基于 WGCNA 和机器学习的类风湿关节炎血小板相关特征诊断模型。

Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning.

机构信息

School of Clinical Medicine, Peking Union Medical College, Beijing, China.

Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

出版信息

Front Immunol. 2023 Jun 23;14:1204652. doi: 10.3389/fimmu.2023.1204652. eCollection 2023.

BACKGROUND AND AIM

Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers.

METHODS

We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC).

RESULTS

The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979.

CONCLUSION

We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.

背景与目的

类风湿关节炎（RA）是一种自身炎症性疾病，可能导致严重的残疾。由于需要具有可靠性和效率的生物标志物，RA 的诊断受到限制。血小板深度参与 RA 的发病机制。我们的研究旨在确定潜在机制并筛选相关生物标志物。

方法

我们从 GEO 数据库中获得了两个微阵列数据集（GSE93272 和 GSE17755）。我们进行了加权相关网络分析（WGCNA），以分析从 GSE93272 中鉴定的差异表达基因的表达模块。我们使用 KEGG、GO 和 GSEA 富集分析来阐明血小板相关特征（PRS）。然后，我们使用 LASSO 算法开发诊断模型。然后，我们使用 GSE17755 作为验证队列，通过操作接收者操作曲线（ROC）来评估诊断性能。

结果

WGCNA 的应用导致鉴定出 11 个不同的共表达模块。值得注意的是，模块 2 在分析的差异表达基因（DEGs）中与血小板表现出明显的关联。此外，使用 LASSO 系数构建了由六个基因（MAPK3、ACTB、ACTG1、VAV2、PTPN6 和 ACTN1）组成的预测模型。PRS 模型在两个队列中均表现出出色的诊断准确性，曲线下面积（AUC）值分别为 0.801 和 0.979。