A novel gene-expression-signature-based model for prediction of response to Tripterysium glycosides tablet for rheumatoid arthritis patients

摘要: Approximately 30% of rheumatoid arthritis (RA) patients treated with Tripterysium glycosides (TG) tablets fail to achieve clinical improvement, implying the essentiality predictive biomarkers and tools. Herein, we aimed identify possible therapeutic effects TG in RA. Gene expression profile peripheral blood mononuclear cells obtained from a discovery cohort was detected by Affymetrix EG1.0 arrays. Then, list candidate gene response were identified integrating differential data analysis signal transduction network analysis. After that, partial-least-squares (PLS) model based on levels RA constructed evaluated using validation cohort. Six (MX1, OASL, SPINK1, CRK, GRAPL RNF2) be predictors therapy. Following construction PLS-based their blood, both 5-fold cross-validation independent dataset validations showed high efficiency this model, demonstrated distinguished improvement PLS-model six biomarkers’ combination over commonly used inflammatory parameters, as well alone, predicting patients’ tablets. This hypothesis-generating study MX1, RNF2 novel targets for intervention, PLS these may have potential prognostic value