Exploring the mechanism Between Pesticide DDT and Breast Cancer: Based on Network Toxicology, Molecular Docking and Molecular Dynamic Simulation

Exploring the mechanism Between Pesticide DDT and Breast Cancer: Based on Network Toxicology, Molecular Docking and Molecular Dynamic Simulation | 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 Between Pesticide DDT and Breast Cancer: Based on Network Toxicology, Molecular Docking and Molecular Dynamic Simulation Yongcheng Tang, Jingrong Huang, Fengyi Yang, Kaifu Li, Xiaoli Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6287887/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Objective The objective of this study was to elucidate the molecular mechanisms underlying the potential contribution of the pesticide Dichlorodiphenyltrichloroethane (DDT) to the pathogenesis of breast cancer. This study aimed to highlight the complex interactions between DDT and key molecular pathways associated with the development of breast cancer. Methods This study utilized multiple online databases to obtain target genes associated with DDT and breast cancer. Network toxicology and molecular docking techniques were employed to analyze the interactions between DDT and key proteins related to breast cancer. Results Our research successfully identified 12 targets associated with the influence of DDT on the development of breast cancer, with core targets primarily related to hormone or growth factor signaling pathways, such as AR, ESR1, ESR2, and ERBB2. These findings elucidate the molecular mechanisms by which DDT may contribute to breast cancer, providing a foundation for future therapeutic strategies aimed at mitigating the adverse effects of DDT on breast health. Conclusion Multiple studies have demonstrated a strong correlation between DDT exposure and the incidence of breast cancer. This research aims to further elucidate the molecular mechanisms by which DDT contributes to the development of breast cancer through the application of network toxicology, protein-protein interactions, and molecular docking. These findings necessitate further epidemiological and clinical investigations to fully understand the impact of DDT exposure on breast cancer risk, thereby providing valuable insights for future prevention and treatment strategies. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Earth and environmental sciences/Environmental sciences Health sciences/Diseases Health sciences/Oncology Health sciences/Pathogenesis Dichlorodiphenyltrichloroethane DDT Breast Cancer Network Toxicology Molecular Docking Molecular Dynamic Simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Dichlorodiphenyltrichloroethane (DDT) is an organochlorine pesticide that was widely used due to its effective insecticidal properties. However, as research into its environmental impacts has advanced, the persistence and bioaccumulation of DDT have raised significant concerns.( 1 , 2 ) The widespread presence of this compound in soil, water, and the food chain makes human exposure nearly inevitable.( 3 , 4 ) Although many countries have restricted or banned the use of DDT, it continues to be employed in certain nations, such as India and South Africa, as part of public health initiatives to combat diseases like malaria.( 5 , 6 ) The lipophilic nature of DDT is associated with its potential endocrine-disrupting effects, which may lead to various health issues.( 5 , 7 ) The impact of environmental factors on global cancer incidence has increasingly attracted attention.( 8 , 9 ) Various studies have indicated a close association between environmental pollutants and cancer prevalence.( 10 ) Research has shown that exposure to DDT in early life increases the risk of developing breast cancer.( 11 ) Despite multiple studies suggesting that DDT exposure may elevate the risk of breast cancer, the specific mechanisms underlying this association remain unclear.( 12 – 14 ) Recent advancements in bioinformatics technologies have provided new tools for exploring the complex interactions between environmental factors and diseases. By integrating multi-omics data and network analysis, network toxicology has emerged as a key approach for identifying molecular targets and pathways involved in disease etiology. Through the integration of these advanced methodologies, this study aims to elucidate the molecular basis by which exposure to DDT influences the onset and progression of breast cancer. A detailed research workflow is illustrated in the Fig. 1 . The results of this study not only enhance the understanding of DDT's role in breast cancer but also provide guidance for pollution reduction efforts to mitigate risk, which is of significant importance for public health. 2. Methods 2.1 Identification of DDT We retrieved the chemical structure and related molecular information of DDT from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Subsequently, we utilized the ADMETLAB 3.0 platform (https://admetlab3.scbdd.com) and the ProTox3 database (https://tox.charite.de/protox3) to validate the carcinogenicity of these pollutants. 2.2 Collection of DDT target genes Potential human target genes for DDT were retrieved from the ChEMBL database (https://www.ebi.ac.uk/chembl), the SwissTargetPrediction database (http://www.swisstargetprediction.ch), and the STITCH database (http://stitch.embl.de). The gene data from these three databases were subsequently merged, and duplicate entries were eliminated, resulting in the compilation of a final target gene set for DDT. 2.3 Collection of breast cancer-related genes Genes associated with "breast cancer" were collected from the GeneCard database (https://www.genecards.org), the OMIM database (https://OMIM.org), and the TTD database (https://db.idrblab.net/ttd/) using the keyword "breast cancer". Genes with a Score greater than 10 were selected based on their ranking from GeneCard. After merging the gene lists from the three databases and removing duplicates, a final set of genes related to breast cancer was obtained. 2.4 GO/KEGG enrichment analysis We utilized the "ClusterProfiler," "Enrichplot," and "Org.Hs.eg.db" packages in R software to conduct Gene Ontology (GO) functional representation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of intersecting genes between DDT and breast cancer. These analyses uncovered the potential roles of key genes in biological processes, molecular functions, and cellular components, along with their involvement in signaling pathways. 