Analysis of Cuproptosis-associated Genes in Rheumatoid Arthritis Based on Bioinformatics and Screening Prediction of Traditional Chinese Medicine
Objective To study the molecular patterns and diagnostic biomarkers of cuproptosis-associated in rheumatoid ar-thritis(RA)by bioinformatics and predict the potential therapeutic effect of traditional Chinese medicine.Methods The expres-sion profiles of GSE55235,GSE55457 and GSE77298 were downloaded from Gene Expression Omnibus database as the training set.Cuproptosis-associated related genes were extracted for analysis.The differentially expressed cuproptosis-associated gene(DEC AG)between RA patients and healthy controls was selected.DEC AG related immune infiltration and biological function were analyzed.RA patients were classified by consensus clustering according to the expression of DEC AG.Weighted gene co-expression network analysis(WGCN A)was constructed based on genotyping to identify core modules and core genes.GO analysis and KEGG analysis were performed on the top 100 core genes of degree value and the training model was constructed,including Random Forest(RF)and Support Vector Machine(SVM),eXtreme Gradient Boosting(XGB)and Generalized Linear Model(GLM).Five genes most related to RA characteristics were screened and verified as diagnostic biomarkers.Finally,Chinese medicine prediction was carried out.Results SixDECAGs(NLRP3,SLC31 A1,LIAS,CDKN2A,DBT,DLST)were obtained.RA genes were classified into two isoforms(C1,C2)using a consensus clustering method,and 418 core genes were obtained by typing WGCNA.The machine learning model was constructed by taking the top 100 core genes of degree value.Based on the analysis of the reverse cumulative distribution map | residual | and the box plot of | residual |,it was found that the SVM model maintaineds the lowest | residual | distribution compared with the other three models.From the overall Receiver operating characteristic curve(ROC)analysis,the SVM model had a higher area under the curve(AUC)value(AUC:0.966)than the other three models.Taken together,SVM mode was the most appropriate training model.Five genes(TMOD3,PIK3CG,WASL,FGF4,GSN)mostly related to RA characteristics were obtained.Based on the expression levels of five RA characteristic genes,a clinical application nomogram was established,and the decision curve analysis(DCA)diagram and correction curve diagram also showed good predic-tion accuracy.Herbal predictions showed that Yujin(Curcumae Radix),Ezhu(Curcumae Rhizoma)and Honghua(Carthami Flos)were most likely to have therapeutic effects in RA.Conclusion Cuproptosis-associated plays an important role in the oc-currence and diagnosis of RA.
万方数据
维普
