Department of Clinical Epidemiology and Evidence-Based Medicine, The First Affiliated Hospital, China Medical University, No.155, Nan Jing Bei Street, Shenyang, Liaoning Province, China.
Department of Medical Record Management Center, The First Affiliated Hospital, China Medical University, Shenyang, China.
Clin Rheumatol. 2022 Apr;41(4):1057-1068. doi: 10.1007/s10067-021-05960-9. Epub 2021 Nov 12.
This study was designed to identify the potential diagnostic biomarkers of rheumatoid arthritis (RA) and to explore the potential pathological relevance of immune cell infiltration in this disease.
Three previously published datasets containing gene expression data from 35 RA patients and 29 controls (GSE55235, GSE55457, and GSE12021) were downloaded from the GEO database, after which a weighted correlation network analysis (WGCNA) approach was utilized to clarify differentially abundant genes. Candidate biomarkers of RA were then identified via the use of a LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. Data were validated based upon the area under the receiver operating characteristic curve (AUC) values, with hub genes being identified as those with an AUC > 85% and a P value < 0.05. Lastly, the CIBERSORT algorithm was used to assess immune cell infiltration of RA tissues, and correlations between immune cell infiltration and disease-related diagnostic biomarkers were assessed.
The green-yellow module containing 87 genes was found to be highly correlated with RA positivity. FADD, CXCL2, and CXCL8 were identified as potential RA diagnostic biomarkers (AUC > 0.85), and these results were validated using the GSE77298 dataset. Immune cell infiltration analyses revealed the expression of hub genes to be correlated with mast cells, monocytes, activated NK cells, CD8 T cells, resting dendritic cells, and plasma cells.
These data indicate that FADD, CXCL2, and CXCL8 are valuable diagnostic biomarkers of RA, offering new insight that can guide future studies of RA incidence and progression.
本研究旨在鉴定类风湿关节炎（RA）的潜在诊断生物标志物，并探讨免疫细胞浸润在该病中的潜在病理相关性。
从 GEO 数据库中下载了包含 35 名 RA 患者和 29 名对照者基因表达数据的三个先前发表的数据集（GSE55235、GSE55457 和 GSE12021），然后采用加权相关网络分析（WGCNA）方法来阐明差异丰度基因。接着，使用 LASSO 回归模型和支持向量机递归特征消除（SVM-RFE）分析来鉴定 RA 的候选生物标志物。根据受试者工作特征曲线（ROC）下面积（AUC）值来验证数据，将 AUC 值>85%且 P 值<0.05 的基因鉴定为枢纽基因。最后，使用 CIBERSORT 算法评估 RA 组织中的免疫细胞浸润，并评估免疫细胞浸润与疾病相关诊断生物标志物之间的相关性。
发现与 RA 阳性高度相关的是包含 87 个基因的绿-黄模块。FADD、CXCL2 和 CXCL8 被鉴定为潜在的 RA 诊断生物标志物（AUC>0.85），这些结果使用 GSE77298 数据集进行了验证。免疫细胞浸润分析表明，枢纽基因的表达与肥大细胞、单核细胞、活化 NK 细胞、CD8 T 细胞、静息树突状细胞和浆细胞有关。
这些数据表明，FADD、CXCL2 和 CXCL8 是 RA 的有价值的诊断生物标志物，为 RA 发病和进展的研究提供了新的见解。
