目的:探讨Anoikis相关基因之间的潜在关联,负责防止异常细胞增殖,类风湿性关节炎(RA)。方法:数据集GSE89408,GSE198520和GSE97165从GEO的282例RA患者和28例健康对照中获得。我们对所有基因和HLA基因进行了差异分析。我们进行了蛋白质-蛋白质相互作用网络分析,并基于STRING和cytoscape鉴定了hub基因。对疾病进行了分组,进行了一致的聚类。SsGSEA用于计算免疫细胞浸润。采用Spearman相关分析来确定相关性。用ssGSEA算法计算GO和KEGG的富集分数。WGCNA和DGIdb数据库用于挖掘hub基因与药物的相互作用。结果:两组间差异表达Anoikis相关基因26个(FDR=0.05,log2FC=1),HLA基因表达差异(P<0.05)。在差异表达基因之间观察到蛋白质-蛋白质相互作用,PIM2与RAC2的相关性最高;疾病组大多数免疫细胞类型与正常对照组的免疫细胞浸润程度差异有统计学意义(P<0.05)。Anoikis相关基因与HLA基因高度相关。基于Anoikis相关基因的表达,RA患者分为两种疾病亚型(cluster1和cluster2)。发现59个差异表达的Anoikis相关基因,在功能富集方面表现出显著差异,免疫细胞浸润程度,HLA基因表达(P<0.05)。Cluster2在所有方面的水平均明显高于Cluster1。共表达网络分析显示,cluster1有51个hub差异表达基因,cluster2有72个hub差异表达基因。其中,cluster1的三个枢纽基因与187种药物相互连接,cluster2的五个hub基因与57种药物相互关联。结论:我们的研究确定了Anoikis相关基因与RA之间的联系,并根据Anoikis相关基因表达确定了两种不同的RA亚型。值得注意的是,Cluster2可能代表RA的更严重状态。
Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman\'s correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes\' interactions with drugs. Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA.