Exploring the Mechanism of Macleaya cordata (Willd) R. Br. Against Breast Cancer by Network Pharmacology and Molecular Docking

Exploring the Mechanism of Macleaya cordata (Willd) R. Br. Against Breast Cancer by Network Pharmacology and Molecular Docking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the Mechanism of Macleaya cordata (Willd) R. Br. Against Breast Cancer by Network Pharmacology and Molecular Docking Lei Zhang, Jing Huang, Yulong Peng, Su Yin, Yang Cao, Kai Nan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4945731/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Macleaya cordata (Willd) R. Br. ( M. cordata ) has widely reported antitumor activity, while the underlying mechanism of M. cordata anti-breast cancer (BC) still remains unclear. The compounds of M. cordata were collected from previous researches and screened by drug-likeness rules to identify bioactive compounds. The targets were obtained from MalaCards, Online Mendelian Inheritance in Man, and SwissTargetPrediction database, then overlapped to get intersections as potential anti-BC targets of M. cordata . After topological analysis of the protein-protein interaction network, the correlation analysis of gene expression and patient pathological stage and survival, respectively, was performed, and 4 pivotal targets were obtained. Four bioactive compounds of M. cordata (6-cyanodihydrogensanguinarine, Corysamine, Oxychelirubine, and Berberrubine) had strong binding efficiency with the 4 pivotal genes after molecular docking analysis. The current study demonstrated that M. cordata acts against BC through multiple targets and pathways that may guide further studies on M. cordata anti-BC effects. M. cordata breast cancer network pharmacology molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Worldwide, breast cancer (BC) is one of the most common cancers endangering women's health[ 18 ]. Current treatment of BC includes surgery, chemotherapy, radiation therapy, targeted therapy, and endocrine therapy[ 15 ]. Although therapies for BC patients have been developed, BC is still the most common cause of death from cancer in developing countries[ 11 ]. Therefore, it is significant to explore new effective and safe drugs for BC therapy. In recent years, phytochemicals have attached much attention and were widely used in clinical cancer treatment due to their advantages in immune strengthening, chemotherapy attenuation, and metastasis inhibition [ 17 ]. Besides, previous research has revealed that the combined application of traditional chemotherapy and Chinese medicine could reverse the drug resistance and reduce the toxicity of chemotherapy [ 31 ]. M. cordata , a plant of the Papaveraceae family, is a significant source of quaternary benzo[c]phenanthridine alkaloids (QBA)[ 10 ]. Particularly, sanguinarine and chelerythrine are the principal members of QBA obtained from M. cordata [ 8 ]. Previous researches have proved that sanguinarine and chelerythrine in M. cordata have anti-proliferative activity in BC cells [ 1 ]. It has been displayed that the apoptosis induced by sanguinarine accompanied the generation of reactive oxygen species in BC cells [ 24 ]. Chelerythrine is widely used as a specific inhibitor of protein kinase C and could selectively inhibited the proliferation of triple-negative BC cells [ 13 ]. However, the compounds and anti-BC targets of M. cordata in treating BC have not been systemically explored. Currently, network pharmacology can provide an effective solution to identify the association between diseases, targets, and drugs [ 43 ]. Molecular docking, a theoretical simulation approach, demonstrates the drug-target interactions by predicting its binding mode and binding affinity. This study revealed the bioactive compounds and underlying mechanisms of M. cordata anti-BC through network pharmacology and molecular docking approach. The current study indicated the M. cordata against BC through multiple targets and pathways and related mechanisms that may provide a potential treatment scheme for the therapeutics of BC. Figure 1 shows the graphical abstract of this study. 2. Results 2.1 BC genes targeted by M. cordata One hundred and ninety compounds of M. cordata were collected from previous research in the literature. All these compounds were screened by drug-likeness rules, including Lipinski rules and Veber rules. Figure 2 showed that the drug-like properties of M. cordata compounds. Finally, a total of 134 bioactive compounds that met both Lipinski and Veber rules were identified (Table S1 in supplementary material). Totally, 994 potential targets for the 134 bioactive compounds from the SwissTargetPrediction database were collected. Meanwhile, 1357 BC-related targets were obtained from the MalaCards and OMIM database. To further discover the mechanism of M. cordata against BC, the target genes of BC were linked with the target genes of M. cordata . Two hundred and forty-four overlapping targets with potential roles in M. cordata against BC mechanism were identified after the M. cordata target genes and BC target genes were overlapped using FUNRICH software (Fig. 3 ). Next, the compound-target network were constructed by Cytoscape software (Fig. 3 ). 2.2 GO analysis and KEGG enrichment analysis of target genes. To verify the targets for M. cordata against BC mechanism, the GO and KEGG pathway enrichment analysis were performed and visualized as bubble chart (Fig. 4 ). GO analysis of targets of M. cordata against BC was conducted regarding three aspects: BP, MF, and CC. The GO item including regulation of MAPK, positive regulation of cell migration and positive regulation of kinase activity, ect. The KEGG enrichment analysis showed that these targets were involved in the PI3K/AKT, FoxO, and MAPK signal pathways. Above all, the PI3K/AKT signalling pathway was suggested as the most enriched pathway. M. Cordata plays an anti-BC role through multiple targets involved in multiple processes in this pathway (Fig. 4 ). 2.3 PPI network of targets for M. cordata against BC. The PPI network of targets were visualized and analyzed by Cytoscape software (Fig. 5 ). The degree value represents the link number of each node. The betweenness centrality describes the role of a node as a pivot in a network[ 42 ]. After network topological analysis, 51 genes analyzed degree and betweenness centrality values above the average (Table 1 ). Hence, these 51 genes encode proteins with key roles in BC. Table 1 Target genes with potentially roles in M. cordata against BRCA mechanism No. Target gene Betweenness Centrality Degree 1 AKT1 0.056115 165 2 ESR1 0.051927 136 3 EGFR 0.037899 150 4 MAPK3 0.033326 144 5 MAPK1 0.031246 137 6 SRC 0.030823 136 7 HSP90AA1 0.026585 127 8 CTNNB1 0.02448 127 9 IL6 0.024386 117 10 VEGFA 0.02299 140 11 CCND1 0.022683 139 12 STAT3 0.020475 133 13 JUN 0.020213 133 14 CASP3 0.018079 130 15 AR 0.016725 95 16 MMP9 0.015117 91 17 MAPK8 0.014446 111 18 SIRT1 0.013842 91 19 EP300 0.013827 94 20 TNF 0.013484 103 21 ERBB2 0.013455 115 22 RHOA 0.010566 92 23 MDM2 0.01012 98 24 MTOR 0.010033 111 25 PIK3CA 0.008577 94 26 MAPK14 0.008375 93 27 EZH2 0.008254 82 28 MMP2 0.008111 80 29 CDC42 0.008065 85 30 CXCL8 0.007876 86 31 JAK2 0.007671 78 32 PTGS2 0.007436 84 33 ATM 0.006524 89 34 XIAP 0.006386 67 35 BCL2L1 0.006347 93 36 RELA 0.005967 82 37 CDK4 0.005883 81 38 MET 0.005551 56 39 NR3C1 0.005332 54 40 KDR 0.005271 80 41 PPARG 0.005218 61 42 PGR 0.005209 64 43 PIK3R1 0.005114 80 44 GSK3B 0.005005 64 45 HIF1A 0.004871 75 46 ABL1 0.004847 65 47 HDAC1 0.004772 70 48 PTPN11 0.004562 67 49 DNMT1 0.004228 56 50 TERT 0.003883 53 51 IL1B 0.00381 65 Furthermore, the PPI network was divided into several clusters using MCODE plug-in (Table 2 and Fig. 5 ). Table 2 Molecular docking parameters and corresponding calculation results Gene Molecule Name Binding energy (kcal/mol) Amino acid CXCL8 6-cyanodihydrogensanguinarine -8.5 Lys25, Ile22, Arg68, Phe65, Pro19, His18, Lys67, Lys64, Lys20 IL1B Corysamine -9.0 Pro31, Met130, Phe133, Ser125, Asp142, Gln141, Gly135, Leu134, Gly136, Trp120, Lys77, Pro131, Thr79 MMP9 Oxychelirubine -10.8 Tyr423, Met422, Pro415, Arg424, Tyr420, Thr426, Pro429, Glu416, Leu418, Leu397 PTGS2 Berberrubine -9.4 Tyr136, Asn34, Gly135, Pro154, Val155, Cys47, Gln461, His39, Leu52, Pro153, Cys36, Peo156, Met48, Ser49, Trp323, Gln327 2.4 Correlation analysis of gene expression and patient pathological stage, and survival, respectively After network topological analysis, a correlation analysis between the expression of 51 genes and the pathological stage and the overall survival of breast cancer patients was conducted. Pathological stage analysis, based on GEPIA, generates expression violin plots based on patient pathological stages. By analyzing the difference in gene expression between tumor tissue and adjacent normal tissue, the stage specific marker genes of the pathological stage were identified. (Pr < 0.01) (Fig. 6 ). Next, survival analysis was used to identify the prognostic risk gene factor ( P < 0.01) (Fig. 6 ). Finally, four genes(CXCL8, IL1B, MMP9,and PTGS2) were found to be significantly associated with both stage and survival of patients, which suggested that they may play an important role in the effect of M. cordata on BC. 2.5 Molecular docking analysis of bioactive compounds and pivotal target genes After network pharmacology analysis, 4 pivotal target proteins: CXCL8 (PDB code:2il8), IL1B (PDB code:4dep), MMP9 (PDB code:1l6j), and PTGS2 (PDB code:5f19) are vital targets on which M. cordata acts against BC targets. To further validate the M. cordata against BC mechanism, molecular docking was carried out between 134 M. cordata compounds and 4 pivotal target genes. After molecular docking, the docking affinity of each compound and protein was shown in the heatmap (Fig. 7 ). As seen in Table 3 and Fig. 7 , the respective binding energy for the 4 alkaloids (6-cyanodihydrogensanguinarine, Corysamine, Oxychelirubine, and Berberrubine) and the 4 protein crystal structures corresponding to the core target genes (CXCL8, IL1B, MMP9, and PTGS2) were significantly greater than other compounds, which indicates that these 4 compounds could be the key compounds in M. cordata working on BC. Table 3 Molecular docking parameters and corresponding calculation results Gene Molecule Name Binding energy (kcal/mol) Amino acid CXCL8 6-cyanodihydrogensanguinarine -8.5 Lys25, Ile22, Arg68, Phe65, Pro19, His18, Lys67, Lys64, Lys20 IL1B Corysamine -9.0 Pro31, Met130, Phe133, Ser125, Asp142, Gln141, Gly135, Leu134, Gly136, Trp120, Lys77, Pro131, Thr79 MMP9 Oxychelirubine -10.8 Tyr423, Met422, Pro415, Arg424, Tyr420, Thr426, Pro429, Glu416, Leu418, Leu397 PTGS2 Berberrubine -9.4 Tyr136, Asn34, Gly135, Pro154, Val155, Cys47, Gln461, His39, Leu52, Pro153, Cys36, Peo156, Met48, Ser49, Trp323, Gln327 2.6 ADMET analysis of key compounds The so selected four key compounds were assessed for the ADMET studies (Table 4 , Fig. 8 ). Four compounds show good (0) absorption properties. 