基于网络药理学和分子对接技术研究蒲公英治疗乳腺增生的潜在机制

doi:10.5246/jcps.2023.11.072

中国药学(英文版) ›› 2023, Vol. 32 ›› Issue (11): 893-910.DOI: 10.5246/jcps.2023.11.072

基于网络药理学和分子对接技术研究蒲公英治疗乳腺增生的潜在机制

孙志勇¹^,^*(), 高淑丽¹, 张阳¹, 薛刚强¹, 苑子林¹, 王少男²

1. 石家庄市妇产医院制剂科, 河北石家庄 050011
2. 河北中医学院药学院, 河北石家庄 050020

收稿日期:2023-05-21 修回日期:2023-06-15 接受日期:2023-07-27 出版日期:2023-12-02 发布日期:2023-12-02
通讯作者: 孙志勇
作者简介:
+ Tel.: +86-311-85281900, E-mail: sunzhiyong1002@sina.com
基金资助:
The Scientific Research Project of the Hebei Provincial Administration of Traditional Chinese Medicine (Grant No. 2022490).

Study on the potential mechanism of Pu Gong Ying in treating breast hyperplasia based on network pharmacology and molecular docking

Zhiyong Sun¹^,^*(), Shuli Gao¹, Yang Zhang¹, Gangqiang Xue¹, Zilin Yuan¹, Shaonan Wang²

1 Pharmaceutical Preparation, Shijiazhuang Obstetrics and Gynecology Hospital, Shijiazhuang 050011, Hebei, China
2 School of Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang 050020, Hebei, China

Received:2023-05-21 Revised:2023-06-15 Accepted:2023-07-27 Online:2023-12-02 Published:2023-12-02
Contact: Zhiyong Sun

摘要/Abstract

摘要：

本研究应用网络药理学方法研究蒲公英治疗乳腺增生的作用靶点及作用路径, 结合分子对接技术分析蒲公英中活性成分和关键靶点之间的亲和性。网络药理学结果发现蒲公英中的15个活性成分涉及的261个作用靶点与乳腺增生疾病关联的2455个作用靶点共有交集靶点90个(在蛋白质交互网络中包含89个相关靶点和1个无关靶点)。Cytoscope软件中cytoHubba插件获得的度值前10靶点与MOCDE插件获得的评分前10的靶点交集获得的 CASP3、EGFR、ESR、ERBB2、MMP9和PTGS2靶点作为核心靶点。富集分析确定了284个具有统计学意义的基因本体论术语。京都基因与基因组百科全书分析了125条具有统计学意义通路, 主要参与调控癌症通路、PI3K-Akt信号通路、MAPK信号通路、癌症中的小分子RNAs和化学致癌受体激活等。分子对接结果显示木犀草素、芹菜素和异鼠李素与核心靶点有很好的结合活性。蒲公英能够通过多路径、多靶点治疗乳腺增生, 阐明了潜在作用机制, 并为乳腺增生靶向治疗和乳腺癌预防提供重要启示。

关键词: 蒲公英, 乳腺增生, 网络药理学, 分子对接, PI3K-Akt信号通路

Abstract:

In the present study, network pharmacology was employed to elucidate the targets and pathways involved in the treatment of breast hyperplasia (BH) by Pu Gong Ying (PGY). Molecular docking was utilized to analyze the interaction between PGY and key targets based on the findings of network pharmacology. The intersection of 261 targets associated with 15 PGY compounds and 2455 targets related to BH yielded 90 common targets (89 of which were included in the protein-protein interaction (PPI) network, while one target did not exhibit interactions with any other targets in the PPI network). The top 10 hub targets ranked in the PPI network and the top 10 targets according to the Molecular Complex Detection (MOCDE) score were intersected, resulting in the identification of CASP3, EGFR, ESR1, ERBB2, MMP9, and PTGS2 as key targets, as determined by different algorithms. Gene Ontology (GO) functional enrichment analysis revealed 284 biological components. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified 125 pathways, primarily associated with the regulation of pathways in cancer, the PI3K-Akt signaling pathway, the MAPK signaling pathway, microRNAs in cancer, chemical carcinogenesis-receptor activation, and others. Molecular docking results demonstrated the favorable binding efficacy of PGY9, PGY10, and PGY11 with the core targets. The findings suggested that PGY exerted its therapeutic effects on BH through multiple targets and biological pathways. This study contributed to the understanding of the molecular mechanisms underlying BH development and provided implications for BH-targeted therapy and even breast cancer prevention.

Key words: Pu Gong Ying, Breast hyperplasia, Network pharmacology, Molecular docking, PI3K-Akt signaling pathway

Supporting:

孙志勇, 高淑丽, 张阳, 薛刚强, 苑子林, 王少男. 基于网络药理学和分子对接技术研究蒲公英治疗乳腺增生的潜在机制[J]. 中国药学(英文版), 2023, 32(11): 893-910.

Zhiyong Sun, Shuli Gao, Yang Zhang, Gangqiang Xue, Zilin Yuan, Shaonan Wang. Study on the potential mechanism of Pu Gong Ying in treating breast hyperplasia based on network pharmacology and molecular docking[J]. Journal of Chinese Pharmaceutical Sciences, 2023, 32(11): 893-910.

图/表 10

Figure 1. Work flow for PGY in the treatment of BH. Network pharmacology was performed to identify the active compounds and key targets of PGY against BH. Molecular docking was conducted to predict the interaction and connection stability between active components and candidate targets.

Table 1. 15 Active compounds of PGY.

Figure 2. Venn diagram of targets for PGY in the treatment of BH. There are 2455 disease targets related to BH in the green and yellow areas. There are 261 target genes related to PGY in the pink and yellow areas, and 90 common genes are identified in the yellow area.

Figure 3. The PPI network diagram for PGY in the treatment of BH. The PPI network is constructed by Cytoscape 3.9.1 software. The node color stands for the size of the degree. The node color is from yellow to red, and the corresponding degree is gradually larger.

Figure 4. PPI network module. The MCODE plugin was analyzed to filter out the most significant modules, with an MCODE score of 17.3, containing 21 nodes and 173 edges.

Figure 5. GO and KEGG enrichment analyses. The histogram of GO enrichment analysis of PGY-BH genes. The top 20 GO entries of BP, CC, and MF were shown, respectively. The abscissa shows gene count, and the ordinate displays enriched GO entries. The bubble diagram of the KEGG enrichment pathway of PGY-BH genes. The top 20 pathways were shown. The abscissa shows fold enrichment, and the ordinate displays enriched entries. The size of the dot is the number of genes contained under this entry, the color of the point represents the degree of enrichment, and the color from red to green corresponds to FDR from small to large.

Figure 6. Construction of Components-Targets-Pathways network diagram for PGY in the treatment of BH. It contains 98 nodes and 599 edges. The red hexagon nodes represent 15 active compounds from PGY. The circular nodes of green stand for BH-related target genes. The light blue square nodes stand for the top 20 pathways in Figure 5. Larger node sizes indicate higher degree values.

Figure 7. Schematic diagram of pathways in cancer (hsa05200). The potential pathways, which were involved in PGY against BH by a red heart.

Figure 8. Molecular docking. The binding of three representative compounds (PGY9: Luteolin, PGY10: Apigenin, PGY11: Isorhamnetin) with six core targets, including CASP3, EGFR, ESR1, ERBB2, MMP9, and PTGS2, was established by molecular docking analysis, respectively.

Table 2. PGY molecular docking binding energy results (Kcal/mol) and hydrogen bond binding sites results.

参考文献 70

[1]	Gao, H.L.; Yang, C.; Fan, J.Q.; Lan, L.; Pang, D. Hereditary and breastfeeding factors are positively associated with the aetiology of mammary gland hyperplasia: a case–control study. Int. Health. 2020, 13, 240–247.
[2]	Chen, C.C.; Jiang, C.G.; Chen, Q.Q.; Gao, D. Efficacy of psychological interventions for patients with breast hyperplasia. Cell Biochem. Biophys. 2015, 71, 1663–1669.
[3]	Wang, L.S.; Zhao, D.Q.; Di, L.; Cheng, D.Y.; Zhou, X.F.; Yang, X.W.; Liu, Y.H. The anti-hyperplasia of mammary gland effect of Thladiantha dubia root ethanol extract in rats reduced by estrogen and progestogen. J. Ethnopharmacol. 2011, 134, 136–140.
[4]	Sun, Z.Y.; Xue, G.Q.; Zhang, Y.; Yuan, Z.L.; Gao, S.L. HPLC fingerprint and determination of isochlorogenic acid B and hesperidin in Kun Runing granules. Guangzhou. Chem. Ind. 2020, 5, 101–104.
[5]	Ge, B.J.; Zhao, P.; Li, H.T.; Sang, R.; Wang, M.; Zhou, H.Y.; Zhang, X.M. Taraxacum mongolicum protects against Staphylococcus aureus-infected mastitis by exerting anti-inflammatory role via TLR2-NF-κB/MAPKs pathways in mice. J. Ethnopharmacol. 2021, 268, 113595.
[6]	Chen, H.B.; Zhou, H.G.; Li, W.T.; Cheng, H.B.; Wu, M.H. Network pharmacology: A new perspective of mechanism research of traditional Chinese medicine formula. Chin. J. Trad. Chin. Med. Pharm. 2019, 7, 2873–2876.
[7]	Saikia, S.; Bordoloi, M. Molecular docking: challenges, advances and its use in drug discovery perspective. Curr. Drug Targets. 2019, 20, 501–521.
[8]	Ding, N.; Zhang, T.; Luo, J.; Liu, H.C.; Deng, Y.; He, Y.H. Study on the mechanism of Baishao Qiwu Decoction in the treatment of colorectal cancer based on network pharmacology and molecular docking. J. Chin. Pharm. Sci. 2023, 32, 17.
[9]	Xu, H.Y.; Zhang, Y.Q.; Liu, Z.M.; Chen, T.; Lv, C.Y.; Tang, S.H.; Zhang, X.B.; Zhang, W.; Li, Z.Y.; Zhou, R.R.; Yang, H.J.; Wang, X.J.; Huang, L.Q. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019, 47, D976–D982.
[10]	Kim, S.; Chen, J.; Cheng, T.J.; Gindulyte, A.; He, J.; He, S.Q.; Li, Q.L.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021, 49, D1388–D1395.
[11]	Daina, A.; Michielin, O.; Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717.
[12]	Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019, 47, W357–W364.
[13]	Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; Kaplan, S.; Dahary, D.; Warshawsky, D.; Guan-Golan, Y.; Kohn, A.; Rappaport, N.; Safran, M.; Lancet, D. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform. 2016, 54, 1.30.1–1.30.33.
[14]	Piñero, J.; Saüch, J.; Sanz, F.; Furlong, L.I. The DisGeNET cytoscape app: exploring and visualizing disease genomics data. Comput. Struct. Biotechnol. J. 2021, 19, 2960–2967.
[15]	Amberger, J.S.; Hamosh, A. Searching online Mendelian inheritance in man (OMIM): a knowledgebase of human genes and genetic phenotypes. Curr. Protoc. Bioinform. 2017, 58, 1.2.1–1.2.12.
[16]	Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.F.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.A.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082.
[17]	Soudy, M.; Anwar, A.M.; Ali Ahmed, E.; Osama, A.; Ezzeldin, S.; Mahgoub, S.; Magdeldin, S. UniprotR: Retrieving and visualizing protein sequence and functional information from Universal Protein Resource (UniProt knowledgebase). J. Proteom. 2020, 213, 103613.
[18]	Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; Jensen, L.J.; von Mering, C. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613.
[19]	Otasek, D.; Morris, J.H.; Bouças, J.; Pico, A.R.; Demchak, B. Cytoscape Automation: empowering workflow-based network analysis. Genome Biol. 2019, 20, 185.
[20]	Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8, S11.
[21]	Saito, R.; Smoot, M.E.; Ono, K.; Ruscheinski, J.; Wang, P.L.; Lotia, S.; Pico, A.R.; Bader, G.D.; Ideker, T. A travel guide to Cytoscape plugins. Nat. Methods. 2012, 9, 1069–1076.
[22]	Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4, 44–57.
[23]	Goodsell, D.S.; Zardecki, C.; Berman, H.M.; Burley, S.K. Insights from 20 years of the molecule of the month. Biochem. Mol. Biol. Educ. 2020, 48, 350–355.
[24]	Trott, O.; Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455–461.
[25]	Shi, S.Y.; Zhao, Y.; Zhou, H.H.; Zhang, Y.P.; Jiang, X.Y.; Huang, K.L. Identification of antioxidants from Taraxacum mongolicum by high-performance liquid chromatography-diode array detection-radical-scavenging detection-electrospray ionization mass spectrometry and nuclear magnetic resonance experiments. J. Chromatogr. A. 2008, 1209, 145–152.
[26]	Shi, S.Y.; Zhou, Q.; Peng, H.; Zhou, C.X.; Hu, M.H.; Tao, Q.F.; Hao, X.J.; Stöckigt, J.; Zhao, Y. Four new constituents from Taraxacum mongolicum. Chin. Chem. Lett. 2007, 18, 1367–1370.
[27]	Wolbis, M.; Krolikowska, M.; Bednarek, P. Polyphenolic compounds in Taraxacum officinale. Acta. Pol. Pharm. 1993, 2–3, 153–155.
[28]	Komissarenko, N.F.; Derkach, A. Taraxacum officinale couma- rins. Khim. Prir. Soedin. 1981, 4, 519–522.
[29]	Wolbis, M.; Krolikowska, M. Polyphenolic compounds of dandelion. Acta Pol. Pharm. 1985, 42, 215–217.
[30]	Ling, Y.; Zhang, Y.L.; Cai, S.Q.; Zheng, J.H. Flavonoids and Steroids from Taraxacumsinicum Kitag. Chin. J. Med. Chem. 1988, 1, 46–48.
[31]	Wang, W.B. Determination of five Flavonoids in different Taraxacum mongolicum Hand.-Mazz. extracts using HPLC. Food. Industry. 2018, 8, 302–305.
[32]	Shi, S.Y.; Zhou, C.X.; Xu, Y.; Tao, Q.F.; Bai, H.; Lu, F.S.; Lin, W.Y.; Chen, H.Y.; Zheng, W.; Wang, L.W.; Wu, Y.H.; Zeng, S.; Huang, K.X.; Zhao, Y.; Li, X.K.; Qu, J. Studies on chemical constituents from herbs of Taraxacum mongolicum. China J. Chin. Mater. Med. 2008, 10, 1147–1157.
[33]	Kisiel, W.; Michalska, K. Sesquiterpenoids and phenolics from Taraxacum hondoense. Fitoterapia. 2005, 76, 520–524.
[34]	Hansel, R.; Kartarahardja, M.; Huang, J.T. Naturally occuring terpene derivatives, part263 sequiterpene lactone-β-D-glucopyranoside together with a new eudes manolide from Taraxacum officinale. Phytochem. 1980, 5, 857–886.
[35]	Hsin, K.Y.; Ghosh, S.; Kitano, H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One. 2013, 8, e83922.
[36]	Hu, G.; Wang, J.J.; Hong, D.; Zhang, T.; Duan, H.Q.; Mu, X.; Yang, Z.J. Effects of aqueous extracts of Taraxacum Officinale on expression of tumor necrosis factor-alpha and intracellular adhesion molecule 1 in LPS-stimulated RMMVECs. BMC Complement. Altern. Med. 2017, 17, 38.
[37]	Sun, Y.W.; Wu, Y.J.; Wang, Z.L.; Chen, J.C.; Yang, Y.; Dong, G.Z. Dandelion extract alleviated lipopolysaccharide-induced oxidative stress through the Nrf2 pathway in bovine mammary epithelial cells. Toxins. 2020, 12, 496.
[38]	San, Z.H. Anti-inflammatory Activity and Regulation Mechanisms of Taraxasterol on Mammitis Induced by LPS. Jilin. Univ. 2015.
[39]	Azab, A.; Nassar, A.; Azab, A.N. Anti-inflammatory activity of natural products. Molecules. 2016, 21, 1321.
[40]	Neha, D.; Shikha, S. Cancer chemotherapy with novel bioactive natural products. J. Chin. Pharm. Sci. 2022, 31, 589.
[41]	Saito, N. Chemical research on antitumor isoquinoline marine natural products and related compounds. Chem. Pharm. Bull. 2021, 69, 155–177.
[42]	Ory, L.; Nazih, E.H.; Daoud, S.; Mocquard, J.; Bourjot, M.; Margueritte, L.; Delsuc, M.A.; Bard, J.M.; Pouchus, Y.F.; Bertrand, S.; Roullier, C. Targeting bioactive compounds in natural extracts - Development of a comprehensive workflow combining chemical and biological data. Anal. Chim. Acta. 2019, 1070, 29–42.
[43]	Conti, P.; Caraffa, A.; Gallenga, C.E.; Ross, R.; Kritas, S.K.; Frydas, I.; Younes, A.; Di Emidio, P.; Ronconi, G.; Pandolfi, F. Powerful anti-inflammatory action of luteolin: potential increase with IL-38. BioFactors. 2021, 47, 165–169.
[44]	Li, B.S.; Zhu, R.Z.; Lim, S.H.; Seo, J.H.; Choi, B.M. Apigenin alleviates oxidative stress-induced cellular senescence via modulation of the SIRT1-NAD+-CD38 axis. Am. J. Chin. Med. 2021, 49, 1235–1250.
[45]	Ren, X.J.; Han, L.Y.; Li, Y.X.; Zhao, H.Y.; Zhang, Z.Y.; Zhuang, Y.R.; Zhong, M.; Wang, Q.; Ma, W.H.; Wang, Y. Isorhamnetin attenuates TNF-α-induced inflammation, proliferation, and migration in human bronchial epithelial cells via MAPK and NF-κB pathways. Anat. Rec. 2021, 304, 901–913.
[46]	Guo, Y.F.; Xu, N.N.; Sun, W.J.; Zhao, Y.F.; Li, C.Y.; Guo, M.Y. Luteolin reduces inflammation in Staphylococcus aureus-induced mastitis by inhibiting NF-kB activation and MMPs expression. Oncotarget. 2017, 17, 28481–28493.
[47]	Tian, L.; Wang, S.; Jiang, S.S.; Liu, Z.Y.; Wan, X.Q.; Yang, C.C.; Zhang, L.; Zheng, Z.; Wang, B.; Li, L. Luteolin as an adjuvant effectively enhances CTL anti-tumor response in B16F10 mouse model. Int. Immunopharmacol. 2021, 94, 107441.
[48]	Wang, Y.; Wang, Q.M.; Feng, W.; Yuan, Q.; Qi, X.W.; Chen, S.; Yao, P.; Dai, Q.; Xia, P.Y.; Zhang, D.L.; Sun, F.J. Folic acid-modified ROS-responsive nanoparticles encapsulating luteolin for targeted breast cancer treatment. Drug Deliv. 2021, 28, 1695–1708.
[49]	Malik, S.; Suchal, K.; Khan, S.I.; Bhatia, J.; Kishore, K.; Dinda, A.K.; Arya, D.S. Apigenin ameliorates streptozotocin-induced diabetic nephropathy in rats via MAPK-NF-κB-TNF-α and TGF-β1-MAPK-fibronectin pathways. Am. J. Physiol. Renal Physiol. 2017, 313, F414–F422.
[50]	Patil, R.H.; Babu, R.L.; Kumar, M.N.; Kiran Kumar, K.M.; Hegde, S.M.; Nagesh, R.; Ramesh, G.T.; Sharma, S.C. Anti-inflammatory effect of apigenin on LPS-induced pro-inflammatory mediators and AP-1 factors in human lung epithelial cells. Inflammation. 2016, 39, 138–147.
[51]	Baier, A.; Nazaruk, J.; Galicka, A.; Szyszka, R. Inhibitory influence of natural flavonoids on human protein kinase CK2 isoforms: effect of the regulatory subunit. Mol. Cell. Biochem. 2018, 444, 35–42.
[52]	Landesman-Bollag, E.; Song, D.H.; Romieu-Mourez, R.; Sussman, D.J.; Cardiff, R.D.; Sonenshein, G.E.; Seldin, D.C. Protein kinase CK2: signaling and tumorigenesis in the mammary gland. Mol. Cell Biochem. 2001, 227, 153–165.
[53]	Kim, S.Y.; Jin, C.Y.; Kim, C.H.; Yoo, Y.H.; Choi, S.H.; Kim, G.Y.; Yoon, H.M.; Park, H.T.; Choi, Y.H. Isorhamnetin alleviates lipopolysaccharide-induced inflammatory responses in BV2 microglia by inactivating NF-κB, blocking the TLR4 pathway and reducing ROS generation. Int. J. Mol. Med. 2019, 43, 682–692.
[54]	Seo, K.; Yang, J.H.; Kim, S.C.; Ku, S.K.; Ki, S.H.; Shin, S.M. The antioxidant effects of isorhamnetin contribute to inhibit COX-2 expression in response to inflammation: a potential role of HO-1. Inflammation. 2014, 37, 712–722.
[55]	Zhang, Z.W.; Zhang, H.; Li, D.B.; Zhou, X.P.; Qin, Q.; Zhang, Q.Y. Caspase-3-mediated GSDME induced Pyroptosis in breast cancer cells through the ROS/JNK signalling pathway. J. Cell Mol. Med. 2021, 25, 8159–8168.
[56]	Gompel, A.; Chaouat, M.; Hugol, D.; Forgez, P. Steroidal hormones and proliferation, differentiation and apoptosis in breast cells. Maturitas. 2004, 49, 16–24.
[57]	Brandt, R.; Eisenbrandt, R.; Leenders, F.; Zschiesche, W.; Binas, B.; Juergensen, C.; Theuring, F. Mammary gland specific hEGF receptor transgene expression induces neoplasia and inhibits differentiation. Oncogene. 2000, 19, 2129–2137.
[58]	Hartmann, L.C.; Degnim, A.C.; Santen, R.J.; Dupont, W.D.; Ghosh, K. Atypical hyperplasia of the breast: risk assessment and management options. N. Engl. J. Med. 2015, 372, 78–89.
[59]	Moasser, M.M. The oncogene HER2: its signaling and transforming functions and its role in human cancer pathogenesis. Oncogene. 2007, 26, 6469–6487.
[60]	Ma, W.; Shi, B.; Zhao, F.K.; Wu, Y.F.; Jin, F. Systematic analysis of breast atypical hyperplasia-associated hub genes and pathways based on text mining. Eur. J. Cancer Prev. 2019, 28, 507–514.
[61]	Tan, H.; Zhong, Y.; Pan, Z. Autocrine regulation of cell proliferation by estrogen receptor-alpha in estrogen receptor-alpha-positive breast cancer cell lines. BMC. Cancer. 2009, 9, 31.
[62]	Mao, X.Y.; Qiao, Z.; Fan, C.F.; Guo, A.Y.; Yu, X.M.; Jin, F. Expression pattern and methylation of estrogen receptor α in breast intraductal proliferative lesions. Oncol. Rep. 2016, 36, 1868–1874.
[63]	Li, H.; Zheng, H.L.; Li, L.H.; Shen, X.G.; Zang, W.J.; Sun, Y.S. The effects of matrix metalloproteinase-9 on dairy goat mastitis and cell survival of goat mammary epithelial cells. PLoS One. 2016, 11, e0160989.
[64]	Naserkheil, M.; Ghafouri, F.; Zakizadeh, S.; Pirany, N.; Manzari, Z.; Ghorbani, S.; Banabazi, M.H.; Bakhtiarizadeh, M.R.; Huq, M.A.; Park, M.N.; Barkema, H.W.; Lee, D.; Min, K.S. Multi-omics integration and network analysis reveal potential hub genes and genetic mechanisms regulating bovine mastitis. Curr. Issues Mol. Biol. 2022, 44, 309–328.
[65]	Glover, J.A.; Hughes, C.M.; Cantwell, M.M.; Murray, L.J. A systematic review to establish the frequency of cyclooxygenase-2 expression in normal breast epithelium, ductal carcinoma in situ, microinvasive carcinoma of the breast and invasive breast cancer. Br. J. Cancer. 2011, 105, 13–17.
[66]	Qin, Q.; Ji, H.F.; Li, D.B.; Zhang, H.; Zhang, Z.W.; Zhang, Q.Y. Tumor-associated macrophages increase COX-2 expression promoting endocrine resistance in breast cancer via the PI3K/Akt/mTOR pathway. Neoplasma. 2021, 68, 938–946.
[67]	Li, Y.; Zhang, Z.L. Potential microRNA-mediated oncogenic intercellular communication revealed by pan-cancer analysis. Sci. Rep. 2014, 4, 7097.
[68]	Pons-Tostivint, E.; Thibault, B.; Guillermet-Guibert, J. Targeting PI3K signaling in combination cancer therapy. Trends Cancer. 2017, 3, 454–469.
[69]	Jiang, K.F.; Zhao, G.; Deng, G.Z.; Wu, H.C.; Yin, N.N.; Chen, X.Y.; Qiu, C.W.; Peng, X.L. Polydatin ameliorates Staphylococcus aureus-induced mastitis in mice via inhibiting TLR2-mediated activation of the p38 MAPK/NF-κB pathway. Acta Pharmacol. Sin. 2017, 38, 211–222.
[70]	Li, S. Network pharmacology evaluation method guidance - draft. World J. Tradit. Chin. Med. 2021, 7, 148.

基于网络药理学和分子对接技术研究蒲公英治疗乳腺增生的潜在机制

Study on the potential mechanism of Pu Gong Ying in treating breast hyperplasia based on network pharmacology and molecular docking

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