| 引用本文: | 商泽斌,邱琬婷,凌燕筠,王光辉,张强.基于网络药理学与生物信息学揭示冬凌草调控乳腺癌能量代谢的作用及分子机制[J].中国现代应用药学,2023,40(11):1441-1453. |
| SHANG Zebin,QIU Wanting,LING Yanjun,WANG Guanghui,ZHANG Qiang.Uncovering the Effect and Molecular Mechanism of Rabdosia Rubescens in Regulating Energy Metabolism in Breast Cancer Based on Network Pharmacology and Bioinformatics[J].Chin J Mod Appl Pharm(中国现代应用药学),2023,40(11):1441-1453. |
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| 摘要: |
| 目的 基于网络药理学结合生物信息学、分子对接探究冬凌草调控乳腺癌能量代谢的作用及分子机制。方法 通过多个数据库结合文献查找冬凌草的有效成分，筛选已报道的冬凌草中具有抗肿瘤作用并符合类药五原则的活性成分；利用PubChem数据库、PharmMapper数据库收集冬凌草抗肿瘤活性成分的结构及其潜在靶点。从TCGA和GEO数据库获取乳腺癌样本转录组数据，通过差异表达基因分析及WGCNA挖掘乳腺癌相关靶点，并与冬凌草抗肿瘤活性成分预测靶点匹配映射，获得两者的共同靶点，之后进行GO和KEGG富集分析。使用STRING数据库对共同靶点构建PPI网络，通过Cytoscape软件对网络进行可视化，使用DEGREE和MCC 2种算法分别取PPI网络中排名前10的靶点，两者去重后与富集排名靠前的能量代谢相关通路和生物学过程中的靶点取交集，得到能量代谢相关的关键靶点。随后，通过UALCAN数据库分析能量代谢相关的关键靶点的表达量，利用TIMER数据库分析这些靶点与免疫浸润的关系。最后，将这些靶点与有预测关系的活性成分进行分子对接。结果 选取冬凌草中冬凌草甲素、冬凌草乙素、鲁山冬凌草丙素、β-蒎烯、D-柠檬烯、太白乌霉素A及信阳冬凌草素J共7个活性成分及其潜在靶点368个，与乳腺癌相关靶点映射得到共同靶点34个。GO和KEGG富集分析发现大部分排名靠前的功能/通路与糖、脂代谢相关，从这些功能/通路得到PCK1、ADH1C、AKR1C3、HSD11B1、MAOA、ALDH2、RBP4这7个能量代谢相关的关键靶点，这些靶点在乳腺癌中的mRNA、蛋白水平的表达量均低于正常组织，且均与肿瘤纯度呈负相关；此外，CD4+T细胞、CD8+T细胞等免疫细胞的浸润程度也会影响乳腺癌患者的预后。分子对接结果表明上述7个有效成分与其有预测关系的靶点有良好结合潜力。结论 冬凌草抗乳腺癌具有多成分、多靶点、多信号通路协同作用的特征，其可能通过调控乳腺癌的能量代谢实现清热解毒之功，从而发挥抗乳腺癌作用。 |
| 关键词: 乳腺癌 冬凌草 网络药理学 生物信息学 能量代谢 肿瘤免疫微环境 |
| DOI：10.13748/j.cnki.issn1007-7693.20221943 |
| 分类号:R285.5 |
| 基金项目:国家自然科学基金项目(82104461) |
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| Uncovering the Effect and Molecular Mechanism of Rabdosia Rubescens in Regulating Energy Metabolism in Breast Cancer Based on Network Pharmacology and Bioinformatics |
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SHANG Zebin1, QIU Wanting2, LING Yanjun1, WANG Guanghui2, ZHANG Qiang1
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1.Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China;2.Xiamen University, Xiamen 361102, China
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| Abstract: |
| OBJECTIVE To investigate the effect and molecular mechanism of Rabdosia rubescens in regulating energy metabolism in breast cancer based on the combination of network pharmacology, bioinformatics and molecular docking. METHODS The active ingredients of Rabdosia rubescens were found through multiple databases and literature, and screened the reported ingredients with the antitumor effects and in accordance with the Lipinski rules. By using the PubChem database and PharmMapper database, the structures of the active ingredients with the antitumor activities from Rabdosia rubescens and their potential targets were collected. In addition, the transcriptome data of breast cancer samples were obtained from TCGA and GEO databases, and the targets related to breast cancer were identified by differentially expressed genes analysis and WGCNA. After mapping with predicted targets of the active ingredients of Rabdosia rubescens, intersecting targets were identified and then analyzed by GO enrichment and KEGG analysis. The PPI network was constructed by using the STRING database for the intersecting targets. Network visualization was performed by Cytoscape software, and the top 10 targets of the PPI network were acquired by using two algorithms, DEGREE and MCC respectively. Key targets related to energy metabolism were obtained after eliminating duplicate targets obtained by both algorithms and intersecting with the top-ranked targets from the enrichment analysis regarding pathways and energy metabolism-related biological processes. Subsequently, the expression of energy metabolism-related key targets was analyzed by the UALCAN database, and the relationship between these targets and immune infiltration was analyzed by the TIMER database. Lastly, molecular docking was utilized between these targets and active ingredients that had predicted interactions. RESULTS Seven active ingredients of Rabdosia rubescens, including Oridonin, Ponicidin, Lushanrubescinsin C, Beta-Pinene, D-Limonene, Taibairubescensin A, Xindongnin J, and 368 potential targets were selected, 34 common targets were identified by mapping breast cancer-related targets. GO and KEGG enrichment analysis revealed that most of the top-ranked functions/pathways were involved with glucose and lipid metabolism. Seven energy metabolism-related key targets, PCK1, ADH1C, AKR1C3, HSD11B1, MAOA, ALDH2, and RBP4, were obtained from these functions/pathways. Meantime, the expression of these targets at the mRNA and protein levels in breast cancer was lower than those in normal tissues, and all of them were negatively correlated with tumor purity. Furthermore, the degree of immune cell infiltration such as CD4+ T cells and CD8+ T cells affected the prognosis of patients with breast cancer. The results of molecular docking showed that the above 7 active ingredients had great binding potential with the predicted targets. CONCLUSION Rabdosia rubescens exhibits anti-breast cancer effects through multi-component, multi-target, and multi-pathway, which may be achieved by regulating the energy metabolism of breast cancer to clear heat and detoxify. |
| Key words: breast cancer Rabdosia rubescens network pharmacology bioinformatics energy metabolism tumor immune microenvironment |