6-cyanodihydrogensanguinarine and Corysamine aqueous solubility is very low, but possible (-8.0 < logSw < -6.0). Oxychelirubine and Berberrubine aqueous solubility is low (-6.0 < logSw -2.209). Four compounds are likely to be hepatotoxic (hepatotoxicity >-2.209). This model predicts blood-brain penetration (blood brain barrier, BBB) after oral administration. Oxychelirubine and 6-cyanodihydrogensanguinarine have high penetrants (0 ≤ logBB < 0.7) of BBB after oral administration. Corysamine and Berberrubine have medium penetrants (-0.52 < logBB < 0) of BBB after oral administration. The classification whether a compound is an CYP2D6 inhibitor using the cut off Bayesian score of 0.161. Four compounds don’t have potential to be an CYP2D6 inhibitor(CYP2D6 inhibitor < 0,161). Table 4 ADMET descriptors Compound Aqueous solubility Blood brain barrier penetration (BBB) Cytochrome P450 (CYP450) 2D6 inhibition Hepatotoxicity Human intestinal absorption (HIA) Plasma protein binding (PPB) Oxychelirubine -5.528 -0.28 -3.96479 4.47019 0 5.17954 6-cyanodihydrogensanguinarine -6.048 -0.03 -3.43352 5.58974 0 5.45352 Corysamine -6.21 0.35 -1.73701 1.87481 0 3.69082 Berberrubine -4.918 0.004 -4.99231 1.20603 0 -0.32292 3. Discussion BC is the most frequently diagnosed cancer and the second-leading cause of cancer deaths in women [ 16 ]. M. cordata , a plant of the Papaveraceae family, is a commonly used agent in traditional Chinese medicine because of its extensive bioactivities, including antimicrobial, anti-inflammatory, and antitumor [28;34;35]. Antitumor activity of M. cordata has attracted much attention in recent years. The main chemical constituents of M. cordata are QBA alkaloids that are responsible for the pharmacological effects, including sanguinarine and chelerythrine[ 21 ]. The anti-tumor activities of sanguinarine have been reported on several cancer cell lines, such as BC, lung cancer, melanoma and pancreatic cancer cells[1; 21; 39]. In this study, the underlying mechanisms of M. cordata on the treatment of BC were demonstrated by using network pharmacology and molecular docking approach. Network pharmacology is an intersectional product of multiple disciplines, and it can identify the targets and further reveal the drug-target interactions [ 14 ]. Molecular docking, is a powerful technology in drug-target interaction verification through filtering the spatial matching design and binding energy. There, the combination of molecular docking and network pharmacology techniques is more conducive to drug target discovery[ 44 ]. This study combined molecular docking with network pharmacology techniques to predict the target of M. Cordata for BC treatment. In this study, the interactions between M. cordata compounds and its potential targets in BC, as well as numerous signalling pathways in which M. cordata anti-BC targets participate by integrating information from the publicly available databases and previous researches were revealed. A total of 134 bioactive compounds of M. cordata were found from previous researches, including sanguinarine and berberine. It has been reported that sanguinarine induced apoptosis in human mammary MCF-7 BC cells through the inhibition of VEGF release, which was induced by the generation of reactive oxygen species [ 6 ]. Another research demonstrated that berberine suppressed cell motility through the downregulation of transforming growth factor-β1 in BC cells [ 23 ]. Based on MalaCards, OMIM, and SwissTargetPrediction database, target genes which were both BC-related targets and targets of M. cordata compounds were selected. After KEGG pathway enrichment analysis, the PI3K/AKT, FoxO, and MAPK signalling pathways were selected for M. cordata to perform its anti-BC effects by regulating these multiple pathways. The PI3K/AKT signal pathway is frequently activated in BC[ 9 ]. M. cordata may play an anti-BC role through PI3K/AKT signal pathway. Studies have shown that many traditional Chinese medicines exert their antitumor effects through PI3K/AKT pathway [5;19]. Especially, previous studies suggested that chelerythrine, one of M. cordata compounds, could inhibit the metastasis of human hepatocellular carcinoma cells by down-regulating the expression of MMP-2/9 mainly via PI3K/AKT/mTOR signal pathway [ 45 ]. Four pivotal genes (CXCL8, IL1B, MMP9, and PTGS2) were not only selected by the topological analysis but also were identified as prognostic risk factors and stage specific marker genes. In previous studies, these pivotal genes were involved in the pathophysiology and treatment of BC. CXCL8 is correlated with clinical BC stage and lymph node metastasis. A higher level of CXCL8 promotes the invasive capacity of BC cells[20;32]. Secretion of CXCL8 in BC cells is significantly up-regulated by vascular endothelial growth factors and promoted by the continuous action of estrogen and progesterone on BC cells [ 2 ]. IL1B is present in the microenvironment of most BC. High IL1B content is always associated with tumor aggressiveness [ 25 ]. Moreover, IL1B may play a pivotal role in regulating breast tumor growth and metastasis [ 18 ]. PTGS2 is involved in carcinogenesis, inhibition of apoptosis, immune response suppression, and tumor cell invasion. Furthermore, PTGS2 genetic variation is associated with BC susceptibility [4;7;27]. MMP9 is associated with BC invasive and metastatic [ 37 ]. Kruppel-like factor 9 could down-regulate MMP9 expression to inhibit breast cancer metastasis. The regulation of MMP9 expression by Arylamine N-acetyltransferase affected BC invasive [3; 29]. The expression of MMP-9 is correlated with metastasis and is associated with patient survival of BC [4; 36]. All these studies suggested that M. cordata may play an anti-BC role by the regulation of these pivotal genes. The molecular docking approach was also used to verify the interactions between M. cordata and its predicted pivotal targets. These results predicted that M. cordata may exert therapeutic effects against BC, at least in part, by the function of the following compounds: 6-cyanodihydrogensanguinarine, corysamine, oxychelirubine, berberrubine. The drugs with good drug ability should have water solubility, good BBB, no inhibition of CYP2D6, good absorption, and no hepatotoxicity. After ADMET analysis, four compounds exhibited desirable ADMET properties. but they all have hepatotoxicity potential, which needs to be addressed in the future. Previous studies demonstrated that berberrubine induced topoisomerase II-mediated DNA cleavage, and might be a new class of antitumor agent [ 22 ]. However, the researches regarding the anti-BC effect of these compounds are still sparse. These results indicated that these compounds are potential anti-cancer drugs after future verification. 4. Conclusion The current study demonstrated that M. cordata exerts therapeutic effects against BC, by modulating the function of the following proteins: CXCL8, IL1B, MMP9, and PTGS2. Furthermore, 6-cyanodihydrogensanguinarine, corysamine, oxychelirubine, and berberrubine could represent the most relevant bioactive compounds of M. cordata against BC. 5. Materials and Methods 5.2 Prediction of the targets of anti-BC targets of M. cordata Compounds of M. cordata were collected from previous research in the literature. The potential targets of BC were collected from the MalaCards database ( https://www.malacards.org/ ) and Online Mendelian Inheritance in Man (OMIM) database ( https://omim.org/ ). Then, the structure of bioactive compounds was imported to the SwissTargetPrediction ( http://www.swisstargetprediction.ch/ ) to predict the potential targets. All the obtained targets were standardized as UniProt IDs and gene names retrieved from the Uniprot KB database ( https://www.uniprot.org/ ). Using the Venn diagram drew M. cordata target genes and BC target genes overlapping. Next, the compound-target network were constructed by Cytoscape software (version 3.8.2, Cytoscape Consortium, San Diego). 5.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis The potential target of M. cordata on BC was uploaded to Metascape ( https://metascape.org/ ), and the data related to GO and KEGG were obtained. GO analysis was divided into three-part, including biological process (BP), cell composition (CC), and molecular function (MF). Then, the top 20 items with significant differences were mapped using the R package (version 3.6.0, R Foundation, Vienna, Austria). 5.4 Protein-protein interaction analysis The STRING database ( https://string-db.org/cgi/input.pl ) was performed to obtain the data from the protein-protein interactions (PPI) and analyzed it by Cytoscape software (version 3.8.2, Cytoscape Consortium, San Diego). Subsequently, the Molecular Complex Detection (MCODE) analyzed the PPI network. 5.5 Pathological stage analysis and survival analysis Pathological stage analysis, based on Gene Expression Profiling Interactive Analysis (GEPIA) database ( http://gepia.cancer-pku.cn/ ), generates expression violin plots based on patient pathological stages[ 38 ]. Survival analysis, based on Kaplan Meier plotter database ( http://kmplot.com/ ), identifies stage specific marker genes out of the prognostic risk factors[ 26 ]. 5.6 Molecular docking analysis The Autodock Vina software (version 1.1.2, Scripps Research, San Diego, USA) was used to performed Molecular docking analysis. 3D structures of M. cordata were constructed by the ChemBioDraw software (version 13.02, PerkinElmer Informatics, Waltham). Then, minimizing energy and optimizing structures of M. cordata by the ChemBioDraw software. The structure of protein was obtained from the Research Collaboratory for Structural Bioinformatics protein data bank ( https://www.rcsb.org/ ). Discovery Studio software (version 2016, Dassault Systèmes, Vélizy-Villacoublay, France) was used to optimize the crystal structures by the remove ligands and water. The docking affinity of docking models was acquired and shown in the heatmap which constructed by GraphPad Prim software. The best model of these ligands interacting with amino acid residues has the lowest binding affinity. PyMOL software (version 2.3.5, Schrödinger, Inc., New York, USA) and Discovery Studio software were employed to visualize the model of docking [ 44 ]. 5.7 Absorption, distribution, metabolism, elimination and toxicity (ADMET)analysis The ADMET module in Discovery Studio software was used to import the established small molecular compounds of compounds into software. Select Calculate Molecular Properties in small Molecules module and click ADMET descriptors for parameter setting, then click run to predict the absorption, distribution, metabolism, excretion and toxicity of compounds. The information of Aqueous solubility, Blood brain barrier penetration (BBB), Cytochrome P2D6 inhibition, Hepatotoxicity, Human intestinal absorption (HIA), and Plasma protein binding (PPB) about compound were obtained. Declarations Acknowledgments Not Applicable. Authors’ contributions Lei Zhang, and Kai Nan conceived and designed the study. Jing Huang, Yulong Peng, and Yang Cao performed the data analysis. Lei Zhang, Jing Huang, and Su Yin wrote the manuscript. All authors are responsible for reviewing data. All authors read and approved the final manuscript. Data Availability Statement Data derived from public domain resources. These data were derived from the following resources available in the public domain: Man database (https://omim.org/), MalaCards database (https://www.malacards.org/), SwissTargetPrediction database (http://www.swisstargetprediction.ch/), Uniprot KB database (https://www.uniprot.org/), Research Collaboratory for Structural Bioinformatics protein data bank (https://www.rcsb.org/), STRING (https://string-db.org/cgi/input.pl), and Metascape (https://metascape.org) databases, Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/), Kaplan Meier plotter database (http://kmplot.com/). Funding Declaration of interest statement The authors declare no conflicts of interest associated with the contents of this article. References Almeida I, Fernandes L, Biazi B, Vicentini V. 2017. Evaluation of the Anticancer Activities of the Plant Alkaloids Sanguinarine and Chelerythrine in Human Breast Adenocarcinoma Cells. Anticancer Agents Med Chem. 17: 1586-1592.doi:10.2174/1871520617666170213115132. Azenshtein E, Meshel T, Shina S, Barak N, Keydar I, Ben-Baruch A. 2005. The angiogenic factors CXCL8 and VEGF in breast cancer: regulation by an array of pro-malignancy factors. Cancer Letters. 217: 73-86.doi:10.1016/j.canlet.2004.05.024. Bai XY, Li SJ, Wang M, Li XH, Yang YY, Xu ZW, Li B, Li Y, Xia K, Chen H, et al.2018. Kruppel-like factor 9 down-regulates matrix metalloproteinase 9 transcription and suppresses human breast cancer invasion. Cancer Lett. 412: 224-235.doi:10.1016/j.canlet.2017.10.027. Bottino J, Gelaleti GB, Maschio LB, Jardim-Perassi BV, de Campos Zuccari DA. 2014.Immunoexpression of ROCK-1 and MMP-9 as prognostic markers in breast cancer. Acta Histochem. 116: 1367-73.doi:10.1016/j.acthis.2014.08.009. Deng F, Ma Y, Liang L, Zhang P, Feng J. 2018. The pro-apoptosis effect of sinomenine in renal carcinoma via inducing autophagy through inactivating PI3K/AKT/mTOR pathway. Biomed Pharmacother. 97: 1269-1274.doi:10.1016/j.biopha.2017.11.064. Dong X, Zhang M, Wang K, Liu P, Guo D, Zheng X, Ge X. 2013. Sanguinarine inhibits vascular endothelial growth factor release by generation of reactive oxygen species in MCF-7 human mammary adenocarcinoma cells. Biomed Res Int. 2013: 517698.doi:10.1155/2013/517698. Dossus L, Kaaks R, Canzian F, Albanes D, Berndt SI, Boeing H, Buring J, Chanock SJ, Clavel-Chapelon F, Feigelson HS,et al.2010. PTGS2 and IL6 genetic variation and risk of breast and prostate cancer: results from the Breast and Prostate Cancer Cohort Consortium (BPC3). Carcinogenesis. 31: 455-461.doi:10.1093/carcin/bgp307. Dvorak Z, Kuban V, Klejdus B, Hlavac J, Vicar J, Ulrichova J, Simanek V. 2006. Quaternary benzo c phenanthridines sanguinarine and chelerythrine: A review of investigations from chemical and biological studies. Heterocycles. 68: 2403-2422.doi:10.1016/j.fct.2006.04.016 Ellis M, Perou C.2013. The genomic landscape of breast cancer as a therapeutic roadmap. Cancer Discov. 3: 27-34.doi:10.1158/2159-8290.CD-12-0462. Franz C, Bauer R, Carle R, Tedesco D, Tubaro A. Zitterl-Eglseer K. 2005. ASSESSMENT OF PLANTS/HERBS, PLANT/HERB EXTRACTS AND THEIR NATURALLY OR SYNTHETICALLY PRODUCED COMPONENTS AS “ADDITIVES” FOR USE IN ANIMAL PRODUCTION. Parma: EFSA Supporting Publications.p.140-152. Harbeck N, Gnant M. 2017. Breast cancer. The Lancet.; 389: 1134-1150.doi:10.1016/S0140-6736(16)31891-8. Hashim D, Boffetta P, La Vecchia C, Rota,M, Bertuccio P, Malvezzi M, Negri E. 2016. The global decrease in cancer mortality: trends and disparities. Annals of Oncology. 27: 926-933.doi:10.1093/annonc/mdw027. Herbert J, Augereau J, Gleye J, Maffrand J. 1990. Chelerythrine is a potent and specific inhibitor of protein kinase C. Biochem Biophys Res Commun. 172: 993-999.doi:10.1016/0006-291x(90)91544-3. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology. 2008; 4: 682-690.doi:10.1038/nchembio.118. Hurvitz S A, Hu Y, O'Brien N, Finn RS. 2013.Current approaches and future directions in the treatment of HER2-positive breast cancer. Cancer Treat Rev. 39: 219-29.doi：10.1016/j.ctrv.2012.04.008. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D 2011.Global Cancer Statistics. CA:A Cancer Journal for Clinicians. 61(2):134.doi:10.3322/caac.20107. Jia L, Lin H, Oppenheim J, Howard O, Fan H, Zhao Z, Farrar W, Zhang Y, Colburn N, Young MR, et al. 2017. US National Cancer Institute-China Collaborative Studies on Chinese Medicine and Cancer. Journal of the National Cancer Institute Monographs. 52: 58-61.doi:10.1093/jncimonographs/lgx007. Jin L, Yuan RQ, Fuchs A, Yao Y, Joseph A, Schwall R, Schnitt SJ, Guida A, Hastings HM, Andres J, et al. 1997.Expression of interleukin-1 beta in human breast carcinoma. Cancer. 80: 421-434.doi: 10.1002/(sici)1097-0142(19970801)80:33.0.co;2-z. Jin Y, Chen W, Yang H, Yan Z, Lai Z, Feng J, Peng J, Lin J. 2017.Scutellaria barbata D. Don inhibits migration and invasion of colorectal cancer cells via suppression of PI3K/AKT and TGF-β/Smad signaling pathways. Exp Ther Med. 14: 5527-5534.doi:10.3892/etm.2017.5242. Johnson K, Ceglowski J, Roweth H, Forward J, Tippy M, El-Husayni S, Kulenthirarajan R, Malloy MW, Machlus KR, Chen WY,et al. 2019.Aspirin inhibits platelets from reprogramming breast tumor cells and promoting metastasis. Blood Adv. 3: 198-211.doi:10.1182/bloodadvances.2018026161. Khin M, Jones A, Cech N, Caesar L. 2018. Macleaya cordata Phytochemical Analysis and Antimicrobial Efficacy of against Extensively Drug-Resistant. Nat Prod Commun. 13: 1479-1483.doi:10.1177/1934578X1801301117. Kim S, Kwon Y, Kim J, Muller M, Chung I. 1998.Induction of topoisomerase II-mediated DNA cleavage by a protoberberine alkaloid, berberrubine. Biochemistry. 37: 16316-16324.doi:10.1021/bi9810961. Kim S, Lee J, You D, Jeong Y, Jeon M, Yu J, Kim SW, Nam SJ, Lee JE. 2018. Berberine Suppresses Cell Motility Through Downregulation of TGF-β1 in Triple Negative Breast Cancer Cells. Cell Physiol Biochem. 45: 795-807.doi:10.1159/000487171. Kim S, Lee T, Leem J, Choi K, Park J, Kwon T. 2008.Sanguinarine-induced apoptosis: generation of ROS, down-regulation of Bcl-2, c-FLIP, and synergy with TRAIL. J Cell Biochem. 104: 895-907.doi:10.1002/jcb.21672. Kurtzman SH, Anderson KH, Wang YP, Miller LJ, Renna M, Stankus M, Lindquist RR, Barrows G, Kreutzer DL.1999.Cytokines in human breast cancer: IL-1 alpha and IL-1 beta expression. Oncology Reports. 6: 65-70.doi:10.3892/or.6.1.65. Lanczky A, Gyorffy B. 2021.Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res. 23:e27633. doi:10.2196/27633. Langsenlehner U, Yazdani-Biuki B, Eder T, Renner W, Wascher TC, Paulweber B, Weitzer W, Samonigg H, Krippl P. 2006.The cyclooxygenase-2 (PTGS2) 8473T > C polymorphism is associated with breast cancer risk. Clinical Cancer Research. 12: 1392-1394.doi:10.1158/1078-0432.CCR-05-2055. Li CM, Yang XY, Zhong YR, Yu JP. 2016.Chemical composition, antioxidant and antimicrobial activity of the essential oil from the leaves of Macleaya cordata (Willd) R. Br. Nat Prod Res. 30: 438-442.doi:10.1080/14786419.2015.1017490. Li PC, Butcher NJ, Minchin RF. 2019. Arylamine N - Acetyltransferase 1 Regulates Expression of Matrix Metalloproteinase 9 in Breast Cancer Cells: Role of Hypoxia-Inducible Factor 1-alpha. Molecular Pharmacology. 96: 573-579.doi:10.1124/mol.119.117432. Lin W, Huang J, Yuan Z, Feng S, Xie Y, Ma W. 2017.Protein kinase C inhibitor chelerythrine selectively inhibits proliferation of triple-negative breast cancer cells. Sci Rep. 7: 2022.doi:10.1038/s41598-017-02222-0. Lou JS, Yao P, Tsim KWK. 2018. Cancer Treatment by Using Traditional Chinese Medicine Probing Active Compounds in Anti-multidrug Resistance During Drug Therapy. Current Medicinal Chemistry. 25: 5128-5141.doi: 10.2174/0929867324666170920161922. Ma Y, Ren Y, Dai Z, Wu C, Ji Y, Xu J. 2017.IL-6, IL-8 and TNF-α levels correlate with disease stage in breast cancer patients. Adv Clin Exp Med. 26: 421-426.doi:10.17219/acem/62120. Meng H, Peng N, Yu M, Sun X, Ma Y, Yang G, Wang X. 2017. Treatment of triple-negative breast cancer with Chinese herbal medicine: A prospective cohort study protocol. Medicine. 96:1-5.doi: 10.1097/MD.0000000000008408. Meng Y, Liu Y, Hu Z, Zhang Y, Ni J, Ma Z, Liao H, Wu Q, Tang Q.2018. Sanguinarine Attenuates Lipopolysaccharide-induced Inflammation and Apoptosis by Inhibiting the TLR4/NF-κB Pathway in H9c2 Cardiomyocytes. Curr Med Sci. 38: 204-211.doi: 10.1007/s11596-018-1867-4. Ouyang L, Su X, He D, Chen Y, Ma,M, Xie Q, Yao S. 2010.A study on separation and extraction of four main alkaloids in Macleaya cordata (Willd) R. Br. with strip dispersion hybrid liquid membrane. J Sep Sci. 33: 2026-2034.doi:10.1002/jssc.201000103. Puzovic V, Brcic I, Ranogajec I, Jakic-Razumovic J. 2014.Prognostic values of ETS-1, MMP-2 and MMP-9 expression and co-expression in breast cancer patients. Neoplasma. 