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Volume 41 Issue 3
Mar.  2023
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MENG Xiangqing, LI Lihua, WANG Hongrui, JIA Dan, JIA Min. Anti-tumor mechanism study on saffron by network pharmacology and reverse molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(3): 160-167, 196. doi: 10.12206/j.issn.2097-2024.202206066
Citation: MENG Xiangqing, LI Lihua, WANG Hongrui, JIA Dan, JIA Min. Anti-tumor mechanism study on saffron by network pharmacology and reverse molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(3): 160-167, 196. doi: 10.12206/j.issn.2097-2024.202206066

Anti-tumor mechanism study on saffron by network pharmacology and reverse molecular docking

doi: 10.12206/j.issn.2097-2024.202206066
  • Received Date: 2022-06-15
  • Rev Recd Date: 2022-10-28
  • Available Online: 2023-07-14
  • Publish Date: 2023-03-25
  •   Objective  To explore the anti-tumor mechanism of saffron (Crocus sativus L.) by network pharmacology and reverse molecular docking techniques.   Methods   The main chemical components of saffron were obtained by searching published literature and TCMSP database. The potential targets of these components were predicted using PharmMapper server. The corresponding target genes were identified from UniProt database. The underlying anti-tumor targets of saffron were obtained by mapping the disease genes of cancer or tumor with GeneCards, OMIM and TTD databases. Cytoscape software was used to construct the action target network of saffron active components. The protein-protein interaction analysis was performed by String database, and the GO function and KEGG pathway enrichment analysis were performed by Metascape platform. Finally, molecular docking was performed to evaluate the binding of main components with their potential targets.   Results   A total of 9 active ingredients in saffron including quercetin, kaempferol, isorhamnetin, picrocrocin and crocin I, were identified, which might act on 37 key targets including AKT1, CCND1, MMP9, EGFR, TP53, involved in P53, TNF and other signaling pathways. Molecular docking indicated modest binding potency through hydrogen bonding, and hydrophobic interactions.   Conclusion  The anti-tumor effect of saffron was evaluated via the network of components-targets-pathways, which might provide a foundation for further research.
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Anti-tumor mechanism study on saffron by network pharmacology and reverse molecular docking

doi: 10.12206/j.issn.2097-2024.202206066

Abstract:   Objective  To explore the anti-tumor mechanism of saffron (Crocus sativus L.) by network pharmacology and reverse molecular docking techniques.   Methods   The main chemical components of saffron were obtained by searching published literature and TCMSP database. The potential targets of these components were predicted using PharmMapper server. The corresponding target genes were identified from UniProt database. The underlying anti-tumor targets of saffron were obtained by mapping the disease genes of cancer or tumor with GeneCards, OMIM and TTD databases. Cytoscape software was used to construct the action target network of saffron active components. The protein-protein interaction analysis was performed by String database, and the GO function and KEGG pathway enrichment analysis were performed by Metascape platform. Finally, molecular docking was performed to evaluate the binding of main components with their potential targets.   Results   A total of 9 active ingredients in saffron including quercetin, kaempferol, isorhamnetin, picrocrocin and crocin I, were identified, which might act on 37 key targets including AKT1, CCND1, MMP9, EGFR, TP53, involved in P53, TNF and other signaling pathways. Molecular docking indicated modest binding potency through hydrogen bonding, and hydrophobic interactions.   Conclusion  The anti-tumor effect of saffron was evaluated via the network of components-targets-pathways, which might provide a foundation for further research.

MENG Xiangqing, LI Lihua, WANG Hongrui, JIA Dan, JIA Min. Anti-tumor mechanism study on saffron by network pharmacology and reverse molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(3): 160-167, 196. doi: 10.12206/j.issn.2097-2024.202206066
Citation: MENG Xiangqing, LI Lihua, WANG Hongrui, JIA Dan, JIA Min. Anti-tumor mechanism study on saffron by network pharmacology and reverse molecular docking[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(3): 160-167, 196. doi: 10.12206/j.issn.2097-2024.202206066
  • 西红花为名贵药材,来源于鸢尾科植物番红花Crocus sativus L.的干燥柱头。原产于地中海地区、希腊、小亚细亚和伊朗,后经西藏传入国内,故又名藏红花[1]。《本草纲目》中记载番红花“主治心忧郁积、气闷不散,活血,亦治惊悸”[2]。2020版《中国药典》描述西红花具有活血化瘀、凉血解毒、解郁安神的功效[3]。越来越多的现代药理研究表明,西红花具有抗肿瘤、抗血小板聚集与凋亡、抗心血管细胞凋亡、降血脂和降血糖等活性[46],在健康和医疗领域具有重要作用。

