中国中药杂志

2019, v.44(16) 3415-3422

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基于转录组数据挖掘和生物分子网络分析整合的雷公藤多苷片治疗类风湿关节炎疗效标志的发现研究
Identification of biomarkers to response of Tripterygium Glycosides Tablets acting on rheumatoid arthritis by integrating transcriptional data mining and biomolecular network analysis

王晓月;王海隆;毛霞;李光耀;郭秋岩;李玮婕;郭敏群;姜泉;张彦琼;林娜;
WANG Xiao-yue;WANG Hai-long;MAO Xia;LI Guang-yao;GUO Qiu-yan;LI Wei-jie;GUO Min-qun;JIANG Quan;ZHANG Yan-qiong;LIN Na;Guangzhou University of Chinese Medicine;Institute of Chinese Materia Medica,China Academy of Chinese Medical Sciences;Division of Rheumatology,Guang'anmen Hospital,China Academy of Chinese Medical Sciences;

摘要(Abstract):

基于转录组数据挖掘和生物分子网络分析的整合研究策略,发现雷公藤多苷片治疗类风湿关节炎(rheumatoid arthritis,RA)的候选疗效标志,并构建其疗效预测模型。从临床收集接受雷公藤多苷片治疗的RA患者外周血样本,提取外周血单个核细胞(PBMC),整合全基因组表达谱特征分别获得雷公藤多苷片治疗RA疗效差异mRNA 360个(185个上调,175个下调)、miRNA 24个(7上调,17下调)。基于miRanada和Target Scan数据库共获得206个差异miRNA靶基因,建立miRNAs-靶mRNA共表达调控网络及miRNA介导的基因表达调控网络,并通过网络拓扑特征计算,筛选到3个候选疗效标志miRNAs(hsa-miR-4720-5p,hsa-miR-374b-5p,hsa-miR-185-3p)。基于上述3个候选标志在RA患者外周血样本中的表达量,采用偏最小二乘法,构建雷公藤多苷片治疗RA药效预测模型。通过5轮的交叉验证,该模型对雷公藤多苷片治疗RA的疗效预测准确率(ACC)分别为100. 00%,100. 00%,100. 00%,66. 67%,66. 67%,受试者工作特征曲线(receiver operating characteristic curve,ROC)曲线下面积(AUC)均为1. 00,表明该模型的预测性能良好且稳定。雷公藤多苷片治疗RA的疗效预测模型有助于临床筛选适用雷公藤多苷片治疗的RA患者,为临床制定RA个体化精准治疗方案提供一种新型、高效且无创的辅助工具。
Growing clinical evidence shows that a partial rheumatoid arthritis( RA) patient treated with Tripterygium Glycosides Tablets( TGT) may fail to achieve clinical improvement. It is of great clinical significance to predict the therapeutic effect of TGT in RA. Therefore,the aim of the current study was to identify potential biomarkers for TGT treatment in RA. Affymetrix EG1.0 arrays were applied to detect gene expression in peripheral blood mononuclear cells obtained from 6 RA patients( 3 responders and 3 non-responders) treated with TGT. By integrating differential expression data analysis and biomolecular network analysis,360 mRNAs( 185 up-regulated and 175 down-regulated) and 24 miRNAs( 7 up-regulated and 17 down-regulated) which were differentially expressed between TGT responder and non-responder groups were identified. A total of 206 candidate target genes for the differentially expressed miRNAs were obtained based on miRanada and Target Scan databases,and then the miRNA target gene coexpression network and miRNA-mediated gene signal transduction network were constructed. Following the network analyses,three candidate miRNAs biomarkers( hsa-miR-4720-5 p,hsa-miR-374 b-5 p,hsa-miR-185-3 p) were identified as candidate biomarkers predicting individual response to TGT. Partialleast-squares( PLS) was applied to construct a model for predicting response to TGT based on the expression levels of the candidate gene biomarkers in RA patients. The five-fold cross-validation showed that the prediction accuracy( ACC) of this PLS-based model efficacy was 100.00%,100.00%,100.00%,66.67% and 66.67% respectively,and all the area under the receiver operating characteristic curve( AUC) were 1.00,indicating the highly predictive efficiency of this PLS-based model. In conclusion,the integrating transcription data mining and biomolecular network investigation show that hsa-mir-4720-5 p,hsa-mir-374 b-5 p and hsa-mir-185-3 p may be candidate biomarkers predicting individual response to TGT. In addition,the PLS model based on the expression levels of these candidate biomarkers may be helpful for the clinical screen of RA patients,which potentially benefit individualized therapy of RA in a daily clinical setting.

关键词(KeyWords): 类风湿关节炎;雷公藤多苷片;转录组学;生物分子网络;个体化治疗
rheumatoid arthritis;Tripterygium Glycosides Tablets;transcriptomics;biomolecular network;individualized treatment

Abstract:

Keywords:

基金项目(Foundation): 北京市自然科学基金项目(7192139);; 中国中医科学院中医药“一带一路”合作专项(GH2017-06);中国中医科学院基本科研业务费自主选题项目院内联合创新专项(Z2017082)

作者(Authors): 王晓月;王海隆;毛霞;李光耀;郭秋岩;李玮婕;郭敏群;姜泉;张彦琼;林娜;
WANG Xiao-yue;WANG Hai-long;MAO Xia;LI Guang-yao;GUO Qiu-yan;LI Wei-jie;GUO Min-qun;JIANG Quan;ZHANG Yan-qiong;LIN Na;Guangzhou University of Chinese Medicine;Institute of Chinese Materia Medica,China Academy of Chinese Medical Sciences;Division of Rheumatology,Guang'anmen Hospital,China Academy of Chinese Medical Sciences;

DOI: 10.19540/j.cnki.cjcmm.20181031.001

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