通过生物信息学分析，三个血液/免疫系统特异性表达基因被认为是早期类风湿关节炎诊断的潜在生物标志物。

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

背景：类风湿关节炎（RA）是最常见的慢性自身免疫性结缔组织病。然而，由于缺乏有效的生物标志物，早期 RA 很难诊断。本研究旨在通过微阵列和生物信息学分析相结合，在转录组水平上确定 RA 疾病进展的新生物标志物和机制。

方法：从基因表达综合数据库（GEO）下载 RA 或骨关节炎（OA）滑膜组织的微阵列数据集，使用 R 软件识别差异表达基因（DEGs）。通过 BioGPS 识别组织/器官特异性基因。进行富集分析，并构建蛋白质-蛋白质相互作用（PPI）网络，以了解 DEGs 的功能和富集途径，并识别枢纽基因。使用 Cytoscape 构建共表达网络和竞争性内源 RNA（ceRNA）网络。使用 GEO 数据集验证具有 RA 早期诊断高诊断价值的生物标志物。使用 Student's t-test 通过 ggpubr 包进行统计分析。

结果：从数据集 GSE77298 和 GSE82107 中，共鉴定出 16 个 RA 样本和 10 个 OA 样本之间的 275 个 DEGs。在这些 DEGs 中，识别出 71 个组织/器官特异性表达基因。基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析表明，DEGs 主要富集于免疫反应、免疫相关生物过程、免疫系统和细胞因子信号通路。通过 Cytoscape 鉴定出 15 个枢纽基因和基因簇模块。通过 GEO 数据集验证了 8 个血液/免疫系统特异性表达的枢纽基因。GZMA、PRC1 和 TTK 可能是早期 RA 诊断的潜在生物标志物。NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK、XIST-miR-25-3p/miR-129-5p-GZMA 和 TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK 可能是调节早期 RA 疾病进展的潜在 RNA 调控途径。

结论：本研究发现了三个血液/免疫系统特异性表达基因，即 GZMA、PRC1 和 TTK，作为 RA 早期诊断和治疗的潜在生物标志物，并在转录组水平上深入了解了 RA 发病机制。此外，我们提出 NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK、XIST-miR-25-3p/miR-129-5p-GZMA 和 TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK 可能是控制早期 RA 疾病进展的潜在 RNA 调控途径。