    世界卫生组织国际癌症研究机构(IARC)发布的最新数据,2020年全球癌症新发患者病例数超过1 930万例,癌症死亡患者接近1 000万例[7]。天然活性成分是抗肿瘤药物研发的重要来源[8]。有研究表明,西红花中特有的西红花酸、西红花苷等具有抗肿瘤活性[9],已有学者在西红花治疗结直肠癌、乳腺癌等的抗肿瘤作用方面进行了相关研究[10-11],但其主要活性成分及抗肿瘤作用机制仍需进一步探索。

    网络药理学[12]将系统生物学、生物信息学、计算生物学、网络科学和靶向药理学相结合,从系统层次和生物网络的整体角度探讨成分—靶标—通路的相互作用关系,为中药多靶点、多成分、系统性、整体性的作用机制研究提供了有力的技术支撑,从而指导新药研发和临床诊疗。因此,本研究应用网络药理学结合反向分子对接的方法,对西红花的抗肿瘤作用成分及靶点机制进行研究,为深入探索西红花抗肿瘤药效物质基础及作用机制提供参考。

    • 利用TCMSP平台获取西红花化学成分,口服生物利用度(oral bioavailability,OB)和类药性(drug-likeness,DL) 是药物筛选的关键参数,一般设置OB≥30%和DL≥0.18的化学成分作为候选药效成分,并结合文献报道[1315]补充4个西红花化学成分。

    • 应用TCMSP平台和PharmMapper[16]工具获取西红花活性成分的作用靶点,并借助UniProt数据库将靶点转换为对应基因。以“tumor”、“cancer”为关键词,在GeneCards(https://www.genecards.org/)、OMIM数据库(https://www.omim.org/)和TTD数据库(http://db.idrblab.net/ttd/)进行检索。将得到的疾病靶点和药物靶点取交集,作为药物作用于疾病的预测靶点。

    • 根据预测的西红花药效成分、交集靶点,使用Cytoscape 3.9.1软件建立“成分-靶点”的网络图。

    • 将药物疾病交集靶点输入String数据库构建PPI网络进行初步筛选,再将PPI网络导入Cytoscape 3.9.1中,以半数degree为参考标准,选取关键靶点。

    • 将筛选获得的37个核心靶点录入Metascape平台(http://metascape.org/gp/index.html),物种设置为人,选择Custom Analysis,设置P<0.01,进行基因本体(gene ontology,GO)功能富集分析及京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)通路富集分析。

    • 将筛选出的西红花主要活性成分通过PubChem下载SDF格式;利用RCSB PDB数据库下载关键蛋白靶点,优先选择有配体、结构相对完整的晶体结构,并采用AutoDock Tools对获取的PDB蛋白分子进行除水、加氢、计算电荷预处理;使用AutoDock Vina进行分子对接,计算结合能;选取最优构象,使用PyMOL软件做出3D结合模式图。

    • 通过TCMSP获得70个西红花化学成分,设置OB≥30%且DL≥0.18进行筛选,再添加文献检索相关成分,共获得9个西红花活性成分,见表1

      序号化合物编号化合物英文名中文名OB (%)DL
      1MOL001389n-heptanal庚醛79.740.59
      2MOL001406crocetin西红花酸35.30.26
      3MOL000354isorhamnetin异鼠李素49.60.31
      4MOL000422kaempferol山柰酚41.880.24
      5MOL000098quercetin槲皮素46.430.28
      6MOL001405crocin Ⅰ西红花苷Ⅰ2.540.12
      7MOL001407crocin Ⅱ西红花苷Ⅱ1.650.21
      8MOL000720safranal藏红花醛39.560.04
      9MOL001409picrocrocin苦番红花素33.710.04
    • 将TCMSP平台和PharmMapper获取结果进行整理,并借助UniProt数据库进行靶基因匹配,获得201个潜在靶点。以“tumor”和“cancer”为关键词,在GeneCards、OMIM和TTD数据库进行预测整理,剔除重复,筛选得到5896个潜在疾病靶点。将得到的疾病靶点和药物靶点取交集,共得到可作为药物作用于疾病的179个预测靶点。

    • 将西红花的9个活性成分与预测到的179个潜在靶点导入Cytoscape 3.9.1软件,构建“药物-活性成分-靶点”网络(图1),网络中绿色代表药物作用于疾病的靶点,蓝色代表西红花活性成分,全图包括189个节点、299条边,其中degree值排名靠前的活性成分为槲皮素、山柰酚、异鼠李素、苦番红花素和西红花苷Ⅰ,这些可能是西红花发挥抗肿瘤作用的潜在活性成分。

    • 将疾病与活性成分的潜在靶点导入String数据库,采用Cytoscape 3.9.1软件绘制PPI网络图,依据degree值进行排序,以大于半数degree值为标准进行两次筛选,获取核心靶点37个(图2)。度值排名前5的靶点分别为EGF、MMP9、NFKBIA、IL-1B和IL-10,提示这些靶点可能是西红花发挥抗肿瘤作用的关键潜在靶点。

    • GO分析常用于注释基因和基因产物生物功能,分析包括生物过程(biological process,BP)、分子功能(molecular function,MF)和细胞组成(cellular component,CC)三部分。此次GO富集分析共得到BP富集结果193个、CC富集结果83个和MF富集结果123个,选取排名前10的条目绘制GO功能分析图(图3)。如图3所示,BP主要涉及对激素的反应、对脂质的反应、对异源刺激的反应等;CC主要涉及膜筏、膜微区、囊腔、细胞质囊泡腔等;MF主要涉及转录因子结合、DNA结合转录因子结合、RNA聚合酶Ⅱ特异性DNA结合转录因子结合等。通过比较发现,细胞生物过程富集的基因数较多,说明西红花可能主要通过调节生物过程发挥抗肿瘤作用。

      KEGG分析共富集到194条信号通路,其中34条癌症相关通路,并对前20条通路绘制气泡图(图4)。依据KEGG分析,西红花可能通过p53信号通路、TNF通路发挥抗肿瘤作用,可能对膀胱癌、胰腺癌、前列腺癌、非小细胞肺癌等肿瘤具有治疗作用,西红花靶点-通路相互作用网络见图5,红色三角形代表与肿瘤相关的信号通路,蓝色矩形代表关键靶点。其中,西红花通过膀胱癌信号通路调控EGF、MMPs、Raf、VEGF、ERK等基因发挥抗肿瘤作用(图6),红色矩形代表西红花可能干预的关键靶点。

    • 将前15个潜在核心靶点与西红花活性成分进行分子对接。结合能(affinity)<0表明配体分子能够与受体蛋白自发结合,结合能≤−17.78 kJ/mol表明配体与受体有一定的结合活性,结合能≤−20.92 kJ/mol 表明配体与受体有较好的结合活性,结合能≤−29.29 kJ/mol 表明配体与受体有强的结合活性[17],且结合能越低,表明对接的效果越好,结合的构象越稳定[18]。经AutoDock Vina对接,将得到的结合能数据使用热图展示(图7)。本研究结合自由能小于−20.92kJ/mol 的活性成分有102个,占75.6%;小于−29.29kJ/mol 的活性成分有73个,占54.1%,可见这些核心化合物与受体结合活性较高,结构相对稳定。选取结合能力最好的4个组合用Pymol软件进行可视化处理(图8)。

