Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis

Abstract Background Rheumatoid arthritis (RA) is the most common chronic autoimmune connective tissue disease. However, early RA difficult to diagnose due lack of effective biomarkers. This study aimed identify new biomarkers and mechanisms for disease progression at transcriptome level through a combination microarray bioinformatics analyses. Methods Microarray datasets synovial in or osteoarthritis (OA) were downloaded from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) identified by R software. Tissue/organ-specific recognized BioGPS. Enrichment analyses performed protein–protein interaction (PPI) networks constructed understand functions enriched pathways DEGs hub genes. Cytoscape was used construct co-expressed network competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value diagnosis validated GEO datasets. The ggpubr package perform statistical Student’s t-test. Results A total 275 between 16 samples 10 OA GSE77298 GSE82107. Among these DEGs, 71 tissue/organ-specific recognized. Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) enrichment analysis indicated that are mostly immune response, immune-related biological process, system, cytokine signal pathways. Fifteen gene cluster modules Cytoscape. Eight haematologic/immune system-specific verified GZMA, PRC1, TTK may be potential RA. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might regulatory regulate Conclusions work three genes, namely, TTK, as treatment provided insight into development level. In addition, we proposed TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK control