School of Public Health, Xi'an Jiaotong University Health Science Center, Key Laboratory of Trace Elements and Endemic Diseases of The National Health and Family Planning Commission, Xi'an, Shaanxi 710061, P.R. China.
School of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712046, P.R. China.
Mol Med Rep. 2019 Jan;19(1):30-40. doi: 10.3892/mmr.2018.9677. Epub 2018 Nov 20.
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.
本研究旨在鉴定类风湿关节炎（RA）和骨关节炎（OA）患者滑膜组织中的潜在新型生物标志物，用于鉴别诊断。从 RA 和 OA 队列的滑膜组织中检索了全基因组表达谱数据集，包括 GSE55235、GSE55457 和 GSE55584 数据集，并使用 R 软件确定 RA 和 OA 之间差异表达基因（DEG；P<0.05；错误发现率<0.05 和倍数变化>2）。对 DEG 进行基因本体论和京都基因与基因组百科全书通路富集分析，以确定与鉴定的 DEG 相关的分子和生化途径，并使用 Cytoscape 软件构建 DEG 的蛋白质-蛋白质相互作用（PPI）网络。使用分子复合物检测算法筛选 PPI 网络中的显著模块和候选驱动基因。通过接收者操作特征和逻辑回归分析评估潜在的生物标志物。在 GSE55235、GSE55457 和 GSE55584 数据集中分别检测到大量的 DEG，分别为 273、205 和 179 个 DEG。其中，在所有三个数据集中有 80 个 DEG 表现出相同的表达趋势，包括 RA 患者中 49 个上调和 31 个下调基因。与 OA 患者相比，RA 患者的 DEG 主要与原发性免疫缺陷途径相关，包括白细胞介素 7 受体（IL7R）和信号转导激活物 1（STAT1）。IL7R+STAT1 区分 RA 与 OA 的灵敏度为 93.94%，特异性为 80.77%。从 GSE36700 数据集分析生成的结果与从 GSE55235、GSE55457 和 GSE55584 数据集分析生成的结果密切相关，进一步验证了上述结果的可靠性。本研究结果表明，滑膜组织中 IL7R 和 STAT1 的表达增加以及原发性免疫缺陷可能与 RA 的发生有关。这些新发现的生物标志物可用于预测疾病的发生，并在临床上区分 RA 和 OA。
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