Elucidating the Multitarget Pharmacological Mechanisms of Berberine in Ovarian Cancer: An Integrated Network Pharmacology and Molecular Docking Study

Elucidating the Multitarget Pharmacological Mechanisms of Berberine in Ovarian Cancer: An Integrated Network Pharmacology and Molecular Docking Study | 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 Research Article Elucidating the Multitarget Pharmacological Mechanisms of Berberine in Ovarian Cancer: An Integrated Network Pharmacology and Molecular Docking Study Brunda Moganamurthy, Sajana Mouvanal, Mutthuraj Dasegowda, Kanthesh M Basalingappa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8373472/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 Ovarian cancer (OC) is one of the most common causes of gynecological cancer mortality worldwide and is caused by late and advanced diagnosis, high metastatic potential, and resistance to most drugs. Multi-target therapeutic agents are urgently required, considering that OC is polygenic and heterogeneous. Berberine (BBR) is an isoquinoline alkaloid with anticancer, antiproliferative, and pro-apoptotic properties, making it a promising agent for OC treatment. In this study, an integrative network pharmacology and molecular docking method was used to identify the possible molecular targets and mechanistic pathways through which BBR exerts its therapeutic effects on OC. Four hundred and eighty-two putative BBR-associated genes were identified, of which 213 were associated with OC, and 35 were known to overlap with the targets. PPI network analysis showed that the 10 hub genes included EGFR, BCL2, AKT1, ESR1, CASP3, ERBB2, PTGS2, TNF, PIK3CA, and MDM2, with EGFR, AKT1, and ESR1 shortlisted as major regulatory nodes. GO and KEGG enrichment tests revealed a high level of participation of the BBR-target genes in cell survival, apoptosis, and cancer-associated signaling pathways, especially PI3K-Akt, MAPK, and hormone-regulated cascades. Molecular docking also revealed the high binding affinities of BBR with EGFR (-9.2 kcal/mol), AKT1 (-11.1 kcal/mol), and ESR1 (-7.6 kcal/mol), indicating stable and good interactions with the standard reference inhibitors. Overall, the results indicate that BBR has the potential to be a powerful multi-target agent that can regulate important oncogenic pathways in OC. This study offers a mechanistic model to be followed by additional in vitro, in vivo, and molecular dynamics validation and emphasizes the translational opportunities of BBR as an adjunct or as a sole therapeutic agent in the management of ovarian cancer. Ovarian Cancer (OC) Berberine (BBR) In silico Network pharmacology Molecular Docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Ovarian cancer (OC) is one of the leading gynecological cancers that cause death globally because most cases are diagnosed at advanced stages, and therapeutic interventions are not effective due to intrinsic and acquired resistance [ 1 ]. Global estimates indicate that hundreds of thousands of women are diagnosed with OC each year, and the disease causes a disproportionate number of deaths among women. Recent GLOBOCAN estimates (2022) [ 2 ] and global burden studies reveal that there are approximately 32,94,316 new cases annually and more than 12,20,712 deaths, with the incidence and burden set to increase in most areas unless more vigorous control efforts are implemented [ 3 ]. In population-based registries of high-income environments, the diagnosis stage is the key factor of survival: five-year relative survival in localized disease is typically above 90%, whereas that in metastatic disease is significantly lower, and the clinical imperative is to detect and treat the disease earlier and with more effective systemic agents [ 4 , 5 ]. Single-target drugs do not often provide durable responses due to the heterogeneity of OC [ 6 ] and the presence of multiple pathways of metastasis, angiogenesis, and chemoresistance that are dependent on various genes [ 7 ]. This means that a systems-level approach that can be used to discover novel multitarget agents with the ability to simultaneously tune a number of oncogenic nodes at the same time is appealing for drug discovery and repurposing [ 8 ]. Network pharmacology is an integrative, computational method that integrates target prediction, interactome/PPI mapping, and pathway enrichment, and has become a viable and reproducible method for prioritizing candidate bioactive compounds and the molecular processes they are most likely to modulate [ 9 ]. This technique is particularly suitable for natural products, which often interact with many protein targets and pathways [ 10 ]. Berberine (BBR), an isoquinoline alkaloid extracted from plants [ 11 ], is a prototypical multi-target natural product with a large amount of preclinical data on anti-proliferative, pro-apoptotic, anti-inflammatory, anti-metastatic, and metabolic modulatory effects in various types of cancer [ 12 ]. Mechanistic research has implicated the regulation of signaling axes such as PI3K/AKT/mTOR, MAPK/ERK, Wnt/β-catenin, and apoptosis regulators [ 13 ]. Recent targeted research has shown that BBR can work in OC ( in vitro and in vivo ) [ 14 ] and can suppress proliferation, migration/invasion, influence autophagy and metabolic pathways, and, to some extent, reverse chemoresistance in experimental studies [ 15 ]. These findings encourage the systematic in silico