雷公藤是治疗类风湿关节炎（RA）的主要药物，以疗效确切、见效快而引起国内外广泛关注研究，但因其疗效个体差异大，临床用药难以精确把握等问题，严重阻碍其临床使用及走向国际。前期我们基于全基因组表达谱扫描技术，获得一批与雷公藤治疗RA疗效相关的差异表达lncRNAs和mRNAs，并初步证实两者之间的关联性。本项目拟在前期工作基础上构建雷公藤治疗RA疗效相关lncRNA-mRNA共表达调控网络，筛查其疗效个体差异的生物标志物，重新选取雷公藤治疗的RA患者作为样本训练集，建立基于lncRNA分子的雷公藤个体化治疗RA的预测模型，并用独立、大样本测试集，对雷公藤治疗RA疗效预测模型的性能进行评估，同时对其疗效个体差异相关lncRNAs的生物学功能进行验证。从lncRNA-mRNA共表达调控网络揭示雷公藤治疗RA疗效个体差异的科学内涵，为RA的个体化诊疗提供新的思路和模式，推动医学向精准医疗方向加速发展

Rheumatoid arthritis(RA) is a common disorder worldwide. RA mainly charcterized by the chronic inflammation of articuar synovium, and the imbalance of gene network plays an important role in the occurrence of rheumatoid arthritis, migration and delay of inflammation, and the joint destroy. .Tripterygium wilfordii Hook F（TWHF） is an important class of drugs used in the treatment of RA. TWHF has the functions of anti-inflammation and immune inhibition, etc. It has been used at present to treat various autoimmune diseases including RA, and has obtained outstanding therapeutic effects. Therefore, there is a need to identify biomarkers that might help to predict treatment outcomes before antidepressant therapy is initiated. This approach may also provide greater insight into drug response mechanisms..However, many patients do not respond adequately to TWHF therapy. The emerging field of pharmacometabolomics is focused on metabolomic signatures for drug exposure and/or efficacy, with the goal of using these signatures to achieve better individualization of drug therapy. Pharmacogenomics shares the goals of pharmacometabolomics but utilizes genomic rather than metabolomic data. .There is little pharmacogenetics study of TWHF has been performed. Many pharmacogenetics studies of antide these candidate gene–based studies, and even recently published genome-wide association studies, have failed to provide reliable biomarkers to predict outcomes of treatment with TWHF..In our previous study, peripheral blood mononuclear cells (PBMC) were isolated from samples of 12 RA patients were collected. We performed the study that using genome-wide scanning technique to analysis RNA expression in RA patient treating with TwHF. Then two groups of the lncRNA-mRNA libraries were constructed respectively. Massively parallel sequencing was used to screen the differentially expressed lncRNAs and mRNAs, and lncRNAs expression profile of RA was established. According to RA disease activity index, RA patients are divided into the TWHF good respond group and TWHF bad respond group, 6 cases each Group. Total ncRNA was extracted were used for ncRNA array using microarray kit of EG1.0ST (Affymetrix_Exon Genminix_lncRNA from Arrymetrix. Query the lncRNAs gene locus and biological function and other information by Target Scan Human Custom, UCSC database, Refseq database, KEGG database with literature from pubmed. Key genes lncRNA-mRNA-coexpression-network was validated through Realtime-PCR. .We analyzed Gene Go Meta-Core software and choose the key genes of lncRNA. We establish a model based on lncRNAs that can predict the effect of RA treating with TWHF. We include other 40 and 100 RA patients treating with TwHF separately. qRT-PCR was used to verify the lncRNA-mRNA-coexpression-network. Functional study was also performed to verify the lncRNA-mRNA-coexpression-network. Validations of individualized drug therapy performance show the classifier and area under ROC curve, and the network topological features integrated into this classifier contribute greatly to improving the predictive performance..In this study, we observed the changes of lncRNA-mRNA-coexpression-network in PBMC with rheumatoid arthritis, and investigated the effects of TWHF.