Objectives. Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease characterized by chronic spinal inflammation, arthritis, gut inflammation, and enthesitis. We aimed to identify the key biomarkers related to immune infiltration and osteoclast differentiation in the pathological process of AS by bioinformatic methods. Methods. GSE25101 from the Gene Expression Omnibus was used to obtain AS-associated microarray datasets. We performed bioinformatics analysis using R software to validate different expression levels. The purpose of the GO and KEGG enrichment analyses of DEGs was to exclude key genes. Using weighted correlation network analysis (WGCNA), we examined all expression profile data and identified differentially expressed genes. The objective was to investigate the interaction between genetic and clinical features and to identify the essential relationships underlying coexpression modules. The CIBERSORT method was used to make a comparison of the immune infiltration in whole blood between the AS group and the control group. The WGCNA R program from Bioconductor was used to identify hub genes. RNA extraction reverse transcription and quantitative polymerase chain reaction were conducted in the peripheral blood collected from six AS patients and six health volunteers matched by age and sex. Results. 125 DEGs were identified, consisting of 36 upregulated and 89 downregulated genes that are involved in the cell cycle and replication processes. In the WGCNA, modules of MCODE with different algorithms were used to find 33 key genes that were related to each other in a strong way. Immune infiltration analysis found that naive CD4+ T cells and monocytes may be involved in the process of AS. PLCG2 and IFNAR1 genes were obtained by screening genes meeting the conditions of immune cell infiltration and osteoclast differentiation in AS patients among IGF2R, GRN, SH2D1A, LILRB3, IFNAR1, PLCG2, and TNFRSF1B. The results demonstrated that the levels of PLCG2 mRNA expression in AS were considerably higher than those in healthy individuals (). IFNAR1 mRNA expression levels were considerably lower in AS than in healthy individuals (). Conclusions. Dysregulation of PLCG2 and IFNAR1 are key factors in disease occurrence and development of AS through regulating immune infiltration and osteoclast differentiation. Explaining the differences in immune infiltration and osteoclast differentiation between AS and normal samples will contribute to understanding the development of spondyloarthritis.

中文翻译：

---

PLCG2 和 IFNAR1：外周血中强直性脊柱炎免疫浸润和破骨细胞分化介导的潜在生物标志物

目标。强直性脊柱炎（AS）是一种慢性炎症性风湿性疾病，以慢性脊柱炎症、关节炎、肠道炎症和附着点炎为特征。我们的目的是通过生物信息学方法鉴定AS病理过程中与免疫浸润和破骨细胞分化相关的关键生物标志物。方法。Gene Expression Omnibus 中的 GSE25101 用于获取 AS 相关微阵列数据集。我们使用 R 软件进行生物信息学分析来验证不同的表达水平。DEGs的GO和KEGG富集分析的目的是排除关键基因。使用加权相关网络分析（WGCNA），我们检查了所有表达谱数据并鉴定了差异表达基因。目的是研究遗传和临床特征之间的相互作用，并确定共表达模块背后的基本关系。采用CIBERSORT法比较AS组与对照组全血免疫浸润情况。Bioconductor 的 WGCNA R 程序用于识别枢纽基因。对 6 名 AS 患者和 6 名年龄和性别匹配的健康志愿者采集的外周血进行 RNA 提取逆转录和定量聚合酶链反应。结果。鉴定出 125 个 DEG，由参与细胞周期和复制过程的 36 个上调基因和 89 个下调基因组成。在WGCNA中，使用具有不同算法的MCODE模块来找到33个彼此密切相关的关键基因。免疫浸润分析发现幼稚CD4+T细胞和单核细胞可能参与AS的过程。通过在IGF2R、GRN、SH2D1A、LILRB3、IFNAR1、PLCG2、TNFRSF1B中筛选符合AS患者免疫细胞浸润和破骨细胞分化条件的基因，获得PLCG2和IFNAR1基因。结果表明，AS 中 PLCG2 mRNA 的表达水平显着高于健康个体（）。AS 中的 IFNAR1 mRNA 表达水平显着低于健康个体（）。 结论。PLCG2和IFNAR1的失调通过调节免疫浸润和破骨细胞分化是AS疾病发生和发展的关键因素。解释 AS 和正常样本之间免疫浸润和破骨细胞分化的差异将有助于了解脊柱关节炎的发展。