Cuproptosis-related gene identification and immune infiltration analysis in systemic lupus erythematosus

Frontiers in Immunology

Immunology

Yan Lv¹  Yeying Sun²  Min Yu²  Yong Wang²  Yuyong Wu²  Wuquan Li²  Xiaoran Guan² 

[1] College of Life Science, Yantai University, Yantai, China;College of Pharmacy, Binzhou Medical University, Yantai, China;

关键词: systemic lupus erythematosus;    WGCNA;    machine learning;    immune infiltration;    biomarker;   

DOI  :  10.3389/fimmu.2023.1157196

 received in 2023-02-17, accepted in 2023-05-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundSystemic 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.MethodsWe 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.ResultsBlue 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.ConclusionWe 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.

【 授权许可】

Unknown   
Copyright © 2023 Li, Guan, Wang, Lv, Wu, Yu and Sun