2.5 DDT-breast cancer core target screening and PPI network construction We performed protein-protein interaction (PPI) analysis for the gene segments between DDT and breast cancer using the STRING database (https://cn.string-db.org/), setting a confidence score threshold of d ≥ 0.4 to select significant interactions. The results were then visualized using Cytoscape 3.10.3 software (https://cytoscape.org/), enabling the construction of a protein interaction network. 2.6 Molecular docking We retrieved the PDB format files for key genes from the RCSB database (https://www.rcsb.org/) and downloaded the molecular structure file for DDT from the PubChem database. Molecular docking analysis was performed using the CB-Dock2 website (https://cadd.labshare.cn/cbdock2/php/blinddock.php#job_list_load). The binding energy was utilized to assess the interactions between the ligand and receptor: a binding energy less than 0 kcal/mol indicates spontaneous binding, whereas a binding energy less than -5 kcal/mol suggests the possibility of binding. Finally, we visualized the molecular docking results using R software and generated a heatmap to illustrate the interaction intensity between key genes and DDT. 2.7 Molecular dynamic simulation Molecular dynamics (MD) simulation, a robust computational approach, models the dynamic behavior of molecular systems to predict their physicochemical properties. In this study, MD simulations were employed to investigate the interaction mechanisms between DDT and the breast cancer-related target ESR1. The ligand-protein complex was embedded in a solvation chamber filled with water molecules, and the system's electrical neutrality was maintained by adding Cl − and Na + ions. The simulation was conducted at a temperature of 25°C with a total runtime of 100 nanoseconds (ns). Post-simulation analysis included calculations of root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bond properties derived from trajectory data to elucidate the stability and binding characteristics of the DDT-ESR1 interaction. 3. Result 3.1 Toxicity assessment of DDT We conducted a toxicity assessment of DDT using two toxicity prediction platforms. According to the selection criteria, if carcinogenicity was predicted by either platform, the contaminant was deemed toxic. The results indicated that DDT possesses significant carcinogenicity ( Table 1 ). Table 1：Molecular weight , SMILES structure and carcinogeni city of DDT. Name Molecular weight SMILES structure Carcinogenicity (admetlab3.0 ) Carcinogenicity (ProTox3 ) DDT 354.5 g/mol C1=CC(=CC=C1C(C2=CC=C(C=C2)Cl)C(Cl)(Cl)Cl)Cl 0.64 0.94 The table summarizes the molecular weight, SMILES(Simplified Molecular Input Line Entry System) structure, and carcinogenicity assessment of DDT. Carcinogenicity was evaluated using the ADMETLAB 3.0 and ProTox-3 databases. 3.2 Collection of DDT target genes After integrating target prediction data from the ChEMBL, SwissTargetPrediction, and STITCH databases, we identified 26 DDT-related target genes ( Supplementary Table 1 ). 3.3 Collection of breast cancer target genes We retrieved 18,389 relevant genes associated with "breast cancer" from the GeneCard database, 106 related genes from the OMIM database, and 100 relevant genes from the TTD database. To optimize the dataset, we selected genes from GeneCard with a score greater than 10. After removing duplicate sequences, we obtained a total of 3,409 breast cancer-related genes ( Supplementary Table 2 ). 3.4 DDT-breast cancer core target screening and PPI network construction We imported the 12 cross-target genes between DDT and breast cancer into the STRING database for protein-protein interaction (PPI) analysis, setting the confidence threshold to ≥0.4 ( Figure 2A ). Subsequently, we visualized the PPI network using Cytoscape 3.10.3. In the network, targets were ranked according to their Maximum Clique Centrality (MCC); deeper colors and larger circles indicated stronger interactions with other proteins ( Figure 2B ). This visualization provided a clear overview of the interaction relationships among the targets, offering valuable insights for further investigation into the mechanistic links between DDT and breast cancer. 3.5 GO/KEGG enrichment analysis Through cross-analysis of DDT-related genes and breast cancer-related genes, we identified 12 overlapping genes. GO functional enrichment analysis indicated that these genes were primarily involved in processes such as cellular estrogen expression, cell growth, and biosynthesis ( Figure 2C ). Furthermore, KEGG pathway analysis revealed significant enrichment in critical pathways, including Chemical carcinogenesis-receptor activation, Breast cancer, Endocrine resistance, Prolactin signaling pathway, and PI3K-Akt signaling pathway ( Figure 2D ). These findings suggest that DDT may influence the occurrence and progression of breast cancer by regulating intracellular hormone levels, disrupting endocrine functions, and affecting carcinogenesis-related pathways. 3.6 Molecular docking Molecular docking analysis indicated that DDT spontaneously bound to all four key genes (binding energy < 0 kcal/mol) ( Figure 3A ). The binding energies between DDT and the four genes were all below -5 kcal/mol, suggesting a stable interaction between these key genes and DDT ( Figures 3B-E ). These findings suggest that DDT may directly interact with key genes, potentially influencing biological processes associated with breast cancer. 3.7 Molecular dynamic simulation Molecular dynamics simulations revealed that DDT can bind to ESR1 through various intermolecular forces ( Figure 4A ). After 20 nanoseconds, the DDT-ESR1 complex exhibited a tendency towards stability within the entire system ( Figures 4B-D ). The findings from the molecular dynamics simulations further support the stable association between DDT and ESR1, suggesting that DDT may promote the occurrence and development of breast cancer by directly interacting with breast cancer-related proteins. 4. Discussion Although the contribution of DDT to global disease control and agricultural productivity cannot be ignored, its use has been banned in multiple countries due to its ecological toxicity and potential risks to human health. Currently, some countries and regions continue to use DDT. Given its long half-life, DDT persists in the environment, leading to lasting pollution and possessing bioaccumulation characteristics.(15) Therefore, even after the prohibition of DDT, a region may still be affected for an extended period.(16, 17) Breast cancer is one of the most common malignant tumors worldwide and poses a significant threat to women's health.(18) Studies have indicated that genetic factors, lifestyle behaviors, and endocrine factors are recognized as primary high-risk factors for breast cancer.(19, 20) However, recent advancements in understanding environmental factors have revealed that various pollutants present in the environment, such as air pollution, pesticide residues, and harmful substances in building materials, may significantly impact the occurrence and development of breast cancer.(21-23) These findings underscore the importance of considering a multitude of factors in the prevention and intervention of breast cancer. However, direct research on the relationship between environmental pollutants and the incidence of breast cancer presents challenges. In this study, we adopted a multidisciplinary approach employing network toxicology and molecular docking techniques to elucidate the potential connection between DDT and breast cancer. This research also identified key genes and their interaction networks, providing new insights into the role of DDT in breast cancer. Our study revealed that 12 genes play a critical bridging role between DDT and breast cancer. Through KEGG and GO functional enrichment analyses, we found that DDT may promote the development of breast cancer via multiple mechanisms, including interference with endocrine levels, biosynthesis, and chemical carcinogenesis. Notably, AR, ERBB2, ESR1, and ESR2 were identified as key genes within the DDT-breast cancer interaction network. These findings provide new theoretical insights into the molecular mechanisms by which DDT influences breast cancer. Androgen receptor (AR), classified as a type I nuclear receptor, plays a pivotal role in regulating critical biological processes such as differentiation, proliferation, apoptosis, and angiogenesis.(24) Studies have demonstrated that AR is expressed in 70-90% of breast cancer cases and plays an essential role in the pathological characteristics and progression of the disease.(25) Consequently, AR has been proposed as a potential therapeutic target for breast cancer.(26) Currently, AR antagonists are being evaluated in preclinical and clinical studies, with some research showing promising efficacy.(27) ERBB2 (human epidermal growth factor receptor 2, HER2) is a gene that encodes a member of the epidermal growth factor receptor family , playing a crucial role in the regulation of cell proliferation, differentiation, and survival, and is closely associated with the invasiveness of tumor cells.(28) As a significant driver gene and prognostic indicator in breast cancer,(29) ERBB2 not only serves as a key predictive factor for targeted therapies but also has driven the development of related drugs that have substantially altered the diagnostic and therapeutic strategies for breast cancer, thereby improving the prognosis of patients with ERBB2-positive breast cancer.(30) Estrogen receptor 1 (ESR1) and estrogen receptor 2 (ESR2) encode estrogen receptor alpha (ER-α) and estrogen receptor beta (ER-β), respectively. Research has indicated that both ESR1 and ESR2 are associated with the risk of breast cancer development; however, the precise mechanisms by which these genes contribute to breast cancer remain incompletely understood.(31, 32) Furthermore, the role of ESR genes may vary across different populations. A study has reported that ESR1 may pose a risk of breast cancer for women of Asian, European, and African descent.(33) Conversely, ESR2 exhibits contrasting associations in Romanian and Greek populations: in the Romanian cohort, ESR2 is correlated with a higher risk of breast cancer,(34) while it is associated with a lower risk in the Greek population.(35) Molecular docking analyses have confirmed that DDT can specifically bind to four key protein. The study found that the binding energies of DDT with all key protein are less than -5 kcal/mol, indicating that DDT can stably interact with these four critical protein. Molecular dynamics simulations demonstrate that DDT can stably bind to ESR1 through various intermolecular interactions. This finding further supports the hypothesis that DDT may directly interact with these key protein to modulate their biological functions, which could ultimately contribute to the onset of cancer and increase the risk of breast cancer. This study presents several advantages: firstly, we employed a comprehensive approach that integrates network toxicology, molecular docking, and molecular dynamics simulations to investigate the relationship between DDT and breast cancer, thereby enhancing the scientific rigor and relevance of the research. Secondly, by focusing on the molecular mechanisms underlying the relationship between DDT and breast cancer, we addressed the existing gap in the molecular understanding of the association between DDT and breast cancer. This study has several limitations. Firstly, all target information is derived from predictions based on network databases, which may be influenced by algorithmic biases and the quality of the original data. Furthermore, there is currently a lack of direct experimental evidence to validate that DDT mediates its effects on breast cancer through these target genes. 5. Conclusion This study systematically reveals the potential association between DDT and breast cancer by integrating approaches of network toxicology, molecular docking, and molecular dynamics simulations. The results indicate that four key genes, namely AR, ERBB2, ESR1, and ESR2, may constitute significant links between DDT exposure and the development of breast cancer. These findings provide new theoretical insights into the molecular mechanisms by which DDT influences breast cancer progression. Furthermore, these key genes present promising molecular targets for early warning, prognostic assessment, and targeted therapy in breast cancer management. Declarations Funding This work was supported by Luzhou Science and Technology and Talent Work Bureau (Nos.2022-SYF-55). Ethical statement The data utilized in this study were sourced from publicly available databases, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). These databases comprise anonymously accessible data that are freely available to the public, thereby obviating the need for any proof or informed consent for this study. The data were used solely for research purposes and were consistent with the references and guidelines applicable to publicly available data. CRediT authorship contribution statement Jingrong Huang : Methodology, Writing – original draft, Writing – review & editing. Yongcheng Tang : Data curation, Methodology, Software, Writing – original draft, Writing–review & editing. Xiaoli Yang : Conceptualization, Funding acquisition, Methodology, Writing–review & editing. Fengyi Yang : Software, Writing–original draft. Kaifu Li : Formal analysis, Writing-original draft. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We would like to express our sincere gratitude to all the staff members of the databases utilized in this study for their valuable contributions. Data Availability Molecular information of DDT from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Potential human target genes for DDT were retrieved from the ChEMBL database (https://www.ebi.ac.uk/chembl), the SwissTargetPrediction database (http://www.swisstargetprediction.ch), and the STITCH database (http://stitch.embl.de). Genes associated with "breast cancer" were collected from the GeneCard database (https://www.genecards.org), the OMIM database (https://OMIM.org), and the TTD database (https://db.idrblab.net/ttd/) using the keyword "breast cancer". 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Supplementary Files SupplementaryTable1.csv SupplementaryTable2.csv Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers invited by journal 29 Mar, 2025 Editor assigned by journal 29 Mar, 2025 Editor invited by journal 25 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 23 Mar, 2025 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. 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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-6287887","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444598993,"identity":"2c4ee8f0-84ed-40bd-aa26-e1ddf3c445b2","order_by":0,"name":"Yongcheng Tang","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongcheng","middleName":"","lastName":"Tang","suffix":""},{"id":444598994,"identity":"6a4ce910-e79f-46f8-a1a5-79df912a7c81","order_by":1,"name":"Jingrong Huang","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingrong","middleName":"","lastName":"Huang","suffix":""},{"id":444598995,"identity":"188e1723-f61a-449d-a450-5cf1272ab40d","order_by":2,"name":"Fengyi Yang","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fengyi","middleName":"","lastName":"Yang","suffix":""},{"id":444598996,"identity":"46920512-459a-4fb9-9090-8b418037af0b","order_by":3,"name":"Kaifu Li","email":"","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kaifu","middleName":"","lastName":"Li","suffix":""},{"id":444598997,"identity":"fb67e02a-eb82-4e35-9e3c-222c0fb2e31d","order_by":4,"name":"Xiaoli Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACeYYDiQ8+GNgw87M3EKnFsPHAY8MZFWnskj0HiLXm8MFn0jxnDvEbzEggUgdj2+E0Cd62A9IGko833mCosYkmqIWd51iyhWTbHWNz6bRiC4ZjabkNBG2ZcSbxhmHbs2TL2TlmEowNhwlrYbj//oNEYtvh+g03zxCr5cCBJIkDZw4zG9zgIVKLYcOBZMOGijRmyR6gXxKI8QsoKh//AUfl4Y03PtTYEOEwJGAgkUCKcogWUnWMglEwCkbByAAAivJHibkwrKYAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-03-23 11:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6287887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6287887/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-20169-5","type":"published","date":"2026-03-21T15:58:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80889706,"identity":"eb84f7ce-4ef9-4b11-8948-99e320c8f3d7","added_by":"auto","created_at":"2025-04-18 09:49:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4596849,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design overview .\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6287887/v1/0ca758158a99f72b222145b5.png"},{"id":80889598,"identity":"da02d40c-35c4-447f-955e-b24b809d4734","added_by":"auto","created_at":"2025-04-18 09:41:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1579020,"visible":true,"origin":"","legend":"\u003cp\u003eA. The PPI network was constructed using the STRING database with a confidence score threshold of ≥0.4. Nodes represent proteins, while edges indicate the interactions between them. This network highlights the functional associations among DDT-breast cancer-related targets. B. The PPI network was further visualized and analyzed using Cytoscape 3.9.0. Nodes were colored and sized based on their degree values; deeper colors and larger circles denote stronger interference. C. GO Enrichment Analysis of DDT-breast cancer targets. D. KEGG Enrichment Analysis of DDT-breast cancer targets.\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-6287887/v1/651f0ceb8312b856669eca05.png"},{"id":80889601,"identity":"29074316-0cf3-47c4-b20a-8f7e2c4d5aaf","added_by":"auto","created_at":"2025-04-18 09:41:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1907770,"visible":true,"origin":"","legend":"\u003cp\u003eA. The heatmap illustrates the binding energies (kcal/mol) obtained from the molecular docking analysis between DDT and four key molecules (AR, ERBB2, ESR1, and ESR2). B. Molecular docking visualization of DDT with AR. C. Molecular docking visualization of DDT with ERBB2. D. Molecular docking visualization of DDT with ESR2. E. Molecular docking visualization of DDT with ESR1.\u003c/p\u003e","description":"","filename":"FIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-6287887/v1/d86f033e87d435a1a31ccbc1.png"},{"id":80889599,"identity":"b8254ba9-8cbb-4345-a0fe-a67e2cda7d13","added_by":"auto","created_at":"2025-04-18 09:41:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":276992,"visible":true,"origin":"","legend":"\u003cp\u003eA. 2D diagram of the interaction between ESR1-DDT system (blue dashed line represents halogen bond interaction; orange dashed line represents Pi Sulfur interaction; dark pink dashed line represents Pi-Pi Stacked and Pi-Pi T-Shaped interactions; light pink dashed line represents hydrophobic interactions between Alkyl and Pi Alkyl). B. RMSD curve of ESR1-DDT over time. C. Rg curve of ESR1-DDT over time. D. The solvent accessible surface area of ESR1-DDT.\u003c/p\u003e","description":"","filename":"FIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-6287887/v1/2a7d5b0a1a04d7078320ff81.png"},{"id":105224489,"identity":"b1af46d7-a286-4f2c-8b76-3f77a75a740b","added_by":"auto","created_at":"2026-03-23 16:14:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8755197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6287887/v1/21d154f9-9a2e-416d-8701-d41b4d9810f2.pdf"},{"id":80889602,"identity":"cb342ad2-efa7-41ff-bfbb-5b65a7fc5127","added_by":"auto","created_at":"2025-04-18 09:41:02","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":206,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-6287887/v1/49c1eaec2a26ed1bae4cae5c.csv"},{"id":80889610,"identity":"cfdd0e30-6b2e-4b53-9961-68acb61fe20c","added_by":"auto","created_at":"2025-04-18 09:41:02","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25234,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-6287887/v1/1c6b49020d6f2e0e88b34d92.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the mechanism Between Pesticide DDT and Breast Cancer: Based on Network Toxicology, Molecular Docking and Molecular Dynamic Simulation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDichlorodiphenyltrichloroethane (DDT) is an organochlorine pesticide that was widely used due to its effective insecticidal properties. However, as research into its environmental impacts has advanced, the persistence and bioaccumulation of DDT have raised significant concerns.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The widespread presence of this compound in soil, water, and the food chain makes human exposure nearly inevitable.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Although many countries have restricted or banned the use of DDT, it continues to be employed in certain nations, such as India and South Africa, as part of public health initiatives to combat diseases like malaria.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) The lipophilic nature of DDT is associated with its potential endocrine-disrupting effects, which may lead to various health issues.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe impact of environmental factors on global cancer incidence has increasingly attracted attention.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Various studies have indicated a close association between environmental pollutants and cancer prevalence.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Research has shown that exposure to DDT in early life increases the risk of developing breast cancer.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Despite multiple studies suggesting that DDT exposure may elevate the risk of breast cancer, the specific mechanisms underlying this association remain unclear.(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRecent advancements in bioinformatics technologies have provided new tools for exploring the complex interactions between environmental factors and diseases. By integrating multi-omics data and network analysis, network toxicology has emerged as a key approach for identifying molecular targets and pathways involved in disease etiology. Through the integration of these advanced methodologies, this study aims to elucidate the molecular basis by which exposure to DDT influences the onset and progression of breast cancer. A detailed research workflow is illustrated in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results of this study not only enhance the understanding of DDT's role in breast cancer but also provide guidance for pollution reduction efforts to mitigate risk, which is of significant importance for public health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Identification of DDT\u003c/p\u003e\n\u003cp\u003eWe retrieved the chemical structure and related molecular information of DDT from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Subsequently, we utilized the ADMETLAB 3.0 platform (https://admetlab3.scbdd.com) and the ProTox3 database (https://tox.charite.de/protox3) to validate the carcinogenicity of these pollutants.\u003c/p\u003e\n\u003cp\u003e2.2 Collection of DDT target genes\u003c/p\u003e\n\u003cp\u003ePotential human target genes for DDT were retrieved from the ChEMBL database (https://www.ebi.ac.uk/chembl), the SwissTargetPrediction database (http://www.swisstargetprediction.ch), and the STITCH database (http://stitch.embl.de). The gene data from these three databases were subsequently merged, and duplicate entries were eliminated, resulting in the compilation of a final target gene set for DDT.\u003c/p\u003e\n\u003cp\u003e2.3 Collection of breast cancer-related genes\u003c/p\u003e\n\u003cp\u003eGenes associated with \u0026quot;breast cancer\u0026quot; were collected from the GeneCard database (https://www.genecards.org), the OMIM database (https://OMIM.org), and the TTD database (https://db.idrblab.net/ttd/) using the keyword \u0026quot;breast cancer\u0026quot;. Genes with a Score greater than 10 were selected based on their ranking from GeneCard. After merging the gene lists from the three databases and removing duplicates, a final set of genes related to breast cancer was obtained.\u003c/p\u003e\n\u003cp\u003e2.4 GO/KEGG enrichment analysis\u003c/p\u003e\n\u003cp\u003eWe utilized the \u0026quot;ClusterProfiler,\u0026quot; \u0026quot;Enrichplot,\u0026quot; and \u0026quot;Org.Hs.eg.db\u0026quot; packages in R software to conduct Gene Ontology (GO) functional representation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of intersecting genes between DDT and breast cancer. These analyses uncovered the potential roles of key genes in biological processes, molecular functions, and cellular components, along with their involvement in signaling pathways.\u003c/p\u003e\n\u003cp\u003e2.5 DDT-breast cancer core target screening and PPI network construction\u003c/p\u003e\n\u003cp\u003eWe performed protein-protein interaction (PPI) analysis for the gene segments between DDT and breast cancer using the STRING database (https://cn.string-db.org/), setting a confidence score threshold of d \u0026ge; 0.4 to select significant interactions. The results were then visualized using Cytoscape 3.10.3 software (https://cytoscape.org/), enabling the construction of a protein interaction network.\u003c/p\u003e\n\u003cp\u003e2.6 Molecular docking\u003c/p\u003e\n\u003cp\u003eWe retrieved the PDB format files for key genes from the RCSB database (https://www.rcsb.org/) and downloaded the molecular structure file for DDT from the PubChem database. Molecular docking analysis was performed using the CB-Dock2 website (https://cadd.labshare.cn/cbdock2/php/blinddock.php#job_list_load). The binding energy was utilized to assess the interactions between the ligand and receptor: a binding energy less than 0 kcal/mol indicates spontaneous binding, whereas a binding energy less than -5 kcal/mol suggests the possibility of binding. Finally, we visualized the molecular docking results using R software and generated a heatmap to illustrate the interaction intensity between key genes and DDT.\u003c/p\u003e\n\u003cp\u003e2.7 Molecular dynamic simulation\u003c/p\u003e\n\u003cp\u003eMolecular dynamics (MD) simulation, a robust computational approach, models the dynamic behavior of molecular systems to predict their physicochemical properties. In this study, MD simulations were employed to investigate the interaction mechanisms between DDT and the breast cancer-related target ESR1. The ligand-protein complex was embedded in a solvation chamber filled with water molecules, and the system\u0026apos;s electrical neutrality was maintained by adding Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e ions. The simulation was conducted at a temperature of 25\u0026deg;C with a total runtime of 100 nanoseconds (ns). Post-simulation analysis included calculations of root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bond properties derived from trajectory data to elucidate the stability and binding characteristics of the DDT-ESR1 interaction.\u003c/p\u003e"},{"header":"3. Result","content":"\u003cp\u003e3.1 Toxicity assessment of DDT\u003c/p\u003e\n\u003cp\u003eWe conducted a toxicity assessment of DDT using two toxicity prediction platforms. According to the selection criteria, if carcinogenicity was predicted by either platform, the contaminant was deemed toxic. The results indicated that DDT possesses significant carcinogenicity (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTable 1：Molecular weight , SMILES structure and carcinogeni city of DDT.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eMolecular weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSMILES structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eCarcinogenicity (admetlab3.0 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCarcinogenicity (ProTox3 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eDDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e354.5 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eC1=CC(=CC=C1C(C2=CC=C(C=C2)Cl)C(Cl)(Cl)Cl)Cl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe table summarizes the molecular weight, SMILES(Simplified Molecular Input Line Entry System) structure, and carcinogenicity assessment of DDT. Carcinogenicity was evaluated using the ADMETLAB 3.0 and ProTox-3 databases.\u003c/p\u003e\n\u003cp\u003e3.2 Collection of DDT target genes\u003c/p\u003e\n\u003cp\u003eAfter integrating target prediction data from the ChEMBL, SwissTargetPrediction, and STITCH databases, we identified 26 DDT-related target genes (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e3.3 Collection of breast cancer target genes\u003c/p\u003e\n\u003cp\u003eWe retrieved 18,389 relevant genes associated with \u0026quot;breast cancer\u0026quot; from the GeneCard database, 106 related genes from the OMIM database, and 100 relevant genes from the TTD database. To optimize the dataset, we selected genes from GeneCard with a score greater than 10. After removing duplicate sequences, we obtained a total of 3,409 breast cancer-related genes (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e3.4 DDT-breast cancer core target screening and PPI network construction\u003c/p\u003e\n\u003cp\u003eWe imported the 12 cross-target genes between DDT and breast cancer into the STRING database for protein-protein interaction (PPI) analysis, setting the confidence threshold to \u0026ge;0.4 (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Subsequently, we visualized the PPI network using Cytoscape 3.10.3. In the network, targets were ranked according to their Maximum Clique Centrality (MCC); deeper colors and larger circles indicated stronger interactions with other proteins (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). This visualization provided a clear overview of the interaction relationships among the targets, offering valuable insights for further investigation into the mechanistic links between DDT and breast cancer.\u003c/p\u003e\n\u003cp\u003e3.5 GO/KEGG enrichment analysis\u003c/p\u003e\n\u003cp\u003eThrough cross-analysis of DDT-related genes and breast cancer-related genes, we identified 12 overlapping genes. GO functional enrichment analysis indicated that these genes were primarily involved in processes such as cellular estrogen expression, cell growth, and biosynthesis (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). Furthermore, KEGG pathway analysis revealed significant enrichment in critical pathways, including Chemical carcinogenesis-receptor activation, Breast cancer, Endocrine resistance, Prolactin signaling pathway, and PI3K-Akt signaling pathway (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). These findings suggest that DDT may influence the occurrence and progression of breast cancer by regulating intracellular hormone levels, disrupting endocrine functions, and affecting carcinogenesis-related pathways.\u003c/p\u003e\n\u003cp\u003e3.6 Molecular docking\u003c/p\u003e\n\u003cp\u003eMolecular docking analysis indicated that DDT spontaneously bound to all four key genes (binding energy \u0026lt; 0 kcal/mol) (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). The binding energies between DDT and the four genes were all below -5 kcal/mol, suggesting a stable interaction between these key genes and DDT (\u003cstrong\u003eFigures 3B-E\u003c/strong\u003e). These findings suggest that DDT may directly interact with key genes, potentially influencing biological processes associated with breast cancer.\u003c/p\u003e\n\u003cp\u003e3.7 Molecular dynamic simulation\u003c/p\u003e\n\u003cp\u003eMolecular dynamics simulations revealed that DDT can bind to ESR1 through various intermolecular forces (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). After 20 nanoseconds, the DDT-ESR1 complex exhibited a tendency towards stability within the entire system (\u003cstrong\u003eFigures 4B-D\u003c/strong\u003e). The findings from the molecular dynamics simulations further support the stable association between DDT and ESR1, suggesting that DDT may promote the occurrence and development of breast cancer by directly interacting with breast cancer-related proteins.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAlthough the contribution of DDT to global disease control and agricultural productivity cannot be ignored, its use has been banned in multiple countries due to its ecological toxicity and potential risks to human health. Currently, some countries and regions continue to use DDT. Given its long half-life, DDT persists in the environment, leading to lasting pollution and possessing bioaccumulation characteristics.(15) Therefore, even after the prohibition of DDT, a region may still be affected for an extended period.(16, 17)\u003c/p\u003e\n\u003cp\u003eBreast cancer is one of the most common malignant tumors worldwide and poses a significant threat to women\u0026apos;s health.(18) Studies have indicated that genetic factors, lifestyle behaviors, and endocrine factors are recognized as primary high-risk factors for breast cancer.(19, 20) However, recent advancements in understanding environmental factors have revealed that various pollutants present in the environment, such as air pollution, pesticide residues, and harmful substances in building materials, may significantly impact the occurrence and development of breast cancer.(21-23) These findings underscore the importance of considering a multitude of factors in the prevention and intervention of breast cancer. However, direct research on the relationship between environmental pollutants and the incidence of breast cancer presents challenges. In this study, we adopted a multidisciplinary approach employing network toxicology and molecular docking techniques to elucidate the potential connection between DDT and breast cancer. This research also identified key genes and their interaction networks, providing new insights into the role of DDT in breast cancer.\u003c/p\u003e\n\u003cp\u003eOur study revealed that 12 genes play a critical bridging role between DDT and breast cancer. Through KEGG and GO functional enrichment analyses, we found that DDT may promote the development of breast cancer via multiple mechanisms, including interference with endocrine levels, biosynthesis, and chemical carcinogenesis. Notably, AR, ERBB2, ESR1, and ESR2 were identified as key genes within the DDT-breast cancer interaction network. These findings provide new theoretical insights into the molecular mechanisms by which DDT influences breast cancer.\u003c/p\u003e\n\u003cp\u003eAndrogen receptor (AR), classified as a type I nuclear receptor, plays a pivotal role in regulating critical biological processes such as differentiation, proliferation, apoptosis, and angiogenesis.(24) Studies have demonstrated that AR is expressed in 70-90% of breast cancer cases and plays an essential role in the pathological characteristics and progression of the disease.(25) Consequently, AR has been proposed as a potential therapeutic target for breast cancer.(26) Currently, AR antagonists are being evaluated in preclinical and clinical studies, with some research showing promising efficacy.(27)\u003c/p\u003e\n\u003cp\u003eERBB2 (human epidermal growth factor receptor 2, HER2) is a gene that encodes a member of the epidermal growth factor receptor family , playing a crucial role in the regulation of cell proliferation, differentiation, and survival, and is closely associated with the invasiveness of tumor cells.(28) As a significant driver gene and prognostic indicator in breast cancer,(29) ERBB2 not only serves as a key predictive factor for targeted therapies but also has driven the development of related drugs that have substantially altered the diagnostic and therapeutic strategies for breast cancer, thereby improving the prognosis of patients with ERBB2-positive breast cancer.(30)\u003c/p\u003e\n\u003cp\u003eEstrogen receptor 1 (ESR1) and estrogen receptor 2 (ESR2) encode estrogen receptor alpha (ER-\u0026alpha;) and estrogen receptor beta (ER-\u0026beta;), respectively. Research has indicated that both ESR1 and ESR2 are associated with the risk of breast cancer development; however, the precise mechanisms by which these genes contribute to breast cancer remain incompletely understood.(31, 32) Furthermore, the role of ESR genes may vary across different populations. A study has reported that ESR1 may pose a risk of breast cancer for women of Asian, European, and African descent.(33) Conversely, ESR2 exhibits contrasting associations in Romanian and Greek populations: in the Romanian cohort, ESR2 is correlated with a higher risk of breast cancer,(34) while it is associated with a lower risk in the Greek population.(35)\u003c/p\u003e\n\u003cp\u003eMolecular docking analyses have confirmed that DDT can specifically bind to four key protein. The study found that the binding energies of DDT with all key protein are less than -5 kcal/mol, indicating that DDT can stably interact with these four critical protein. Molecular dynamics simulations demonstrate that DDT can stably bind to ESR1 through various intermolecular interactions. This finding further supports the hypothesis that DDT may directly interact with these key protein to modulate their biological functions, which could ultimately contribute to the onset of cancer and increase the risk of breast cancer.\u003c/p\u003e\n\u003cp\u003eThis study presents several advantages: firstly, we employed a comprehensive approach that integrates network toxicology, molecular docking, and molecular dynamics simulations to investigate the relationship between DDT and breast cancer, thereby enhancing the scientific rigor and relevance of the research. Secondly, by focusing on the molecular mechanisms underlying the relationship between DDT and breast cancer, we addressed the existing gap in the molecular understanding of the association between DDT and breast cancer.