Identification of immune related genes in ankylosing spondylitis based on machine learning
ZHOU Ruozhou1, HU Xin1, FAN Yu1, FANG Zuozhong1, TIAN Jian2, DENG Guobing1
1. Department of Spine Surgery, the First People's Hospital of Chenzhou, Chenzhou 423000, China; 2. Department of Orthopedics, Xiangya Hospital, Central South University, Changsha 410008, China
Abstract：ObjectiveTo explore the diagnostic value and molecular mechanism of immune related characteristic genes in ankylosing spondylitis (AS).MethodsDownload the GSE73756 and GSE25101 datasets from the Gene Expression Omnibus (GEO) database. Combined the weighted gene overexpression network (WGCNA) and the Least absolute shrinkage and selection operator, LASSO) and the Support Vector Machine-recursive feature elimination (SVM-RFE) to screen characteristic genes and evaluate their diagnostic value in AS. Bioinformatics analysis such as functional and pathway enrichment analysis was performed.ResultsFinally, a total of 5 key immune related genes were identified: CD81, IL2RB, CSF3R, IL8, and CCL3L3. The diagnostic model constructed by the combination of five genes has high diagnostic value in both the training set and the test set.ConclusionCD81, IL2RB, CSF3R, IL8 and CCL3L3 may be the diagnostic biomarkers and potential therapeutic targets of AS.
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2023,Vol. 20
