Identification of Key Genes in Rheumatoid Arthritis Based on Bioinformatics and Machine Learning

LIU Huajing; LIU Ying; DING Jinya

doi:10.13730/j.issn.2097-2148.2024.04.009

Abstract：Objective To bioinformatics analyze and machine learn the genetic datasets of rheumatoid arthritis(RA),and screen out the potential key genes related to diagnosis and therapeutic targets.Methods The RA related data sets were obtained to screen differentially expressed genes(DEGs).Least absolute shrinkage and selection operator(LASSO)and multiple support vector machine recursive feature elimination(mSVM-RFE)were applied to screen key genes,and the receiver operating characteristic(ROC)curve of key genes was drawn to evaluate the potential value of key genes as diagnostic and therapeutic targets.Results A total of 377 DEGs were screened from the two databases,inclu-ding 266 up-regulated genes and 111 down-regulated genes.Six key genes were identified by two machine learning algo-rithms:HCP5,LRRC15,MREG,SDC1,SLC26A10 and SNX10.ROC curve analysis showed that area under the curve(AUC)of the six key genes above for RA diagnosis in the training set were 0.959,0.945,0.878,0.929,0.882,0.903,all above 0.8.The AUC of the six key genes above in the validation set were 0.821,0.912,0.971,0.997,0.671 and 0.894 respectively,which were all greater than 0.8 except for SLC26A10 gene,indicating that all the six key genes above had high diagnostic value for RA.Conclusion The key genes obtained by bioinformatics analysis and machine learning al-gorithms may be potential diagnostic markers and precision treatment targets for RA.