Accumulating clinical evidence show that not all rheumatoid arthritis (RA) patients\nbenefit to the same extent from a Tripterygium wilfordii Hook F (TwHF)-based\ntherapy-Tripterysium glycosides tablets (TG tablets), which emphasizes the need of\npredictive biomarkers and tools for drug response. Herein, we integrated TG tablets�\nresponse-related miRNA and mRNA expression profiles obtained from the clinical\ncohort-based microarray, miRNA target prediction, miRNA-target gene coexpression, as\nwell as gene-gene interactions, to identify four candidate circulating miRNA biomarkers\nthat were predictive of response to TG tablets. Moreover, we applied the support\nvector machines (SVM) algorithm to construct the prediction model for the treatment\noutcome of TG tablets based on the levels of the candidate miRNA biomarkers, and also\nconfirmed its good performance via both 5-fold cross-validation and the independent\nclinical cohort validations. Collectively, this circulating miRNA-based biomarker model\nmay assist in screening the responsive RA patients to TG tablets and thus potentially\nbenefit individualized therapy of RA in a daily clinical setting.
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