investigation of putative OC targets of BBR and enriched pathways, such that experimental validation can be focused at the point where it is most likely to be productive. The main goal of this study is to create an evidence-based, prioritized list of BBR –OC candidate targets by utilizing complementary in silico target prediction and disease-gene mining strategies. This study will use several bioinformatics databases to determine important hub proteins in the molecular network and explain the enriched biological pathways that are plausibly responsible for the anticancer action of BBR in OC. Moreover, this study aims to confirm the structural feasibility of interactions between BBR and targets using molecular docking analysis of the chosen high-priority hub proteins to support the justification of the next biochemical and cellular research. Because of the high incidence of OC worldwide and the limited existing treatment approaches, this integrative computational approach has great translational potential [ 16 ]. This method offers a logical progression of promising phytochemicals, either alone or in combination with current therapeutic approaches, into preclinical studies and, ultimately, clinical trials by providing a mechanistic framework for understanding the multitarget actions of BBR [ 17 ]. This will help achieve the larger objective of developing novel, secure, and efficient treatment measures for one of the most challenging malignancies in women globally. Materials and Methods Mining target genes for the treatment of ovarian cancer : The SMILES representation of BBR was obtained from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) [ 18 , 19 ]. Target proteins associated with the compounds were identified using TargetNet ( http://targetnet.scbdd.com/home/index/ ) [ 20 ], SeaSearch ( https://sea.bkslab.org/ ) [ 21 ], and SwissTargetPrediction ( http://www.swisstargetprediction.ch/ ) [ 22 ]. Official gene symbols corresponding to these targets were retrieved from UniProt ( https://www.uniprot.org/id- mapping) [ 23 ]. Ovarian Cancer (OC) associated common genes were retrieved from GeneCards ( https://www.genecards.org/ ) [ 24 ], DisGeNET ( https://www.disgenet.org/search ) [ 25 ], and OMIM ( https://www.omim.org/ ) [ 26 ] using "Ovarian Cancer" as the primary search term. After aggregating the data, duplicates were removed systematically. The intersection of these datasets was determined by creating a Venn diagram using the Venny 2 tool ( https://csbg.cnb.csic.es/BioinfoGP/venny.html ) [ 27 ]. Protein-protein interaction (PPI) analysis and hub target screening : The common genes identified through the previously described method were used as input for the STRING database (V_12.0) ( https://string-db.org/ ) [ 28 ] to obtain PPI data. The resulting PPI network was visualized using Cytoscape v3.10.0 [ 29 ]. To identify the top ten hub genes, the CytoHubba plug-in was employed based on their degree centrality [ 30 ]. Gene Ontology (GO) and KEGG enrichment analysis : GO and KEGG enrichment analyses have been performed using the DAVID database ( https://david.ncifcrf.gov/home.jsp ) [ 19 , 31 ] to investigate the possible functions of BBR-target genes [ 19 ]. An FDR significance threshold of 0.05 was set to identify relevant KEGG pathways and GO keywords. To visualize the top 10 phrases based on the number of enriched genes, a free online data analysis and visualization tool called SRplot was used ( https://www.bioinformatics.com.cn/en ). Molecular docking validation : Epidermal Growth Factor Receptor (EGFR), AKT Serine/Threonine Kinase 1 (AKT1), and Estrogen Receptor 1 (ESR1) were identified as key molecular targets for analyzing the binding activity of BBR based on the hub gene network (Fig. 5 ). The SDF structure of BBR was obtained from PubChem and converted to PDBQT format using PyRx software [ 19 , 32 ]. The RCSB Protein Data Bank ( http://www.rcsb.org/ ). provided the crystal structures of EGFR (PDB ID: 1M17), AKT1 (PDB ID: 3O96), and ESR1 (PDB ID: 3ERT). For comparative analysis, inhibitor structures unique to EGFR (erlotinib), AKT1 (Akt Inhibitor-VIII), and ESR1 (tamoxifen) were obtained from PubChem. To ensure a clean molecular docking environment, Discovery Studio 2025 was used to preprocess the protein structures, which included removing solvent molecules and small ligands. The processed protein and ligand structures were prepared using AutoDock 1.5.7 and saved in the PDBQT format for docking simulations. Molecular docking studies were carried out using AutoDock Vina v1.1.2 [ 19 , 33 ]. Results Target genes of OC A total of 482 genes were initially identified using target prediction tools that showed an affinity for BBR. After integrating genes from the DisGeNET, GeneCards, and OMIM databases and removing duplicates, 213 OC-related genes were identified. 35 common genes were obtained for doing a Venn diagram (Fig. 1). PPI analysis and hub target screening To identify potential targets of OC treatment, we used 35 target genes to analyze the PPI networks using the STRING database. We then visualized a complete PPI network using Cytoscape v3.10.0, which comprised 35 nodes and 308 edges (Fig. 2a). CytoHubba was used to create a core PPI subnetwork that included the 10 genes with the highest degree. The top 10 hubs included EGFR, BCL2, AKT1, ESR1, CASP3, ERBB2, PTGS2, TNF, PIK3CA, and MDM2 were identified and shown in Fig. 2b, and a summary is provided in Table 1 . Among the hub genes, EGFR, AKT1, and ESR1 were selected for further analysis, as they are known to be involved in cancer-related signaling pathways. Table 1 Hub genes and their topological parameters Rank Gene Name Protein Name Score 1 EGFR Epidermal growth factor receptor 32 2 BCL2 B-cell Lymphoma 2 32 3 AKT1 AKT Serine/Threonine Kinase 1 32 4 ESR1 Estrogen Receptor 1 31 5 CASP3 Caspase 3 30 6 ERBB2 Erb-B2 Receptor Tyrosine Kinase 2 30 7 PTGS2 Peroxisome proliferator-activated receptor gamma 28 8 TNF Tumor necrosis factor 28 9 PIK3CA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha 27 10 MDM2 Mouse Double Minute 2 Homolog 27 GO and KEGG enrichment analyses To determine the biological importance and possible mechanisms of BBR-target interaction in OC, we performed detailed Gene Ontology (GO) and KEGG pathway analyses using the DAVID database. In our GO analysis, important functional annotations were identified, including the most important Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) terms, as shown in Fig. 4 a. Furthermore, we identified 135 KEGG pathways, and the top 10 pathways are illustrated in Fig. 4 b. Notably, several of the involved genes were over-represented in cancer-associated pathways, with potential opportunities for further exploration. Molecular docking validation In molecular docking, the binding energy is an important parameter of the affinity of ligands and the strength of the interaction of BBR and target proteins. A decrease in the values of binding energy connotes stronger and more preferred interactions and is consequently deemed to be desirable in the case of potential drug candidates. The binding energy of BBR to EGFR, AKT1, and ESR1 was found to be -9.2 kcal/mol, -11.1 kcal/mol, and − 7.6 kcal/mol, respectively, which indicates that BBR has a strong affinity to these key targets. Table 2 gives a breakdown of the binding scores and the involved residues in these interactions in detail. Figures 4 , 5 , and 6 represent the 3D and the 2D molecular interactions of BBR with the target proteins, which provide a global representation of the binding configurations. Discussion The present study provides valuable insights into the prospective use of BBR as a therapeutic agent for (OC). Using a multi-step procedure that involved identifying target genes, PPI networks, functional enrichment, and molecular docking, we demonstrated the biological relevance of the interactions between BBR and the major OC-related targets. First, we predicted 482 genes displaying affinity towards BBR using target prediction tools. Following the incorporation of data available in the DisGeNET, OMIM databases, GeneCards, and exclusion of redundant data, we narrowed down this list to 213 OC-related genes. Venn diagram analysis identified 35 overlapping genes, which were presumed to be the intersection of the targets of BBR and OC pathology. The subset of genes was analyzed using PPI, which provided better insight into their interactions. The PPI network with 44 nodes and 308 edges allowed us to identify the ten most connected hub genes: EGFR, BCL2, AKT1, ESR1, CASP3, ERBB2, PTGS2, TNF, PIK3CA, and MDM2. It is also known that genes play a vital role in cancer biology, and EGFR, AKT1, and ESR1 are considered the main targets for further investigation since they play an important role in the pathogenesis of OC. This hypothesis was further supported by GO and KEGG enrichment analyses, which demonstrated a significant enrichment of the biological processes of cell cycle regulation, apoptotic signaling, and cellular stress response. KEGG identified 135 enriched pathways, with cancer-related cascades of PI3K-Akt signaling, MAPK signaling, and cancer pathways being predominantly present. These findings highlight the potential of BBR as a modulator of various oncogenic pathways, but not as a single-target inhibitor, in line with its well-established nature as a natural polypharmacological compound. Molecular docking validation showed high binding affinities of BBR with EGFR (-9.2 kcal/mol), AKT1 (-11.1 kcal/mol), and ESR1 (-7.6 kcal/mol). These values are either similar to or superior to those of the respective reference inhibitors (Erlotinib, Akt Inhibitor-VIII, and Tamoxifen, respectively), indicating the possibility of BBR having strong target engagement. The presence of hydrophobic, hydrogen-bonding, and van der Waals interactions between BBR and the active-site residues indicates stable conformational binding, which is favorable for inhibitory activity. Altogether, these results indicate that BBR acts on the principal OC-specific pathways and has a high binding affinity for essential proteins involved in cancer development. The identification of EGFR, AKT1, and ESR1 as major targets provides a strong foundation for the development of future therapeutic strategies. Conclusion This study presents a thorough in silico analysis indicating the therapeutic value of BBR as a multi-target inhibitor in ovarian cancer. Our results are relevant to the accumulating evidence supporting the repurposing of polypharmacological natural compounds in cancer treatment. The next step of research needs to be in vitro and in vivo validation, molecular dynamics research, and structure-activity relationship (SAR) optimization to convert these computational results into clinical therapeutic approaches. Moreover, considering the known pharmacokinetic and bioavailability limitations of BBR, formulation refinement or nano delivery strategies should be pursued to enhance its therapeutic potential and clinical translation. Together, this study demonstrates that BBR can be used as an effective adjunct or first-line agent for targeted OC treatment. Declarations Authorship contribution statement Brunda M- writing-original draft, Analysis and data interpretation, Sajana M- writing- Review and editing, Mutthuraj Dasegowda- writing- Review and editing, Kanthesh M Basalingappa- Funding Support, Conceptualization, Supervision, Research Validation. Financial support The present work was supported by the JSS AHER Research grant Ref: RG230018, JSS Academy of Higher Education and Research, Mysuru, to Dr. Kanthesh M Basalingappa. Declaration of Competing Interest We declared that the manuscript has not been submitted and published elsewhere; it is original, and it has been accepted and approved by all authors, with no conflict of interest. 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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-8373472","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600479083,"identity":"563c6ce2-ad92-4897-91da-de5e8ff66e65","order_by":0,"name":"Brunda Moganamurthy","email":"","orcid":"","institution":"JSS Academy of Higher Education \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Brunda","middleName":"","lastName":"Moganamurthy","suffix":""},{"id":600479084,"identity":"307820cb-22d7-4c41-9e2b-731cd878c810","order_by":1,"name":"Sajana Mouvanal","email":"","orcid":"","institution":"JSS Academy of Higher Education \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Sajana","middleName":"","lastName":"Mouvanal","suffix":""},{"id":600479085,"identity":"fc0f2ad0-7843-4156-a4fc-25ce3285fa55","order_by":2,"name":"Mutthuraj Dasegowda","email":"","orcid":"","institution":"JSS Academy of Higher Education \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Mutthuraj","middleName":"","lastName":"Dasegowda","suffix":""},{"id":600479086,"identity":"5d51ac84-0761-4810-9d69-eeb2a7744336","order_by":3,"name":"Kanthesh M Basalingappa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACCSBmBuIEBgnmAwYgETaIqAQ+LYzNEC1sCSRr4TFAsRsnkGw/e/xxQUVdnu7sng+FP/4czuNjYD54m4fBIg+XFmmevMTmGWcOF5vdObvBmLftcDEbA1uyNQ+DRDEuLXIMOYbNvG0HErfdyN1gzNhwOLGNgcdMGqglsQGXFv43QC3/6oBach4YAh0G1ML/Da8WaQmQLQ3MIC0MBjxsYFvY8GqRnPHGcDbPscOJ2+4cMwD6JT2xjZnN2HKOAW4tEudzDD7z1AAddrv5GdBh1onz25sf3nhTUYdTCzJgg0QMKDEwGOBVCQfMD4hTNwpGwSgYBSMNAACptlWEvzraeQAAAABJRU5ErkJggg==","orcid":"","institution":"JSS Academy of Higher Education \u0026 Research","correspondingAuthor":true,"prefix":"","firstName":"Kanthesh","middleName":"M","lastName":"Basalingappa","suffix":""}],"badges":[],"createdAt":"2025-12-16 08:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8373472/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8373472/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104018568,"identity":"71b2b593-ae20-45cb-94c8-f796bf3db541","added_by":"auto","created_at":"2026-03-05 17:46:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVenn Diagram of the Potential Targets of BBR Against OC\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8373472/v1/47e94ff01f3db5b16757a89f.png"},{"id":104018566,"identity":"97a35f2a-f869-4251-834e-8ddd53a5d1d5","added_by":"auto","created_at":"2026-03-05 17:46:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":552016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ea. Protein–protein interaction network of BBR in OC targets obtained from STRING; 2b. Ten hub targets were identified using CytoHubba.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8373472/v1/24e8610717db10e6ac19ad42.png"},{"id":104018567,"identity":"24d90ab1-f770-43d7-ba04-d5d6cd3315fb","added_by":"auto","created_at":"2026-03-05 17:46:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ea. Gene Ontology of \u003c/em\u003eBiological Process, Cellular Components, and Molecular Function of OC\u003cem\u003e; b. KEGG Pathway Enrichment of OC\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8373472/v1/bc29a7635022b2ac2706b957.png"},{"id":104018570,"identity":"5db831a4-46f9-47bf-8646-2c27de6127ee","added_by":"auto","created_at":"2026-03-05 17:46:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":284945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDocking visualizations of 3d and 2D interactions of BBR with EGFR\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8373472/v1/5df9b8696f33835344fda5a3.png"},{"id":104018569,"identity":"d88119e3-b3f1-42b5-bcfb-5a843db5beee","added_by":"auto","created_at":"2026-03-05 17:46:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":265036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDocking visualizations of 3d and 2D interactions of BBR with AKT1\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8373472/v1/f472b4cbd51cdd3434a8d13d.png"},{"id":104018565,"identity":"7e6dea95-ef55-4d44-8719-d0f0f0025ee6","added_by":"auto","created_at":"2026-03-05 17:46:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":289644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDocking visualizations of 3d and 2D interactions of BBR with ESR1\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8373472/v1/c5f950a6baf576d290138957.png"},{"id":106130762,"identity":"6c48b20c-d9fc-4af7-8cac-0028af1a7b83","added_by":"auto","created_at":"2026-04-04 02:09:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2278529,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8373472/v1/7385aeac-49c5-4407-9e70-4a6d7c4187be.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating the Multitarget Pharmacological Mechanisms of Berberine in Ovarian Cancer: An Integrated Network Pharmacology and Molecular Docking Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) is one of the leading gynecological cancers that cause death globally because most cases are diagnosed at advanced stages, and therapeutic interventions are not effective due to intrinsic and acquired resistance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Global estimates indicate that hundreds of thousands of women are diagnosed with OC each year, and the disease causes a disproportionate number of deaths among women. Recent GLOBOCAN estimates (2022) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and global burden studies reveal that there are approximately 32,94,316 new cases annually and more than 12,20,712 deaths, with the incidence and burden set to increase in most areas unless more vigorous control efforts are implemented [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In population-based registries of high-income environments, the diagnosis stage is the key factor of survival: five-year relative survival in localized disease is typically above 90%, whereas that in metastatic disease is significantly lower, and the clinical imperative is to detect and treat the disease earlier and with more effective systemic agents [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSingle-target drugs do not often provide durable responses due to the heterogeneity of OC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and the presence of multiple pathways of metastasis, angiogenesis, and chemoresistance that are dependent on various genes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This means that a systems-level approach that can be used to discover novel multitarget agents with the ability to simultaneously tune a number of oncogenic nodes at the same time is appealing for drug discovery and repurposing [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Network pharmacology is an integrative, computational method that integrates target prediction, interactome/PPI mapping, and pathway enrichment, and has become a viable and reproducible method for prioritizing candidate bioactive compounds and the molecular processes they are most likely to modulate [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This technique is particularly suitable for natural products, which often interact with many protein targets and pathways [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBerberine (BBR), an isoquinoline alkaloid extracted from plants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], is a prototypical multi-target natural product with a large amount of preclinical data on anti-proliferative, pro-apoptotic, anti-inflammatory, anti-metastatic, and metabolic modulatory effects in various types of cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Mechanistic research has implicated the regulation of signaling axes such as PI3K/AKT/mTOR, MAPK/ERK, Wnt/β-catenin, and apoptosis regulators [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent targeted research has shown that BBR can work in OC (\u003cem\u003ein vitro and in vivo\u003c/em\u003e) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and can suppress proliferation, migration/invasion, influence autophagy and metabolic pathways, and, to some extent, reverse chemoresistance in experimental studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These findings encourage the systematic \u003cem\u003ein silico\u003c/em\u003e investigation of putative OC targets of BBR and enriched pathways, such that experimental validation can be focused at the point where it is most likely to be productive.\u003c/p\u003e \u003cp\u003eThe main goal of this study is to create an evidence-based, prioritized list of BBR \u0026ndash;OC candidate targets by utilizing complementary in silico target prediction and disease-gene mining strategies. This study will use several bioinformatics databases to determine important hub proteins in the molecular network and explain the enriched biological pathways that are plausibly responsible for the anticancer action of BBR in OC. Moreover, this study aims to confirm the structural feasibility of interactions between BBR and targets using molecular docking analysis of the chosen high-priority hub proteins to support the justification of the next biochemical and cellular research. Because of the high incidence of OC worldwide and the limited existing treatment approaches, this integrative computational approach has great translational potential [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This method offers a logical progression of promising phytochemicals, either alone or in combination with current therapeutic approaches, into preclinical studies and, ultimately, clinical trials by providing a mechanistic framework for understanding the multitarget actions of BBR [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This will help achieve the larger objective of developing novel, secure, and efficient treatment measures for one of the most challenging malignancies in women globally.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eMining target genes for the treatment of ovarian cancer\u003c/b\u003e: The SMILES representation of BBR was obtained from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Target proteins associated with the compounds were identified using TargetNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://targetnet.scbdd.com/home/index/\u003c/span\u003e\u003cspan address=\"http://targetnet.scbdd.com/home/index/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], SeaSearch (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sea.bkslab.org/\u003c/span\u003e\u003cspan address=\"https://sea.bkslab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and 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) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Official gene symbols corresponding to these targets were retrieved from UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/id-\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/id-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e mapping) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Ovarian Cancer (OC) associated common genes were retrieved from GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], DisGeNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/search\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and OMIM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omim.org/\u003c/span\u003e\u003cspan address=\"https://www.omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] using \"Ovarian Cancer\" as the primary search term. After aggregating the data, duplicates were removed systematically. The intersection of these datasets was determined by creating a Venn diagram using the Venny 2 tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://csbg.cnb.csic.es/BioinfoGP/venny.html\u003c/span\u003e\u003cspan address=\"https://csbg.cnb.csic.es/BioinfoGP/venny.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eProtein-protein interaction (PPI) analysis and hub target screening\u003c/b\u003e: The common genes identified through the previously described method were used as input for the STRING database (V_12.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] to obtain PPI data. The resulting PPI network was visualized using Cytoscape v3.10.0 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To identify the top ten hub genes, the CytoHubba plug-in was employed based on their degree centrality [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene Ontology (GO) and KEGG enrichment analysis\u003c/b\u003e: GO and KEGG enrichment analyses have been performed using the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/home.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/home.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] to investigate the possible functions of BBR-target genes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. An FDR significance threshold of 0.05 was set to identify relevant KEGG pathways and GO keywords. To visualize the top 10 phrases based on the number of enriched genes, a free online data analysis and visualization tool called SRplot was used (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/en\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular docking validation\u003c/b\u003e: Epidermal Growth Factor Receptor (EGFR), AKT Serine/Threonine Kinase 1 (AKT1), and Estrogen Receptor 1 (ESR1) were identified as key molecular targets for analyzing the binding activity of BBR based on the hub gene network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The SDF structure of BBR was obtained from PubChem and converted to PDBQT format using PyRx software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). provided the crystal structures of EGFR (PDB ID: 1M17), AKT1 (PDB ID: 3O96), and ESR1 (PDB ID: 3ERT). For comparative analysis, inhibitor structures unique to EGFR (erlotinib), AKT1 (Akt Inhibitor-VIII), and ESR1 (tamoxifen) were obtained from PubChem.\u003c/p\u003e \u003cp\u003eTo ensure a clean molecular docking environment, Discovery Studio 2025 was used to preprocess the protein structures, which included removing solvent molecules and small ligands. The processed protein and ligand structures were prepared using AutoDock 1.5.7 and saved in the PDBQT format for docking simulations. Molecular docking studies were carried out using AutoDock Vina v1.1.2 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTarget genes of OC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 482 genes were initially identified using target prediction tools that showed an affinity for BBR. After integrating genes from the DisGeNET, GeneCards, and OMIM databases and removing duplicates, 213 OC-related genes were identified. 35 common genes were obtained for doing a Venn diagram (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI analysis and hub target screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify potential targets of OC treatment, we used 35 target genes to analyze the PPI networks using the STRING database. We then visualized a complete PPI network using Cytoscape v3.10.0, which comprised 35 nodes and 308 edges (Fig. 2a). CytoHubba was used to create a core PPI subnetwork that included the 10 genes with the highest degree. The top 10 hubs included EGFR, BCL2, AKT1, ESR1, CASP3, ERBB2, PTGS2, TNF, PIK3CA, and MDM2 were identified and shown in Fig. 2b, and a summary is provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the hub genes, EGFR, AKT1, and ESR1 were selected for further analysis, as they are known to be involved in cancer-related signaling pathways.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHub genes and their topological parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEpidermal growth factor receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-cell Lymphoma 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT Serine/Threonine Kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstrogen Receptor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCASP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaspase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERBB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErb-B2 Receptor Tyrosine Kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeroxisome proliferator-activated receptor gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor necrosis factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIK3CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMouse Double Minute 2 Homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eGO and KEGG enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the biological importance and possible mechanisms of BBR-target interaction in OC, we performed detailed Gene Ontology (GO) and KEGG pathway analyses using the DAVID database. In our GO analysis, important functional annotations were identified, including the most important Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) terms, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea. Furthermore, we identified 135 KEGG pathways, and the top 10 pathways are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb. Notably, several of the involved genes were over-represented in cancer-associated pathways, with potential opportunities for further exploration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn molecular docking, the binding energy is an important parameter of the affinity of ligands and the strength of the interaction of BBR and target proteins. A decrease in the values of binding energy connotes stronger and more preferred interactions and is consequently deemed to be desirable in the case of potential drug candidates. The binding energy of BBR to EGFR, AKT1, and ESR1 was found to be -9.2 kcal/mol, -11.1 kcal/mol, and \u0026minus;\u0026thinsp;7.6 kcal/mol, respectively, which indicates that BBR has a strong affinity to these key targets.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e gives a breakdown of the binding scores and the involved residues in these interactions in detail. Figures \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e represent the 3D and the 2D molecular interactions of BBR with the target proteins, which provide a global representation of the binding configurations.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"695\" height=\"685\"\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides valuable insights into the prospective use of BBR as a therapeutic agent for (OC). Using a multi-step procedure that involved identifying target genes, PPI networks, functional enrichment, and molecular docking, we demonstrated the biological relevance of the interactions between BBR and the major OC-related targets.\u003c/p\u003e \u003cp\u003eFirst, we predicted 482 genes displaying affinity towards BBR using target prediction tools. Following the incorporation of data available in the DisGeNET, OMIM databases, GeneCards, and exclusion of redundant data, we narrowed down this list to 213 OC-related genes. Venn diagram analysis identified 35 overlapping genes, which were presumed to be the intersection of the targets of BBR and OC pathology. The subset of genes was analyzed using PPI, which provided better insight into their interactions. The PPI network with 44 nodes and 308 edges allowed us to identify the ten most connected hub genes: EGFR, BCL2, AKT1, ESR1, CASP3, ERBB2, PTGS2, TNF, PIK3CA, and MDM2. It is also known that genes play a vital role in cancer biology, and EGFR, AKT1, and ESR1 are considered the main targets for further investigation since they play an important role in the pathogenesis of OC.\u003c/p\u003e \u003cp\u003eThis hypothesis was further supported by GO and KEGG enrichment analyses, which demonstrated a significant enrichment of the biological processes of cell cycle regulation, apoptotic signaling, and cellular stress response. KEGG identified 135 enriched pathways, with cancer-related cascades of PI3K-Akt signaling, MAPK signaling, and cancer pathways being predominantly present. These findings highlight the potential of BBR as a modulator of various oncogenic pathways, but not as a single-target inhibitor, in line with its well-established nature as a natural polypharmacological compound.\u003c/p\u003e \u003cp\u003eMolecular docking validation showed high binding affinities of BBR with EGFR (-9.2 kcal/mol), AKT1 (-11.1 kcal/mol), and ESR1 (-7.6 kcal/mol). These values are either similar to or superior to those of the respective reference inhibitors (Erlotinib, Akt Inhibitor-VIII, and Tamoxifen, respectively), indicating the possibility of BBR having strong target engagement. The presence of hydrophobic, hydrogen-bonding, and van der Waals interactions between BBR and the active-site residues indicates stable conformational binding, which is favorable for inhibitory activity.\u003c/p\u003e \u003cp\u003eAltogether, these results indicate that BBR acts on the principal OC-specific pathways and has a high binding affinity for essential proteins involved in cancer development. The identification of EGFR, AKT1, and ESR1 as major targets provides a strong foundation for the development of future therapeutic strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a thorough \u003cem\u003ein silico\u003c/em\u003e analysis indicating the therapeutic value of BBR as a multi-target inhibitor in ovarian cancer. Our results are relevant to the accumulating evidence supporting the repurposing of polypharmacological natural compounds in cancer treatment. The next step of research needs to be \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e validation, molecular dynamics research, and structure-activity relationship (SAR) optimization to convert these computational results into clinical therapeutic approaches. Moreover, considering the known pharmacokinetic and bioavailability limitations of BBR, formulation refinement or nano delivery strategies should be pursued to enhance its therapeutic potential and clinical translation. Together, this study demonstrates that BBR can be used as an effective adjunct or first-line agent for targeted OC treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrunda M- writing-original draft, Analysis and data interpretation, Sajana M- writing- Review and editing,\u0026nbsp;Mutthuraj Dasegowda-\u0026nbsp;writing- Review and editing, Kanthesh M Basalingappa- Funding Support, Conceptualization, Supervision, Research Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present work was supported by the JSS AHER Research grant Ref: RG230018,\u0026nbsp;JSS Academy of Higher Education and Research, Mysuru, to Dr. Kanthesh M Basalingappa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declared that the manuscript has not been submitted and published elsewhere; it is original, and it has been accepted and approved by all authors, with no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI, Brunda M, acknowledge the Ph.D. fellowship support by JSS Academy of Higher Education and Research, Mysuru, Karnataka, India\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlrosan K, Alrosan AZ, Heilat GB, Alrousan AF, Gammoh OS, Alqudah A, Madae'En S, Alrousan MJ (2025) Treatment of ovarian cancer: From the past to the new era. 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J Chem Inf Model 61(8):3891\u0026ndash;3898\u003c/span\u003e\u003c/li\u003e\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":"Ovarian Cancer (OC), Berberine (BBR), In silico, Network pharmacology, Molecular Docking","lastPublishedDoi":"10.21203/rs.3.rs-8373472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8373472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOvarian cancer (OC) is one of the most common causes of gynecological cancer mortality worldwide and is caused by late and advanced diagnosis, high metastatic potential, and resistance to most drugs. Multi-target therapeutic agents are urgently required, considering that OC is polygenic and heterogeneous. Berberine (BBR) is an isoquinoline alkaloid with anticancer, antiproliferative, and pro-apoptotic properties, making it a promising agent for OC treatment. In this study, an integrative network pharmacology and molecular docking method was used to identify the possible molecular targets and mechanistic pathways through which BBR exerts its therapeutic effects on OC.\u003c/p\u003e \u003cp\u003eFour hundred and eighty-two putative BBR-associated genes were identified, of which 213 were associated with OC, and 35 were known to overlap with the targets. PPI network analysis showed that the 10 hub genes included EGFR, BCL2, AKT1, ESR1, CASP3, ERBB2, PTGS2, TNF, PIK3CA, and MDM2, with EGFR, AKT1, and ESR1 shortlisted as major regulatory nodes. GO and KEGG enrichment tests revealed a high level of participation of the BBR-target genes in cell survival, apoptosis, and cancer-associated signaling pathways, especially PI3K-Akt, MAPK, and hormone-regulated cascades. Molecular docking also revealed the high binding affinities of BBR with EGFR (-9.2 kcal/mol), AKT1 (-11.1 kcal/mol), and ESR1 (-7.6 kcal/mol), indicating stable and good interactions with the standard reference inhibitors.\u003c/p\u003e \u003cp\u003eOverall, the results indicate that BBR has the potential to be a powerful multi-target agent that can regulate important oncogenic pathways in OC. This study offers a mechanistic model to be followed by additional in vitro, in vivo, and molecular dynamics validation and emphasizes the translational opportunities of BBR as an adjunct or as a sole therapeutic agent in the management of ovarian cancer.\u003c/p\u003e","manuscriptTitle":"Elucidating the Multitarget Pharmacological Mechanisms of Berberine in Ovarian Cancer: An Integrated Network Pharmacology and Molecular Docking Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 17:46:50","doi":"10.21203/rs.3.rs-8373472/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":"51acc63a-1482-4f69-8d05-18be6e806a08","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-04T02:09:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 17:46:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8373472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8373472","identity":"rs-8373472","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}