The Lnc‐ENST00000602558/IGF1 axis as a predictor of response to treatment with tripterygium glycosides in rheumatoid arthritis patients - Yang Gao; Xiaoyue Wang; Yanfeng Gao; Jian Bai; Yanpeng Zhao; Renyi Wang; Hanzhou Wang; Guangzhao Zhu; Xixi Wang; Xiaochen Han; Yanqiong Zhang; Hailong Wang (Immunity, Inflammation and Disease)
Abstract Aims Growing clinical evidence suggests that not all patients with rheumatoid arthritis (RA) benefit to the same extent by treatment with tripterygium glycoside (TG), which highlights the need to identify RA‐related genes that can be used to predict drug responses. In addition, single genes as markers of RA are not sufficiently accurate for use as predictors. Therefore, there is a need to identify paired expression genes that can serve as biomarkers for predicting the therapeutic effects of TG tablets in RA. Methods A total of 17 pairs of co‐expressed genes were identified as candidates for predicting an RA patient's response to TG therapy, and genes involved in the Lnc‐ENST00000602558/GF1 axis were selected for that purpose. A partial‐least‐squares (PLS)‐based model was constructed based on the expression levels of Lnc‐ENST00000602558/IGF1 in peripheral blood. The model showed high efficiency for predicting an RA patient's response to TG tablets. Results Our data confirmed that genes co‐expressed in the Lnc‐ENST00000602558/IGF1 axis mediate the efficacy of TG in RA treatment, reduce tumor necrosis factor‐α induced IGF1 expression, and decrease the inflammatory response of MH7a cells. Conclusion We found that genes expressed in the Lnc‐ENST00000602558/IGF1 axis may be useful for identifying RA patients who will not respond to TG treatment. Our findings provide a rationale for the individualized treatment of RA in clinical settings.