Jijia Sun, Baocheng Liu, Ying Yuan, Lei Zhang, Jianying Wang
{"title":"结合生物信息学分析和中药虚拟筛选鉴定类风湿关节炎的疾病标志物和治疗靶点。","authors":"Jijia Sun, Baocheng Liu, Ying Yuan, Lei Zhang, Jianying Wang","doi":"10.31083/j.fbl2709267","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to identify potentially important Rheumatoid arthritis (RA) targets related to immune cells based on bioinformatics analysis, and to identify small molecules of traditional Chinese medicine (TCM) associated with these targets that have potential therapeutic effects on RA.</p><p><strong>Methods: </strong>Gene expression profile data related to RA were downloaded from the Gene Expression Omnibus (GSE55235, GSE55457, and GSE77298), and datasets were merged by the batch effect removal method. The RA key gene set was identified by protein-protein interaction network analysis and machine learning-based feature extraction. Furthermore, immune cell infiltration analysis was carried out on all DEGs to obtain key RA markers related to immune cells. Batch molecular docking of key RA markers was performed on our previously compiled dataset of small molecules in TCM using AutoDock Vina. Moreover, <i>in vitro</i> experiments were performed to examine the inhibitory effect of screened compounds on the synovial cells of an RA rat model.</p><p><strong>Results: </strong>The PPI network and feature extraction with machine learning classifiers identified eight common key RA genes: <i>MYH11</i>, <i>CFP</i>, <i>LY96</i>, <i>IGJ</i>, <i>LPL</i>, <i>CD48</i>, <i>RAC2</i>, and <i>CSK</i>. <i>RAC2</i> was significantly correlated with the infiltration and expression of five immune cells, with significant differences in these immune cells in the normal and RA samples. Molecular docking and <i>in vitro</i> experiments also showed that sanguinarine, sesamin, and honokiol could effectively inhibit the proliferation of RA rat synovial cells, also could all effectively inhibit the secretion of TNF-α and IL-1β in synovial cells, and had a certain inhibitory effect on expression of the target protein <i>RAC2</i>.</p><p><strong>Conclusions: </strong>The core gene set of RA was screened from a new perspective, revealing biomarkers related to immune cell infiltration. Using molecular docking, we screened out TCM small molecules for the treatment of RA, providing methods and technical support for the treatment of RA with TCM.</p>","PeriodicalId":73069,"journal":{"name":"Frontiers in bioscience (Landmark edition)","volume":"27 9","pages":"267"},"PeriodicalIF":3.1000,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Disease Markers and Therapeutic Targets for Rheumatoid Arthritis Identified by Integrating Bioinformatics Analysis with Virtual Screening of Traditional Chinese Medicine.\",\"authors\":\"Jijia Sun, Baocheng Liu, Ying Yuan, Lei Zhang, Jianying Wang\",\"doi\":\"10.31083/j.fbl2709267\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>The aim of this study was to identify potentially important Rheumatoid arthritis (RA) targets related to immune cells based on bioinformatics analysis, and to identify small molecules of traditional Chinese medicine (TCM) associated with these targets that have potential therapeutic effects on RA.</p><p><strong>Methods: </strong>Gene expression profile data related to RA were downloaded from the Gene Expression Omnibus (GSE55235, GSE55457, and GSE77298), and datasets were merged by the batch effect removal method. The RA key gene set was identified by protein-protein interaction network analysis and machine learning-based feature extraction. Furthermore, immune cell infiltration analysis was carried out on all DEGs to obtain key RA markers related to immune cells. Batch molecular docking of key RA markers was performed on our previously compiled dataset of small molecules in TCM using AutoDock Vina. Moreover, <i>in vitro</i> experiments were performed to examine the inhibitory effect of screened compounds on the synovial cells of an RA rat model.</p><p><strong>Results: </strong>The PPI network and feature extraction with machine learning classifiers identified eight common key RA genes: <i>MYH11</i>, <i>CFP</i>, <i>LY96</i>, <i>IGJ</i>, <i>LPL</i>, <i>CD48</i>, <i>RAC2</i>, and <i>CSK</i>. <i>RAC2</i> was significantly correlated with the infiltration and expression of five immune cells, with significant differences in these immune cells in the normal and RA samples. Molecular docking and <i>in vitro</i> experiments also showed that sanguinarine, sesamin, and honokiol could effectively inhibit the proliferation of RA rat synovial cells, also could all effectively inhibit the secretion of TNF-α and IL-1β in synovial cells, and had a certain inhibitory effect on expression of the target protein <i>RAC2</i>.</p><p><strong>Conclusions: </strong>The core gene set of RA was screened from a new perspective, revealing biomarkers related to immune cell infiltration. Using molecular docking, we screened out TCM small molecules for the treatment of RA, providing methods and technical support for the treatment of RA with TCM.</p>\",\"PeriodicalId\":73069,\"journal\":{\"name\":\"Frontiers in bioscience (Landmark edition)\",\"volume\":\"27 9\",\"pages\":\"267\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2022-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in bioscience (Landmark edition)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31083/j.fbl2709267\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioscience (Landmark edition)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31083/j.fbl2709267","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 2
摘要
目的:本研究旨在基于生物信息学分析,鉴定与免疫细胞相关的类风湿关节炎(RA)潜在重要靶点,并鉴定与这些靶点相关的具有潜在治疗作用的中药小分子。方法:从Gene expression Omnibus (GSE55235、GSE55457、GSE77298)下载RA相关基因表达谱数据,采用批效应去除法对数据集进行合并。通过蛋白-蛋白相互作用网络分析和基于机器学习的特征提取,识别RA关键基因集。此外,对所有deg进行免疫细胞浸润分析,获得与免疫细胞相关的关键RA标志物。利用AutoDock Vina对我们之前编制的中药小分子数据集进行了关键RA标记的批量分子对接。此外,我们还进行了体外实验,研究筛选的化合物对RA大鼠滑膜细胞的抑制作用。结果:PPI网络和机器学习分类器特征提取识别出8个常见的RA关键基因:MYH11、CFP、LY96、IGJ、LPL、CD48、RAC2和CSK。RAC2与五种免疫细胞的浸润和表达显著相关,在正常和RA样本中这些免疫细胞的浸润和表达有显著差异。分子对接和体外实验也表明,血根碱、芝麻素、宏木酚均能有效抑制RA大鼠滑膜细胞的增殖,也均能有效抑制滑膜细胞中TNF-α和IL-1β的分泌,并对靶蛋白RAC2的表达有一定的抑制作用。结论:从新的角度筛选了RA核心基因集,揭示了与免疫细胞浸润相关的生物标志物。通过分子对接,筛选出治疗类风湿性关节炎的中药小分子,为中药治疗类风湿性关节炎提供方法和技术支持。
Disease Markers and Therapeutic Targets for Rheumatoid Arthritis Identified by Integrating Bioinformatics Analysis with Virtual Screening of Traditional Chinese Medicine.
Objective: The aim of this study was to identify potentially important Rheumatoid arthritis (RA) targets related to immune cells based on bioinformatics analysis, and to identify small molecules of traditional Chinese medicine (TCM) associated with these targets that have potential therapeutic effects on RA.
Methods: Gene expression profile data related to RA were downloaded from the Gene Expression Omnibus (GSE55235, GSE55457, and GSE77298), and datasets were merged by the batch effect removal method. The RA key gene set was identified by protein-protein interaction network analysis and machine learning-based feature extraction. Furthermore, immune cell infiltration analysis was carried out on all DEGs to obtain key RA markers related to immune cells. Batch molecular docking of key RA markers was performed on our previously compiled dataset of small molecules in TCM using AutoDock Vina. Moreover, in vitro experiments were performed to examine the inhibitory effect of screened compounds on the synovial cells of an RA rat model.
Results: The PPI network and feature extraction with machine learning classifiers identified eight common key RA genes: MYH11, CFP, LY96, IGJ, LPL, CD48, RAC2, and CSK. RAC2 was significantly correlated with the infiltration and expression of five immune cells, with significant differences in these immune cells in the normal and RA samples. Molecular docking and in vitro experiments also showed that sanguinarine, sesamin, and honokiol could effectively inhibit the proliferation of RA rat synovial cells, also could all effectively inhibit the secretion of TNF-α and IL-1β in synovial cells, and had a certain inhibitory effect on expression of the target protein RAC2.
Conclusions: The core gene set of RA was screened from a new perspective, revealing biomarkers related to immune cell infiltration. Using molecular docking, we screened out TCM small molecules for the treatment of RA, providing methods and technical support for the treatment of RA with TCM.