61: 439-446.doi:10.4149/neo_2014_054. Somiari SB, Shriver CD, Heckman C, Olsen C, Hu H, Jordan R,Arciero C, Russell S,Garguilo G,Hooke J,et al. 2006.Plasma concentration and activity of matrix metalloproteinase 2 and 9 in patients with breast disease, breast cancer and at risk of developing breast cancer. Cancer Lett. 233: 98-107.doi:10.1016/j.canlet.2005.03.003. Tang Z, Tang ZF, Li CW, Kang BX, Gao G, Li C, Zhang ZM. 2017. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res.45: W98-W102.doi: 10.1093/nar/gkx247. Wang X, Decker C, Zechner L, Krstin S, Wink M. 2019.In vitro wound healing of tumor cells: inhibition of cell migration by selected cytotoxic alkaloids. BMC Pharmacol Toxicol. 20: 1-12.doi:10.1186/s40360-018-0284-4. Wang X, Fang G, Pang Y. 2018.Chinese Medicines in the Treatment of Prostate Cancer: From Formulas to Extracts and Compounds. Nutrients. 10: 283.doi:10.3390/nu10030283. Wyatt GL, Crump LS, Young CM, Wessells VM, McQueen CM, Wall SW, Gustafson TL, Fan YY, Chapkin RS,Porter WW,et al. 2019.Cross-talk between SIM2s and NFκB regulates cyclooxygenase 2 expression in breast cancer. Breast Cancer Res.21: 131.doi: 10.1186/s13058-019-1224-y. Yu HY, Kim PM, Sprecher E, Trifonov V, Gerstein M. 2007.The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 3: 713-720.doi:10.1371/journal.pcbi.0030059. Zeng L, Yang K.2017. Exploring the pharmacological mechanism of Yanghe Decoction on HER2-positive breast cancer by a network pharmacology approach. Journal of Ethnopharmacology. 199: 68-85.doi:10.1016/j.jep.2017.01.045. Zhang L, Yang K, Wang M, Zeng LZ, Sun EZ, Zhang FX, Cao Z, Zhang XX, Zhang H, Guo ZJ. 2020.Exploring the Mechanism of Cremastra Appendiculata (SUANPANQI) against Breast Cancer by Network Pharmacology and Molecular Docking. Computational Biology and Chemistry. 94: 107396.doi:10.1016/j.compbiolchem.2020.107396 Zhu Y, Pan Y, Zhang G, Wu Y, Zhong W, Chu C, Qian Y, Zhu G. 2018.Chelerythrine Inhibits Human Hepatocellular Carcinoma Metastasis in Vitro. Biol Pharm Bull. 41: 36-46.doi:10.1248/bpb.b17-00451. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4945731","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":359925544,"identity":"a928d446-5a4f-4526-8fc0-87bfbfba5838","order_by":0,"name":"Lei Zhang","email":"","orcid":"","institution":"Shaanxi Provincial Hospital of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":359925545,"identity":"3626b69a-4024-4c50-84ef-12e3bd11034c","order_by":1,"name":"Jing Huang","email":"","orcid":"","institution":"Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Huang","suffix":""},{"id":359925549,"identity":"45d7b76a-234f-41f4-a8d3-05b28ad37730","order_by":2,"name":"Yulong Peng","email":"","orcid":"","institution":"Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yulong","middleName":"","lastName":"Peng","suffix":""},{"id":359925550,"identity":"f91fdd9e-55d5-4ab8-aa6d-abf914e7d39c","order_by":3,"name":"Su Yin","email":"","orcid":"","institution":"Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"Yin","suffix":""},{"id":359925551,"identity":"8c6a74f6-a820-4ac6-a27d-291fd1601559","order_by":4,"name":"Yang Cao","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Cao","suffix":""},{"id":359925552,"identity":"5cec9be0-d441-409f-9f37-d41da45999b1","order_by":5,"name":"Kai Nan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDCCAxCCh5+BIQHEZGwgWotkA0NiA0laGAwOQFQT1sJ3++zBzwVn7GSMbyQ8f8zDYCO74QDzswf4tEiey0uWnnEjmcfszIHEZh6GNOMNB9jMDfBpMTjDYyDN84GZx+x4A0jL4cQNB3jYJAhoMf7N86Gex7iZAaTlP1FazKR5bhzmMWAH23KAsBZJoBZrnjPHeSSAfpk5xyDZeOZhNjO8WviADrvNc6zann9GTsKHNxV2sn3Hm5/h1YIEeBKA7gTSzESqBwL2A8SrHQWjYBSMghEFAL+uShJKDju2AAAAAElFTkSuQmCC","orcid":"","institution":"Honghui Hospital, Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Nan","suffix":""}],"badges":[],"createdAt":"2024-08-20 14:22:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4945731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4945731/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66872823,"identity":"b95ebfaa-6461-4e72-bab4-8fc1aaa554c1","added_by":"auto","created_at":"2024-10-17 10:01:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":464104,"visible":true,"origin":"","legend":"\u003cp\u003eThe graphical abstract of this study.\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/0d2f8657b82f60ee77d80142.png"},{"id":66873725,"identity":"1f07c3f9-7c0f-46e2-9379-a2a809604559","added_by":"auto","created_at":"2024-10-17 10:09:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5775852,"visible":true,"origin":"","legend":"\u003cp\u003eParameters used to assess drug-likeness rules (A) molecular weight, (B) partition coefficient, (C) hydrogen bond acceptors, (D) hydrogen bond donors, (E) rotatable bonds, and (F) polar surface area.\u003c/p\u003e","description":"","filename":"FIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/7a62297f706435f3d60aab93.png"},{"id":66873726,"identity":"38a503b2-764c-40be-9b88-ae586ea54ba3","added_by":"auto","created_at":"2024-10-17 10:09:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6755751,"visible":true,"origin":"","legend":"\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eVenn diagram of targets of \u003cem\u003eM. cordata\u003c/em\u003e and BC. (B) Compound-target network. Rhombus on the inner circle represent compounds, and circle on the outer circle represent target genes. Each compound was linked with their potential targets. The colder the color is, the more the degree of the nodes has.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/5564c64f6c1bb1a48ebcf883.png"},{"id":66872824,"identity":"b7d85fb4-28b7-4e9b-9e05-c04eb85d6879","added_by":"auto","created_at":"2024-10-17 10:01:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3118670,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG pathway enrichment analyses of\u003cem\u003e M. cordata\u003c/em\u003eagainst BC (A) The top 20 cellular component pathways’ bubble chart; (B) The top 20 biological process pathways’ bubble chart; (C) The top 20 molecular function pathways’ bubble chart; (D) The top 20 KEGG signalling pathways’ bubble chart(E) The distribution of targets in the PI3K/AKT signalling pathway. The pink rectangles represent potential targets for \u003cem\u003eM. cordata\u003c/em\u003e against BC.\u003c/p\u003e","description":"","filename":"FIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/885de3e040ee2b477322033c.png"},{"id":66872828,"identity":"5dbe9162-58ee-4fd9-950a-af144edbcfa0","added_by":"auto","created_at":"2024-10-17 10:01:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14490841,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of \u003cem\u003eM. cordata\u003c/em\u003e anti-BC (A) included six clusters (B-I). The colder the dot color is, the more the degree the nodes have. Proteins are represented by network nodes, and protein-protein associations are represented by edges.\u003c/p\u003e","description":"","filename":"FIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/0eda2ef8fc878952f5b71d30.png"},{"id":66873967,"identity":"fc352618-6258-49bb-a7d2-68b949afca3f","added_by":"auto","created_at":"2024-10-17 10:17:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3043948,"visible":true,"origin":"","legend":"\u003cp\u003ePathological stage analysis of patients with\u003cstrong\u003e \u003c/strong\u003eBC, based on GEPIA database, was performed on pivotal target genes. CXCL8 (A), IL1B (B), MMP9 (C), and PTGS2(D). Survival analysis of patients with BC, based on Kaplan Meier plotter database, was performed on pivotal target genes. CXCL8 (E), IL1B (F), MMP9 (G), and PTGS2(H).The numbers represent the number of patient cases used to make the image curve.\u003c/p\u003e","description":"","filename":"FIG6.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/a179475e3b1bff61ab7c7d8c.png"},{"id":66872829,"identity":"6ce7488d-1845-4463-bf77-bc1adc244329","added_by":"auto","created_at":"2024-10-17 10:01:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3582464,"visible":true,"origin":"","legend":"\u003cp\u003e(A)The heat map of binding affinity between the bioactive compounds and key targets is based on molecular docking. The molecular docking results of \u003cem\u003eM. cordata\u003c/em\u003e anti-BC key compounds and pivotal targets. The docking model of 6-cycanodihydrogensanguinarine-CXCL8 (B), the docking model of corysamine-IL1B (C), the docking model of oxychelirubine- MMP9 (D), and the docking model of berberrubine-PTGS2(E).\u003c/p\u003e","description":"","filename":"FIG7.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/27086599c06e3a7a5fb3ab9c.png"},{"id":66872825,"identity":"01304cff-decd-442e-a321-894c24ed83d0","added_by":"auto","created_at":"2024-10-17 10:01:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":647457,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of intestinal absorption and blood brain barrier penetration of four key compounds.\u003c/p\u003e","description":"","filename":"FIG8.png","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/af23d5423eb074645a8dd65a.png"},{"id":66875051,"identity":"1de0815a-6af0-4045-bae0-55e732923d6d","added_by":"auto","created_at":"2024-10-17 10:33:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":33351136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/023187f6-e86e-46c4-bf5e-dc4eff2ce70e.pdf"},{"id":66873728,"identity":"2d8a30ea-50eb-41c1-a949-1e468860287e","added_by":"auto","created_at":"2024-10-17 10:09:07","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3336704,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-4945731/v1/123564ce3fd5e65cdbef5b43.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Mechanism of Macleaya cordata (Willd) R. Br. Against Breast Cancer by Network Pharmacology and Molecular Docking","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWorldwide, breast cancer (BC) is one of the most common cancers endangering women's health[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Current treatment of BC includes surgery, chemotherapy, radiation therapy, targeted therapy, and endocrine therapy[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although therapies for BC patients have been developed, BC is still the most common cause of death from cancer in developing countries[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, it is significant to explore new effective and safe drugs for BC therapy.\u003c/p\u003e \u003cp\u003eIn recent years, phytochemicals have attached much attention and were widely used in clinical cancer treatment due to their advantages in immune strengthening, chemotherapy attenuation, and metastasis inhibition [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Besides, previous research has revealed that the combined application of traditional chemotherapy and Chinese medicine could reverse the drug resistance and reduce the toxicity of chemotherapy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eM. cordata\u003c/em\u003e, a plant of the Papaveraceae family, is a significant source of quaternary benzo[c]phenanthridine alkaloids (QBA)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Particularly, sanguinarine and chelerythrine are the principal members of QBA obtained from \u003cem\u003eM. cordata\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous researches have proved that sanguinarine and chelerythrine in \u003cem\u003eM. cordata\u003c/em\u003e have anti-proliferative activity in BC cells [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It has been displayed that the apoptosis induced by sanguinarine accompanied the generation of reactive oxygen species in BC cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Chelerythrine is widely used as a specific inhibitor of protein kinase C and could selectively inhibited the proliferation of triple-negative BC cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the compounds and anti-BC targets of \u003cem\u003eM. cordata\u003c/em\u003e in treating BC have not been systemically explored.\u003c/p\u003e \u003cp\u003eCurrently, network pharmacology can provide an effective solution to identify the association between diseases, targets, and drugs [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Molecular docking, a theoretical simulation approach, demonstrates the drug-target interactions by predicting its binding mode and binding affinity.\u003c/p\u003e \u003cp\u003eThis study revealed the bioactive compounds and underlying mechanisms of \u003cem\u003eM. cordata\u003c/em\u003e anti-BC through network pharmacology and molecular docking approach. The current study indicated the \u003cem\u003eM. cordata\u003c/em\u003e against BC through multiple targets and pathways and related mechanisms that may provide a potential treatment scheme for the therapeutics of BC. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the graphical abstract of this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 BC genes targeted by M. cordata\u003c/h2\u003e \u003cp\u003eOne hundred and ninety compounds of \u003cem\u003eM. cordata\u003c/em\u003e were collected from previous research in the literature. All these compounds were screened by drug-likeness rules, including Lipinski rules and Veber rules. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that the drug-like properties of \u003cem\u003eM. cordata\u003c/em\u003e compounds. Finally, a total of 134 bioactive compounds that met both Lipinski and Veber rules were identified (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in supplementary material).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTotally, 994 potential targets for the 134 bioactive compounds from the SwissTargetPrediction database were collected. Meanwhile, 1357 BC-related targets were obtained from the MalaCards and OMIM database. To further discover the mechanism of \u003cem\u003eM. cordata\u003c/em\u003e against BC, the target genes of BC were linked with the target genes of \u003cem\u003eM. cordata\u003c/em\u003e. Two hundred and forty-four overlapping targets with potential roles in \u003cem\u003eM. cordata\u003c/em\u003e against BC mechanism were identified after the \u003cem\u003eM. cordata\u003c/em\u003e target genes and BC target genes were overlapped using FUNRICH software (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Next, the compound-target network were constructed by Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 GO analysis and KEGG enrichment analysis of target genes.\u003c/h2\u003e \u003cp\u003eTo verify the targets for \u003cem\u003eM. cordata\u003c/em\u003e against BC mechanism, the GO and KEGG pathway enrichment analysis were performed and visualized as bubble chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). GO analysis of targets of \u003cem\u003eM. cordata\u003c/em\u003e against BC was conducted regarding three aspects: BP, MF, and CC. The GO item including regulation of MAPK, positive regulation of cell migration and positive regulation of kinase activity, ect. The KEGG enrichment analysis showed that these targets were involved in the PI3K/AKT, FoxO, and MAPK signal pathways. Above all, the PI3K/AKT signalling pathway was suggested as the most enriched pathway. \u003cem\u003eM. Cordata\u003c/em\u003e plays an anti-BC role through multiple targets involved in multiple processes in this pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 PPI network of targets for M. cordata against BC.\u003c/h2\u003e \u003cp\u003eThe PPI network of targets were visualized and analyzed by Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The degree value represents the link number of each node. The betweenness centrality describes the role of a node as a pivot in a network[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter network topological analysis, 51 genes analyzed degree and betweenness centrality values above the average (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Hence, these 51 genes encode proteins with key roles in BC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTarget genes with potentially roles in \u003cem\u003eM. cordata\u003c/em\u003e against BRCA mechanism\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBetweenness Centrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAPK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAPK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSP90AA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTNNB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAPK8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIRT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEP300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERBB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRHOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAPK14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEZH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDC42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXIAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCL2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRELA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNR3C1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPARG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIK3R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSK3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIF1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTPN11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the PPI network was divided into several clusters using MCODE plug-in (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking parameters and corresponding calculation results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecule Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy\u003c/p\u003e \u003cp\u003e(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-cyanodihydrogensanguinarine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys25, Ile22, Arg68, Phe65, Pro19, His18, Lys67, Lys64, Lys20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorysamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePro31, Met130, Phe133, Ser125, Asp142, Gln141, Gly135, Leu134, Gly136, Trp120, Lys77, Pro131, Thr79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxychelirubine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyr423, Met422, Pro415, Arg424, Tyr420, Thr426, Pro429, Glu416, Leu418, Leu397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBerberrubine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyr136, Asn34, Gly135, Pro154, Val155, Cys47, Gln461, His39, Leu52, Pro153, Cys36, Peo156, Met48, Ser49, Trp323, Gln327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Correlation analysis of gene expression and patient pathological stage, and survival, respectively\u003c/h2\u003e \u003cp\u003eAfter network topological analysis, a correlation analysis between the expression of 51 genes and the pathological stage and the overall survival of breast cancer patients was conducted. Pathological stage analysis, based on GEPIA, generates expression violin plots based on patient pathological stages. By analyzing the difference in gene expression between tumor tissue and adjacent normal tissue, the stage specific marker genes of the pathological stage were identified. (Pr\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Next, survival analysis was used to identify the prognostic risk gene factor (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Finally, four genes(CXCL8, IL1B, MMP9,and PTGS2) were found to be significantly associated with both stage and survival of patients, which suggested that they may play an important role in the effect of \u003cem\u003eM. cordata\u003c/em\u003e on BC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Molecular docking analysis of bioactive compounds and pivotal target genes\u003c/h2\u003e \u003cp\u003eAfter network pharmacology analysis, 4 pivotal target proteins: CXCL8 (PDB code:2il8), IL1B (PDB code:4dep), MMP9 (PDB code:1l6j), and PTGS2 (PDB code:5f19) are vital targets on which \u003cem\u003eM. cordata\u003c/em\u003e acts against BC targets. To further validate the \u003cem\u003eM. cordata\u003c/em\u003e against BC mechanism, molecular docking was carried out between 134 \u003cem\u003eM. cordata\u003c/em\u003e compounds and 4 pivotal target genes. After molecular docking, the docking affinity of each compound and protein was shown in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). As seen in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the respective binding energy for the 4 alkaloids (6-cyanodihydrogensanguinarine, Corysamine, Oxychelirubine, and Berberrubine) and the 4 protein crystal structures corresponding to the core target genes (CXCL8, IL1B, MMP9, and PTGS2) were significantly greater than other compounds, which indicates that these 4 compounds could be the key compounds in \u003cem\u003eM. cordata\u003c/em\u003e working on BC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking parameters and corresponding calculation results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecule Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy\u003c/p\u003e \u003cp\u003e(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-cyanodihydrogensanguinarine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys25, Ile22, Arg68, Phe65, Pro19, His18, Lys67, Lys64, Lys20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorysamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePro31, Met130, Phe133, Ser125, Asp142, Gln141, Gly135, Leu134, Gly136, Trp120, Lys77, Pro131, Thr79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxychelirubine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyr423, Met422, Pro415, Arg424, Tyr420, Thr426, Pro429, Glu416, Leu418, Leu397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBerberrubine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyr136, Asn34, Gly135, Pro154, Val155, Cys47, Gln461, His39, Leu52, Pro153, Cys36, Peo156, Met48, Ser49, Trp323, Gln327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 ADMET analysis of key compounds\u003c/h2\u003e \u003cp\u003eThe so selected four key compounds were assessed for the ADMET studies (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Four compounds show good (0) absorption properties. 