基于加权基因共表达网络分析(WGCNA)和套索(LASSO)算法的类风湿关节炎关键生物标志物识别及免疫浸润分析

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm.

作者信息

Jiang Fan, Zhou Hongyi, Shen Haili

机构信息

Second Clinical Medical College, Lanzhou University, Lanzhou, China.

Department of General Medicine, Beijing Luhe Hospital, Capital Medical University, Beijing, China.

出版信息

Front Immunol. 2022 Jun 29;13:925695. doi: 10.3389/fimmu.2022.925695. eCollection 2022.

Abstract

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

摘要

类风湿性关节炎(RA)是最常见的炎性关节炎,也是发病和死亡的重要原因。RA患者的滑膜炎症包含多种基因和信号通路,目前人们对其了解甚少。本研究的目的是发现与RA病程相关的主要生物标志物,以及它们与免疫细胞浸润的关联。利用基因表达综合数据库下载基因微阵列数据。采用差异表达分析、加权基因共表达网络分析(WGCNA)和最小绝对收缩和选择算子(LASSO)回归来识别RA的核心标志物。使用单样本基因集富集分析(GSEA)来检测28种免疫细胞的浸润水平及其与核心基因标志物的关联。通过逆转录-聚合酶链反应(RT-PCR)验证核心基因在RA-人成纤维样滑膜细胞(RA-HFLS)和人成纤维样滑膜细胞(HFLS)中的表达。应用细胞计数试剂盒-8(CCK-8)检测法来确定核心基因在RA中的作用。在本研究中,我们在RA中鉴定出21个差异表达基因(DEG)。WGCNA产生了两个共表达模块,其中一个与RA表现出最强的关联。结合差异基因,共发现6个交集基因。对数据进行LASSO分析后,确定了6个核心基因作为RA的潜在生物标志物。通过ROC曲线分析发现,三个核心基因,即细胞周期蛋白依赖性激酶2(CKS2)、胱抑素A(CSTA)和淋巴细胞抗原96(LY96)具有较高的诊断价值。结果表明它们与几种免疫细胞的浓度密切相关。RT-PCR证实,与HFLS细胞相比,CKS2、CSTA和LY96在RA-HFLS细胞中的表达明显上调。更重要的是,敲低CKS2可抑制RA-HFLS细胞的增殖。总体而言,预计CKS2、CSTA和LY96将有助于RA的诊断和治疗,上述免疫细胞浸润可能对该疾病的发生和发展产生重大影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780b/9277141/664130344e89/fimmu-13-925695-g001.jpg

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