    • 肿瘤的发生和发展是多基因、多步骤的结果。中药多成分、多靶点的特点使其在肿瘤治疗方面有独特的优势。大量的临床实践表明,中药在治疗肿瘤中能够改善症状、提高患者生存质量、延长生存期等,有着其他治疗药物及手段不可替代的作用[19-20]。以中药黄芪为例,不仅可以通过Wnt5/β-catenin信号通路抑制肿瘤生长[21],同时具有通过PD-L1下调诱导耐药黑色素瘤的干性抑制和化疗敏感性增强的作用,可以减少化疗药物用量[22],还能充当免疫佐剂,提高患者免疫力,改善生存质量[23]。网络药理学最大的优势在于可以运用系统生物学的分析,为中药多成分、多靶点、多通路的机制研究提供有力的技术支撑[12],其分析理念和技术路径又与中医药治疗疾病的整体观相契合,已用于多种中药和中药复方作用机制的研究,如灯盏细辛、半枝莲等中药和茵陈蒿汤、桃红四物汤等中药复方,利用网络药理学的方法得到治疗机制的详细阐述和证明[2427],为中药药理作用机制的探索提供了很好的参考。

      本研究发现西红花中多种成分,如西红花酸、西红花苷等可与IL-6、AKT1、CCND1、IL-1β、MMP9、EGFR、TP53靶点产生适度结合,提示这些靶点可能是西红花中活性成分发挥抗肿瘤作用的关键靶点。研究发现,AKT1是PI3K-AKT-mTOR信号通路中的重要靶点,被磷酸化激活后可以促进细胞的增殖与存活,与肿瘤细胞的生长密切相关[28]。多项研究表明,通过抑制AKT1可以治疗肺癌、结肠癌、卵巢癌等多种实体癌[29]。EGFR与一些全球发病率和致死率高的癌症发病机制直接或间接相关,包括肺癌、乳腺癌和结直肠癌等[30]。当EGFR过度表达时,细胞表面会出现过量的受体,诱导正常的细胞转化为癌细胞,并为癌细胞持续生存提供条件[31]。CCND1是细胞周期家族的一员[32],公认的原癌基因,在甲状旁腺瘤、乳腺癌、肝癌及食管、肺、头颈部鳞状细胞癌的发生、发展过程中均起着重要作用[33-34]。西红花苷是由西红花酸和龙胆二糖或葡萄糖结合形成的二萜苷类化合物,西红花苷Ⅰ和西红花苷Ⅱ的差别在于分子中糖苷基数目的多少 [35]。西红花酸已具有抗肿瘤作用,以其为苷元形成的西红花苷同样也表现出较好的抗肿瘤活性,其中西红花苷Ⅱ的表现最好。西红花苷可以通过P53途径下调细胞周期蛋白d1和p21的表达,诱导细胞凋亡和细胞周期停滞,从而抑制肿瘤生长[36]。分子对接的结果与GO富集分析、KEGG通路富集分析结果一致,验证了网络药理学分析结果的正确性。

      Buyun等学者在肝癌Hep3B和HepG2细胞中使用西红花苷抑制了IL-6对STAT3以及细胞周期蛋白D1的激活,验证了西红花对肝癌细胞的抗增殖,凋亡和阻断入侵作用[37]。在转移性乳腺癌的研究中,Ali等研究人员在体内和体外实验中均证明了西红花苷可以通过VEGF和MMP9下调发挥抗肿瘤作用,而且对乳腺癌的转移扩散有较好的抑制作用[38]。这些研究成果在一定程度上验证了利用网络药理学探究发现的西红花抗肿瘤作用机制的可行性。

      综上所述,本研究利用网络药理学结合分子对接技术,探究西红花抗肿瘤作用的活性成分、作用靶点及信号通路。发现西红花抗肿瘤的作用具有多成分、多靶点、多通路、多机制的特点,其中以西红花苷为代表的西红花特有化学成分显示出了良好的抗肿瘤活性,可以在多条肿瘤发生通路中发挥作用,为西红花治疗肿瘤的深入研究提供了理论基础。但这些结果受限于各个数据库信息的片面性,而且只关注了成分,没有考量到成分的含量及其之间是否存在相互作用,预测的结果存在一定的片面性和局限性,需要进一步进行体内、外实验验证。

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