP2, and CLP1) and five immune cells namely T cell CD8, CD4 memory resting, T cells regulatory (Tregs) Macrophages M0, and Neutrophils. Based on the LASSO regression algorithm, hub genes and immune cell prediction models were established respectively in RA and a nomogram based on the immune cell model was built. Around 4 key RNA modification regulator genes, miRNA-mRNA, mRNA-TF networks have been established, and GSEA-GO, KEGG-GSEA enrichment analysis has been carried out. Finally, CLP1 was established as an effective RA diagnostic marker, and was highly positively correlated with T cells follicular helper (Tfh) infiltration. On the other hand, highly negatively correlated with the expression of mast cells. In short, CLP1 may play a non-negligible role in the onset and development of RA by altering immune cell infiltration, and it is predicted to represent a novel target for RA clinical diagnosis and therapy.
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