6-cyanodihydrogensanguinarine and Corysamine aqueous solubility is very low, but possible (-8.0\u0026thinsp;\u0026lt;\u0026thinsp;logSw \u0026lt; -6.0). Oxychelirubine and Berberrubine aqueous solubility is low (-6.0\u0026thinsp;\u0026lt;\u0026thinsp;logSw \u0026lt; -4.1). Four compounds are likely to be highly bound to carrier proteins in the blood (PPB \u0026gt;-2.209). Four compounds are likely to be hepatotoxic (hepatotoxicity \u0026gt;-2.209). This model predicts blood-brain penetration (blood brain barrier, BBB) after oral administration. Oxychelirubine and 6-cyanodihydrogensanguinarine have high penetrants (0\u0026thinsp;\u0026le;\u0026thinsp;logBB\u0026thinsp;\u0026lt;\u0026thinsp;0.7) of BBB after oral administration. Corysamine and Berberrubine have medium penetrants (-0.52\u0026thinsp;\u0026lt;\u0026thinsp;logBB\u0026thinsp;\u0026lt;\u0026thinsp;0) of BBB after oral administration. The classification whether a compound is an CYP2D6 inhibitor using the cut off Bayesian score of 0.161. Four compounds don\u0026rsquo;t have potential to be an CYP2D6 inhibitor(CYP2D6 inhibitor\u0026thinsp;\u0026lt;\u0026thinsp;0,161).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eADMET descriptors\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAqueous solubility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlood brain barrier penetration (BBB)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCytochrome P450 (CYP450) 2D6 inhibition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHepatotoxicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman intestinal absorption (HIA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePlasma protein binding\u003c/p\u003e \u003cp\u003e(PPB)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxychelirubine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.96479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.47019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.17954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-cyanodihydrogensanguinarine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.43352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.58974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.45352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorysamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.73701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.87481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.69082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBerberrubine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.99231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.32292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eBC is the most frequently diagnosed cancer and the second-leading cause of cancer deaths in women [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eM. cordata\u003c/em\u003e, a plant of the Papaveraceae family, is a commonly used agent in traditional Chinese medicine because of its extensive bioactivities, including antimicrobial, anti-inflammatory, and antitumor [28;34;35]. Antitumor activity of \u003cem\u003eM. cordata\u003c/em\u003e has attracted much attention in recent years. The main chemical constituents of \u003cem\u003eM. cordata\u003c/em\u003e are QBA alkaloids that are responsible for the pharmacological effects, including sanguinarine and chelerythrine[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The anti-tumor activities of sanguinarine have been reported on several cancer cell lines, such as BC, lung cancer, melanoma and pancreatic cancer cells[1; 21; 39]. In this study, the underlying mechanisms of \u003cem\u003eM. cordata\u003c/em\u003e on the treatment of BC were demonstrated by using network pharmacology and molecular docking approach.\u003c/p\u003e \u003cp\u003eNetwork pharmacology is an intersectional product of multiple disciplines, and it can identify the targets and further reveal the drug-target interactions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Molecular docking, is a powerful technology in drug-target interaction verification through filtering the spatial matching design and binding energy. There, the combination of molecular docking and network pharmacology techniques is more conducive to drug target discovery[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This study combined molecular docking with network pharmacology techniques to predict the target of \u003cem\u003eM. Cordata\u003c/em\u003e for BC treatment.\u003c/p\u003e \u003cp\u003eIn this study, the interactions between \u003cem\u003eM. cordata\u003c/em\u003e compounds and its potential targets in BC, as well as numerous signalling pathways in which \u003cem\u003eM. cordata\u003c/em\u003e anti-BC targets participate by integrating information from the publicly available databases and previous researches were revealed. A total of 134 bioactive compounds of \u003cem\u003eM. cordata\u003c/em\u003e were found from previous researches, including sanguinarine and berberine. It has been reported that sanguinarine induced apoptosis in human mammary MCF-7 BC cells through the inhibition of VEGF release, which was induced by the generation of reactive oxygen species [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Another research demonstrated that berberine suppressed cell motility through the downregulation of transforming growth factor-β1 in BC cells [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Based on MalaCards, OMIM, and SwissTargetPrediction database, target genes which were both BC-related targets and targets of \u003cem\u003eM. cordata\u003c/em\u003e compounds were selected. After KEGG pathway enrichment analysis, the PI3K/AKT, FoxO, and MAPK signalling pathways were selected for \u003cem\u003eM. cordata\u003c/em\u003e to perform its anti-BC effects by regulating these multiple pathways.\u003c/p\u003e \u003cp\u003eThe PI3K/AKT signal pathway is frequently activated in BC[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cem\u003eM. cordata\u003c/em\u003e may play an anti-BC role through PI3K/AKT signal pathway. Studies have shown that many traditional Chinese medicines exert their antitumor effects through PI3K/AKT pathway [5;19]. Especially, previous studies suggested that chelerythrine, one of \u003cem\u003eM. cordata\u003c/em\u003e compounds, could inhibit the metastasis of human hepatocellular carcinoma cells by down-regulating the expression of MMP-2/9 mainly via PI3K/AKT/mTOR signal pathway [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFour pivotal genes (CXCL8, IL1B, MMP9, and PTGS2) were not only selected by the topological analysis but also were identified as prognostic risk factors and stage specific marker genes. In previous studies, these pivotal genes were involved in the pathophysiology and treatment of BC. CXCL8 is correlated with clinical BC stage and lymph node metastasis. A higher level of CXCL8 promotes the invasive capacity of BC cells[20;32]. Secretion of CXCL8 in BC cells is significantly up-regulated by vascular endothelial growth factors and promoted by the continuous action of estrogen and progesterone on BC cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. IL1B is present in the microenvironment of most BC. High IL1B content is always associated with tumor aggressiveness [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, IL1B may play a pivotal role in regulating breast tumor growth and metastasis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. PTGS2 is involved in carcinogenesis, inhibition of apoptosis, immune response suppression, and tumor cell invasion. Furthermore, PTGS2 genetic variation is associated with BC susceptibility [4;7;27]. MMP9 is associated with BC invasive and metastatic [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Kruppel-like factor 9 could down-regulate MMP9 expression to inhibit breast cancer metastasis. The regulation of MMP9 expression by Arylamine N-acetyltransferase affected BC invasive [3; 29]. The expression of MMP-9 is correlated with metastasis and is associated with patient survival of BC [4; 36]. All these studies suggested that \u003cem\u003eM. cordata\u003c/em\u003e may play an anti-BC role by the regulation of these pivotal genes.\u003c/p\u003e \u003cp\u003eThe molecular docking approach was also used to verify the interactions between \u003cem\u003eM. cordata\u003c/em\u003e and its predicted pivotal targets. These results predicted that \u003cem\u003eM. cordata\u003c/em\u003e may exert therapeutic effects against BC, at least in part, by the function of the following compounds: 6-cyanodihydrogensanguinarine, corysamine, oxychelirubine, berberrubine. The drugs with good drug ability should have water solubility, good BBB, no inhibition of CYP2D6, good absorption, and no hepatotoxicity. After ADMET analysis, four compounds exhibited desirable ADMET properties. but they all have hepatotoxicity potential, which needs to be addressed in the future. Previous studies demonstrated that berberrubine induced topoisomerase II-mediated DNA cleavage, and might be a new class of antitumor agent [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the researches regarding the anti-BC effect of these compounds are still sparse. These results indicated that these compounds are potential anti-cancer drugs after future verification.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe current study demonstrated that \u003cem\u003eM. cordata\u003c/em\u003e exerts therapeutic effects against BC, by modulating the function of the following proteins: CXCL8, IL1B, MMP9, and PTGS2. Furthermore, 6-cyanodihydrogensanguinarine, corysamine, oxychelirubine, and berberrubine could represent the most relevant bioactive compounds of \u003cem\u003eM. cordata\u003c/em\u003e against BC.\u003c/p\u003e"},{"header":"5. Materials and Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Prediction of the targets of anti-BC targets of M. cordata\u003c/h2\u003e \u003cp\u003eCompounds of \u003cem\u003eM. cordata\u003c/em\u003e were collected from previous research in the literature. The potential targets of BC were collected from the MalaCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.malacards.org/\u003c/span\u003e\u003cspan address=\"https://www.malacards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Online Mendelian Inheritance in Man (OMIM) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Then, the structure of bioactive compounds was imported to the SwissTargetPrediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict the potential targets. All the obtained targets were standardized as UniProt IDs and gene names retrieved from the Uniprot KB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing the Venn diagram drew \u003cem\u003eM. cordata\u003c/em\u003e target genes and BC target genes overlapping. Next, the compound-target network were constructed by Cytoscape software (version 3.8.2, Cytoscape Consortium, San Diego).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis\u003c/h2\u003e \u003cp\u003eThe potential target of \u003cem\u003eM. cordata\u003c/em\u003e on BC was uploaded to Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/\u003c/span\u003e\u003cspan address=\"https://metascape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the data related to GO and KEGG were obtained. GO analysis was divided into three-part, including biological process (BP), cell composition (CC), and molecular function (MF). Then, the top 20 items with significant differences were mapped using the R package (version 3.6.0, R Foundation, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Protein-protein interaction analysis\u003c/h2\u003e \u003cp\u003eThe STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/cgi/input.pl\u003c/span\u003e\u003cspan address=\"https://string-db.org/cgi/input.pl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was performed to obtain the data from the protein-protein interactions (PPI) and analyzed it by Cytoscape software (version 3.8.2, Cytoscape Consortium, San Diego). Subsequently, the Molecular Complex Detection (MCODE) analyzed the PPI network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Pathological stage analysis and survival analysis\u003c/h2\u003e \u003cp\u003ePathological stage analysis, based on Gene Expression Profiling Interactive Analysis (GEPIA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), generates expression violin plots based on patient pathological stages[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Survival analysis, based on Kaplan Meier plotter database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/\u003c/span\u003e\u003cspan address=\"http://kmplot.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), identifies stage specific marker genes out of the prognostic risk factors[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Molecular docking analysis\u003c/h2\u003e \u003cp\u003eThe Autodock Vina software (version 1.1.2, Scripps Research, San Diego, USA) was used to performed Molecular docking analysis. 3D structures of \u003cem\u003eM. cordata\u003c/em\u003e were constructed by the ChemBioDraw software (version 13.02, PerkinElmer Informatics, Waltham). Then, minimizing energy and optimizing structures of \u003cem\u003eM. cordata\u003c/em\u003e by the ChemBioDraw software. The structure of protein was obtained from the Research Collaboratory for Structural Bioinformatics protein data bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiscovery Studio software (version 2016, Dassault Syst\u0026egrave;mes, V\u0026eacute;lizy-Villacoublay, France) was used to optimize the crystal structures by the remove ligands and water. The docking affinity of docking models was acquired and shown in the heatmap which constructed by GraphPad Prim software. The best model of these ligands interacting with amino acid residues has the lowest binding affinity. PyMOL software (version 2.3.5, Schr\u0026ouml;dinger, Inc., New York, USA) and Discovery Studio software were employed to visualize the model of docking [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.7 Absorption, distribution, metabolism, elimination and toxicity (ADMET)analysis\u003c/h2\u003e \u003cp\u003eThe ADMET module in Discovery Studio software was used to import the established small molecular compounds of compounds into software. Select Calculate Molecular Properties in small Molecules module and click ADMET descriptors for parameter setting, then click run to predict the absorption, distribution, metabolism, excretion and toxicity of compounds. The information of Aqueous solubility, Blood brain barrier penetration (BBB), Cytochrome P2D6 inhibition, Hepatotoxicity, Human intestinal absorption (HIA), and Plasma protein binding (PPB) about compound were obtained.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLei Zhang, and Kai Nan conceived and designed the study. Jing Huang, Yulong Peng, and Yang Cao performed the data analysis. Lei Zhang, Jing Huang, and Su Yin wrote the manuscript. All authors are responsible for reviewing data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData derived from public domain resources. These data were derived from the following resources available in the public domain: Man database (https://omim.org/),\u0026nbsp;MalaCards database (https://www.malacards.org/), SwissTargetPrediction database (http://www.swisstargetprediction.ch/), Uniprot KB database (https://www.uniprot.org/),\u0026nbsp;Research Collaboratory for Structural Bioinformatics protein data bank (https://www.rcsb.org/), STRING (https://string-db.org/cgi/input.pl), and Metascape (https://metascape.org) databases, Gene Expression Profiling Interactive Analysis (GEPIA)\u0026nbsp;database (http://gepia.cancer-pku.cn/),\u0026nbsp;Kaplan Meier plotter database\u0026nbsp;(http://kmplot.com/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest associated with the contents of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida I, Fernandes L, Biazi B, Vicentini V. 2017. Evaluation of the Anticancer Activities of the Plant Alkaloids Sanguinarine and Chelerythrine in Human Breast Adenocarcinoma Cells. Anticancer Agents Med Chem. 17: 1586-1592.doi:10.2174/1871520617666170213115132.\u003c/li\u003e\n\u003cli\u003eAzenshtein E, Meshel T, Shina S, Barak N, Keydar I, Ben-Baruch A. 2005. The angiogenic factors CXCL8 and VEGF in breast cancer: regulation by an array of pro-malignancy factors. Cancer Letters. 217: 73-86.doi:10.1016/j.canlet.2004.05.024.\u003c/li\u003e\n\u003cli\u003eBai XY, Li SJ, Wang M, Li XH, Yang YY, Xu ZW, Li B, Li Y, Xia K, Chen H, et al.2018. Kruppel-like factor 9 down-regulates matrix metalloproteinase 9 transcription and suppresses human breast cancer invasion. Cancer Lett. 412: 224-235.doi:10.1016/j.canlet.2017.10.027.\u003c/li\u003e\n\u003cli\u003eBottino J, Gelaleti GB, Maschio LB, Jardim-Perassi BV, de Campos Zuccari DA. 2014.Immunoexpression of ROCK-1 and MMP-9 as prognostic markers in breast cancer. Acta Histochem. 116: 1367-73.doi:10.1016/j.acthis.2014.08.009.\u003c/li\u003e\n\u003cli\u003eDeng F, Ma Y, Liang L, Zhang P, Feng J. 2018. The pro-apoptosis effect of sinomenine in renal carcinoma via inducing autophagy through inactivating PI3K/AKT/mTOR pathway. Biomed Pharmacother. 97: 1269-1274.doi:10.1016/j.biopha.2017.11.064.\u003c/li\u003e\n\u003cli\u003eDong X, Zhang M, Wang K, Liu P, Guo D, Zheng X, Ge X. 2013. Sanguinarine inhibits vascular endothelial growth factor release by generation of reactive oxygen species in MCF-7 human mammary adenocarcinoma cells. Biomed Res Int. 2013: 517698.doi:10.1155/2013/517698.\u003c/li\u003e\n\u003cli\u003eDossus L, Kaaks R, Canzian F, Albanes D, Berndt SI, Boeing H, Buring J, Chanock SJ, Clavel-Chapelon F, Feigelson HS,et al.2010. PTGS2 and IL6 genetic variation and risk of breast and prostate cancer: results from the Breast and Prostate Cancer Cohort Consortium (BPC3). Carcinogenesis. 31: 455-461.doi:10.1093/carcin/bgp307.\u003c/li\u003e\n\u003cli\u003eDvorak Z, Kuban V, Klejdus B, Hlavac J, Vicar J, Ulrichova J, Simanek V. 2006. Quaternary benzo c phenanthridines sanguinarine and chelerythrine: A review of investigations from chemical and biological studies. Heterocycles. 68: 2403-2422.doi:10.1016/j.fct.2006.04.016\u003c/li\u003e\n\u003cli\u003eEllis M, Perou C.2013. The genomic landscape of breast cancer as a therapeutic roadmap. Cancer Discov. 3: 27-34.doi:10.1158/2159-8290.CD-12-0462.\u003c/li\u003e\n\u003cli\u003eFranz C, Bauer R, Carle R, Tedesco D, Tubaro A. Zitterl-Eglseer K. 2005. ASSESSMENT OF PLANTS/HERBS, PLANT/HERB EXTRACTS AND THEIR NATURALLY OR SYNTHETICALLY PRODUCED COMPONENTS AS \u0026ldquo;ADDITIVES\u0026rdquo; FOR USE IN ANIMAL PRODUCTION. Parma: EFSA Supporting Publications.p.140-152.\u003c/li\u003e\n\u003cli\u003eHarbeck N, Gnant M. 2017. Breast cancer. The Lancet.; 389: 1134-1150.doi:10.1016/S0140-6736(16)31891-8.\u003c/li\u003e\n\u003cli\u003eHashim D, Boffetta P, La Vecchia C, Rota,M, Bertuccio P, Malvezzi M, Negri E. 2016. The global decrease in cancer mortality: trends and disparities. Annals of Oncology. 27: 926-933.doi:10.1093/annonc/mdw027.\u003c/li\u003e\n\u003cli\u003eHerbert J, Augereau J, Gleye J, Maffrand J. 1990. Chelerythrine is a potent and specific inhibitor of protein kinase C. Biochem Biophys Res Commun. 172: 993-999.doi:10.1016/0006-291x(90)91544-3.\u003c/li\u003e\n\u003cli\u003eHopkins AL. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology. 2008; 4: 682-690.doi:10.1038/nchembio.118.\u003c/li\u003e\n\u003cli\u003eHurvitz S A, Hu Y, O\u0026apos;Brien N, Finn RS. 2013.Current approaches and future directions in the treatment of HER2-positive breast cancer. Cancer Treat Rev. 39: 219-29.doi：10.1016/j.ctrv.2012.04.008.\u003c/li\u003e\n\u003cli\u003eJemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D 2011.Global Cancer Statistics. CA:A Cancer Journal for Clinicians. 61(2):134.doi:10.3322/caac.20107.\u003c/li\u003e\n\u003cli\u003eJia L, Lin H, Oppenheim J, Howard O, Fan H, Zhao Z, Farrar W, Zhang Y, Colburn N, Young MR, et al. 2017. US National Cancer Institute-China Collaborative Studies on Chinese Medicine and Cancer. Journal of the National Cancer Institute Monographs. 52: 58-61.doi:10.1093/jncimonographs/lgx007.\u003c/li\u003e\n\u003cli\u003eJin L, Yuan RQ, Fuchs A, Yao Y, Joseph A, Schwall R, Schnitt SJ, Guida A, Hastings HM, Andres J, et al. 1997.Expression of interleukin-1 beta in human breast carcinoma. Cancer. 80: 421-434.doi: 10.1002/(sici)1097-0142(19970801)80:3\u0026lt;421::aid-cncr10\u0026gt;3.0.co;2-z.\u003c/li\u003e\n\u003cli\u003eJin Y, Chen W, Yang H, Yan Z, Lai Z, Feng J, Peng J, Lin J. 2017.Scutellaria barbata D. Don inhibits migration and invasion of colorectal cancer cells via suppression of PI3K/AKT and TGF-\u0026beta;/Smad signaling pathways. Exp Ther Med. 14: 5527-5534.doi:10.3892/etm.2017.5242.\u003c/li\u003e\n\u003cli\u003eJohnson K, Ceglowski J, Roweth H, Forward J, Tippy M, El-Husayni S, Kulenthirarajan R, Malloy MW, Machlus KR, Chen WY,et al. 2019.Aspirin inhibits platelets from reprogramming breast tumor cells and promoting metastasis. Blood Adv. 3: 198-211.doi:10.1182/bloodadvances.2018026161.\u003c/li\u003e\n\u003cli\u003eKhin M, Jones A, Cech N, Caesar L. 2018. Macleaya cordata Phytochemical Analysis and Antimicrobial Efficacy of against Extensively Drug-Resistant. Nat Prod Commun. 13: 1479-1483.doi:10.1177/1934578X1801301117.\u003c/li\u003e\n\u003cli\u003eKim S, Kwon Y, Kim J, Muller M, Chung I. 1998.Induction of topoisomerase II-mediated DNA cleavage by a protoberberine alkaloid, berberrubine. Biochemistry. 37: 16316-16324.doi:10.1021/bi9810961.\u003c/li\u003e\n\u003cli\u003eKim S, Lee J, You D, Jeong Y, Jeon M, Yu J, Kim SW, Nam SJ, Lee JE. 2018. Berberine Suppresses Cell Motility Through Downregulation of TGF-\u0026beta;1 in Triple Negative Breast Cancer Cells. Cell Physiol Biochem. 45: 795-807.doi:10.1159/000487171.\u003c/li\u003e\n\u003cli\u003eKim S, Lee T, Leem J, Choi K, Park J, Kwon T. 2008.Sanguinarine-induced apoptosis: generation of ROS, down-regulation of Bcl-2, c-FLIP, and synergy with TRAIL. J Cell Biochem. 104: 895-907.doi:10.1002/jcb.21672.\u003c/li\u003e\n\u003cli\u003eKurtzman SH, Anderson KH, Wang YP, Miller LJ, Renna M, Stankus M, Lindquist RR, Barrows G, Kreutzer DL.1999.Cytokines in human breast cancer: IL-1 alpha and IL-1 beta expression. Oncology Reports. 6: 65-70.doi:10.3892/or.6.1.65.\u003c/li\u003e\n\u003cli\u003eLanczky A, Gyorffy B. 2021.Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res. 23:e27633. doi:10.2196/27633.\u003c/li\u003e\n\u003cli\u003eLangsenlehner U, Yazdani-Biuki B, Eder T, Renner W, Wascher TC, Paulweber B, Weitzer W, Samonigg H, Krippl P. 2006.The cyclooxygenase-2 (PTGS2) 8473T \u0026gt; C polymorphism is associated with breast cancer risk. Clinical Cancer Research. 12: 1392-1394.doi:10.1158/1078-0432.CCR-05-2055.\u003c/li\u003e\n\u003cli\u003eLi CM, Yang XY, Zhong YR, Yu JP. 2016.Chemical composition, antioxidant and antimicrobial activity of the essential oil from the leaves of Macleaya cordata (Willd) R. Br. Nat Prod Res. 30: 438-442.doi:10.1080/14786419.2015.1017490.\u003c/li\u003e\n\u003cli\u003eLi PC, Butcher NJ, Minchin RF. 2019. Arylamine N - Acetyltransferase 1 Regulates Expression of Matrix Metalloproteinase 9 in Breast Cancer Cells: Role of Hypoxia-Inducible Factor 1-alpha. Molecular Pharmacology. 96: 573-579.doi:10.1124/mol.119.117432.\u003c/li\u003e\n\u003cli\u003eLin W, Huang J, Yuan Z, Feng S, Xie Y, Ma W. 2017.Protein kinase C inhibitor chelerythrine selectively inhibits proliferation of triple-negative breast cancer cells. Sci Rep. 7: 2022.doi:10.1038/s41598-017-02222-0.\u003c/li\u003e\n\u003cli\u003eLou JS, Yao P, Tsim KWK. 2018. Cancer Treatment by Using Traditional Chinese Medicine Probing Active Compounds in Anti-multidrug Resistance During Drug Therapy. Current Medicinal Chemistry. 25: 5128-5141.doi: 10.2174/0929867324666170920161922.\u003c/li\u003e\n\u003cli\u003eMa Y, Ren Y, Dai Z, Wu C, Ji Y, Xu J. 2017.IL-6, IL-8 and TNF-\u0026alpha; levels correlate with disease stage in breast cancer patients. Adv Clin Exp Med. 26: 421-426.doi:10.17219/acem/62120.\u003c/li\u003e\n\u003cli\u003eMeng H, Peng N, Yu M, Sun X, Ma Y, Yang G, Wang X. 2017. Treatment of triple-negative breast cancer with Chinese herbal medicine: A prospective cohort study protocol. Medicine. 96:1-5.doi: 10.1097/MD.0000000000008408.\u003c/li\u003e\n\u003cli\u003eMeng Y, Liu Y, Hu Z, Zhang Y, Ni J, Ma Z, Liao H, Wu Q, Tang Q.2018. Sanguinarine Attenuates Lipopolysaccharide-induced Inflammation and Apoptosis by Inhibiting the TLR4/NF-\u0026kappa;B Pathway in H9c2 Cardiomyocytes. Curr Med Sci. 38: 204-211.doi: 10.1007/s11596-018-1867-4.\u003c/li\u003e\n\u003cli\u003eOuyang L, Su X, He D, Chen Y, Ma,M, Xie Q, Yao S. 2010.A study on separation and extraction of four main alkaloids in Macleaya cordata (Willd) R. Br. with strip dispersion hybrid liquid membrane. J Sep Sci. 33: 2026-2034.doi:10.1002/jssc.201000103.\u003c/li\u003e\n\u003cli\u003ePuzovic V, Brcic I, Ranogajec I, Jakic-Razumovic J. 2014.Prognostic values of ETS-1, MMP-2 and MMP-9 expression and co-expression in breast cancer patients. Neoplasma. 61: 439-446.doi:10.4149/neo_2014_054.\u003c/li\u003e\n\u003cli\u003eSomiari SB, Shriver CD, Heckman C, Olsen C, Hu H, Jordan R,Arciero C, Russell S,Garguilo G,Hooke J,et al. 2006.Plasma concentration and activity of matrix metalloproteinase 2 and 9 in patients with breast disease, breast cancer and at risk of developing breast cancer. Cancer Lett. 233: 98-107.doi:10.1016/j.canlet.2005.03.003.\u003c/li\u003e\n\u003cli\u003eTang Z, Tang ZF, Li CW, Kang BX, Gao G, Li C, Zhang ZM. 2017. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res.45: W98-W102.doi: 10.1093/nar/gkx247.\u003c/li\u003e\n\u003cli\u003eWang X, Decker C, Zechner L, Krstin S, Wink M. 2019.In vitro wound healing of tumor cells: inhibition of cell migration by selected cytotoxic alkaloids. BMC Pharmacol Toxicol. 20: 1-12.doi:10.1186/s40360-018-0284-4.\u003c/li\u003e\n\u003cli\u003eWang X, Fang G, Pang Y. 2018.Chinese Medicines in the Treatment of Prostate Cancer: From Formulas to Extracts and Compounds. Nutrients. 10: 283.doi:10.3390/nu10030283.\u003c/li\u003e\n\u003cli\u003eWyatt GL, Crump LS, Young CM, Wessells VM, McQueen CM, Wall SW, Gustafson TL, Fan YY, Chapkin RS,Porter WW,et al. 2019.Cross-talk between SIM2s and NF\u0026kappa;B regulates cyclooxygenase 2 expression in breast cancer. Breast Cancer Res.21: 131.doi: 10.1186/s13058-019-1224-y.\u003c/li\u003e\n\u003cli\u003eYu HY, Kim PM, Sprecher E, Trifonov V, Gerstein M. 2007.The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 3: 713-720.doi:10.1371/journal.pcbi.0030059.\u003c/li\u003e\n\u003cli\u003eZeng L, Yang K.2017. Exploring the pharmacological mechanism of Yanghe Decoction on HER2-positive breast cancer by a network pharmacology approach. Journal of Ethnopharmacology. 199: 68-85.doi:10.1016/j.jep.2017.01.045.\u003c/li\u003e\n\u003cli\u003eZhang L, Yang K, Wang M, Zeng LZ, Sun EZ, Zhang FX, Cao Z, Zhang XX, Zhang H, Guo ZJ. 2020.Exploring the Mechanism of Cremastra Appendiculata (SUANPANQI) against Breast Cancer by Network Pharmacology and Molecular Docking. Computational Biology and Chemistry. 94: 107396.doi:10.1016/j.compbiolchem.2020.107396\u003c/li\u003e\n\u003cli\u003eZhu Y, Pan Y, Zhang G, Wu Y, Zhong W, Chu C, Qian Y, Zhu G. 2018.Chelerythrine Inhibits Human Hepatocellular Carcinoma Metastasis in Vitro. Biol Pharm Bull. 41: 36-46.doi:10.1248/bpb.b17-00451.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"M. cordata, breast cancer, network pharmacology, molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-4945731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4945731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eMacleaya cordata\u003c/em\u003e (Willd) R. Br. (\u003cem\u003eM. cordata\u003c/em\u003e) has widely reported antitumor activity, while the underlying mechanism of \u003cem\u003eM. cordata\u003c/em\u003e anti-breast cancer (BC) still remains unclear. The compounds of M. cordata were collected from previous researches and screened by drug-likeness rules to identify bioactive compounds. The targets were obtained from MalaCards, Online Mendelian Inheritance in Man, and SwissTargetPrediction database, then overlapped to get intersections as potential anti-BC targets of \u003cem\u003eM. cordata\u003c/em\u003e. After topological analysis of the protein-protein interaction network, the correlation analysis of gene expression and patient pathological stage and survival, respectively, was performed, and 4 pivotal targets were obtained. Four bioactive compounds of M. cordata (6-cyanodihydrogensanguinarine, Corysamine, Oxychelirubine, and Berberrubine) had strong binding efficiency with the 4 pivotal genes after molecular docking analysis. The current study demonstrated that \u003cem\u003eM. cordata\u003c/em\u003e acts against BC through multiple targets and pathways that may guide further studies on \u003cem\u003eM. cordata\u003c/em\u003e anti-BC effects.\u003c/p\u003e","manuscriptTitle":"Exploring the Mechanism of Macleaya cordata (Willd) R. Br. Against Breast Cancer by Network Pharmacology and Molecular Docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 10:01:00","doi":"10.21203/rs.3.rs-4945731/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9718a572-ae6b-4832-ba5c-3780826c31b7","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-17T10:01:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 10:01:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4945731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4945731","identity":"rs-4945731","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}