College of Pharmacy, Binzhou Medical University, Yantai, China.
College of Life Science, Yantai University, Yantai, China.
Front Immunol. 2023 May 29;14:1157196. doi: 10.3389/fimmu.2023.1157196. eCollection 2023.
Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to self-antigen, autoantibody production, and abnormal immune response. Cuproptosis is a recently reported cell death form correlated with the initiation and development of multiple diseases. This study intended to probe cuproptosis-related molecular clusters in SLE and constructed a predictive model.
We analyzed the expression profile and immune features of cuproptosis-related genes (CRGs) in SLE based on GSE61635 and GSE50772 datasets and identified core module genes associated with SLE occurrence using the weighted correlation network analysis (WGCNA). We selected the optimal machine-learning model by comparing the random forest (RF) model, support vector machine (SVM) model, generalized linear model (GLM), and the extreme gradient boosting (XGB) model. The predictive performance of the model was validated by nomogram, calibration curve, decision curve analysis (DCA), and external dataset GSE72326. Subsequently, a CeRNA network based on 5 core diagnostic markers was established. Drugs targeting core diagnostic markers were acquired using the CTD database, and Autodock vina software was employed to perform molecular docking.
Blue module genes identified using WGCNA were highly related to SLE initiation. Among the four machine-learning models, the SVM model presented the best discriminative performance with relatively low residual and root-mean-square error (RMSE) and high area under the curve (AUC = 0.998). An SVM model was constructed based on 5 genes and performed favorably in the GSE72326 dataset for validation (AUC = 0.943). The nomogram, calibration curve, and DCA validated the predictive accuracy of the model for SLE as well. The CeRNA regulatory network includes 166 nodes (5 core diagnostic markers, 61 miRNAs, and 100 lncRNAs) and 175 lines. Drug detection showed that D00156 (Benzo (a) pyrene), D016604 (Aflatoxin B1), D014212 (Tretinoin), and D009532 (Nickel) could simultaneously act on the 5 core diagnostic markers.
We revealed the correlation between CRGs and immune cell infiltration in SLE patients. The SVM model using 5 genes was selected as the optimal machine learning model to accurately evaluate SLE patients. A CeRNA network based on 5 core diagnostic markers was constructed. Drugs targeting core diagnostic markers were retrieved with molecular docking performed.
系统性红斑狼疮（SLE）是一种自身免疫性疾病，其特征是对自身抗原的耐受性丧失、产生自身抗体和异常免疫反应。铜死亡是一种最近报道的与多种疾病的发生和发展相关的细胞死亡形式。本研究旨在探讨 SLE 中与铜死亡相关的分子聚类，并构建预测模型。
我们基于 GSE61635 和 GSE50772 数据集分析了 SLE 中与铜死亡相关基因（CRGs）的表达谱和免疫特征，并使用加权相关网络分析（WGCNA）确定与 SLE 发生相关的核心模块基因。我们通过比较随机森林（RF）模型、支持向量机（SVM）模型、广义线性模型（GLM）和极端梯度提升（XGB）模型，选择了最佳的机器学习模型。通过列线图、校准曲线、决策曲线分析（DCA）和外部数据集 GSE72326 验证了模型的预测性能。随后，建立了一个基于 5 个核心诊断标志物的 CeRNA 网络。使用 CTD 数据库获取针对核心诊断标志物的药物，并使用 Autodock vina 软件进行分子对接。
WGCNA 确定的蓝色模块基因与 SLE 的发生高度相关。在这四个机器学习模型中，SVM 模型具有最低的残差和均方根误差（RMSE）和最高的曲线下面积（AUC=0.998），表现出最佳的判别性能。基于 5 个基因构建的 SVM 模型在 GSE72326 数据集的验证中表现良好（AUC=0.943）。列线图、校准曲线和 DCA 也验证了该模型对 SLE 的预测准确性。CeRNA 调控网络包括 166 个节点（5 个核心诊断标志物、61 个 miRNA 和 100 个 lncRNA）和 175 条线。药物检测表明，D00156（苯并（a）芘）、D016604（黄曲霉毒素 B1）、D014212（维甲酸）和 D009532（镍）可以同时作用于 5 个核心诊断标志物。
我们揭示了 CRGs 与 SLE 患者免疫细胞浸润之间的相关性。选择使用 5 个基因的 SVM 模型作为最佳机器学习模型，以准确评估 SLE 患者。构建了一个基于 5 个核心诊断标志物的 CeRNA 网络。使用分子对接检索针对核心诊断标志物的药物。