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Firstly, all target information is derived from predictions based on network databases, which may be influenced by algorithmic biases and the quality of the original data. Furthermore, there is currently a lack of direct experimental evidence to validate that DDT mediates its effects on breast cancer through these target genes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study systematically reveals the potential association between DDT and breast cancer by integrating approaches of network toxicology, molecular docking, and molecular dynamics simulations. The results indicate that four key genes, namely AR, ERBB2, ESR1, and ESR2, may constitute significant links between DDT exposure and the development of breast cancer. These findings provide new theoretical insights into the molecular mechanisms by which DDT influences breast cancer progression. Furthermore, these key genes present promising molecular targets for early warning, prognostic assessment, and targeted therapy in breast cancer management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Luzhou Science and Technology and Talent Work Bureau (Nos.2022-SYF-55).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study were sourced from publicly available databases, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). These databases comprise anonymously accessible data that are freely available to the public, thereby obviating the need for any proof or informed consent for this study. The data were used solely for research purposes and were consistent with the references and guidelines applicable to publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJingrong Huang\u003c/strong\u003e: Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYongcheng Tang\u003c/strong\u003e: Data curation, Methodology, Software, Writing \u0026ndash; original draft, Writing\u0026ndash;review \u0026amp; editing. \u003cstrong\u003eXiaoli Yang\u003c/strong\u003e: Conceptualization, Funding acquisition, Methodology, Writing\u0026ndash;review \u0026amp; editing. \u003cstrong\u003eFengyi Yang\u003c/strong\u003e: Software, Writing\u0026ndash;original draft. \u003cstrong\u003eKaifu Li\u003c/strong\u003e: Formal analysis, Writing-original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all the staff members of the databases utilized in this study for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular information of DDT from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Potential human target genes for DDT were retrieved from the ChEMBL database (https://www.ebi.ac.uk/chembl), the SwissTargetPrediction database (http://www.swisstargetprediction.ch), and the STITCH database (http://stitch.embl.de). Genes associated with \u0026quot;breast cancer\u0026quot; were collected from the GeneCard database (https://www.genecards.org), the OMIM database (https://OMIM.org), and the TTD database (https://db.idrblab.net/ttd/) using the keyword \u0026quot;breast cancer\u0026quot;. The datasets used in this study are all publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKeswani C, Dilnashin H, Birla H, Roy P, Tyagi RK, Singh D, et al.Global footprints of organochlorine pesticides: a pan-global survey. 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Rev Environ Health. 2024.\u003c/li\u003e\n\u003cli\u003eCohn BA, Wolff MS, Cirillo PM, Sholtz RI\u003cstrong\u003e. \u003c/strong\u003eDDT and breast cancer in young women: new data on the significance of age at exposure. Environ Health Perspect. 2007;115(10):1406-14.\u003c/li\u003e\n\u003cli\u003eChang S, El-Zaemey S, Heyworth J, Tang MC\u003cstrong\u003e. \u003c/strong\u003eDDT exposure in early childhood and female breast cancer: Evidence from an ecological study in Taiwan. Environ Int. 2018;121(Pt 2):1106-12.\u003c/li\u003e\n\u003cli\u003eRodgers KM, Udesky JO, Rudel RA, Brody JG\u003cstrong\u003e. \u003c/strong\u003eEnvironmental chemicals and breast cancer: An updated review of epidemiological literature informed by biological mechanisms. Environ Res. 2018;160:152-82.\u003c/li\u003e\n\u003cli\u003eZeinomar N, Oskar S, Kehm RD, Sahebzeda S, Terry MB\u003cstrong\u003e. \u003c/strong\u003eEnvironmental exposures and breast cancer risk in the context of underlying susceptibility: A systematic review of the epidemiological literature. Environ Res. 2020;187:109346.\u003c/li\u003e\n\u003cli\u003eKoureas M, Rousou X, Haftiki H, Mouchtouri VA, Rachiotis G, Rakitski V, et al.Spatial and temporal distribution of p,p\u0026apos;-DDE (1‑dichloro‑2,2‑bis (p‑chlorophenyl) ethylene) blood levels across the globe. A systematic review and meta-analysis. Sci Total Environ. 2019;686:440-51.\u003c/li\u003e\n\u003cli\u003eDonets MM, Yu Tsygankov V, Boyarova MD, Gumovskiy AN, Kulshova VI, Elkhoury JA, et al.Flounders as indicators of environmental contamination by persistent organic pollutants and health risk. Mar Pollut Bull. 2021;164:111977.\u003c/li\u003e\n\u003cli\u003eBatt AL, Wathen JB, Lazorchak JM, Olsen AR, Kincaid TM\u003cstrong\u003e. \u003c/strong\u003eStatistical Survey of Persistent Organic Pollutants: Risk Estimations to Humans and Wildlife through Consumption of Fish from U.S. Rivers. Environ Sci Technol. 2017;51(5):3021-31.\u003c/li\u003e\n\u003cli\u003eTorre LA, Siegel RL, Ward EM, Jemal A\u003cstrong\u003e. \u003c/strong\u003eGlobal Cancer Incidence and Mortality Rates and Trends--An Update. Cancer Epidemiol Biomarkers Prev. 2016;25(1):16-27.\u003c/li\u003e\n\u003cli\u003eSun YS, Zhao Z, Yang ZN, Xu F, Lu HJ, Zhu ZY, et al.Risk Factors and Preventions of Breast Cancer. Int J Biol Sci. 2017;13(11):1387-97.\u003c/li\u003e\n\u003cli\u003eKey TJ, Appleby PN, Reeves GK, Travis RC, Alberg AJ, Barricarte A, et al.Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol. 2013;14(10):1009-19.\u003c/li\u003e\n\u003cli\u003eWu AH, Wu J, Tseng C, Stram DO, Shariff-Marco S, Larson T, et al.Air Pollution and Breast Cancer Incidence in the Multiethnic Cohort Study. J Clin Oncol. 2025;43(3):273-84.\u003c/li\u003e\n\u003cli\u003ePanis C, Candiotto LZP, Gaboardi SC, Teixeira GT, Alves FM, da Silva JC, et al.Exposure to Pesticides and Breast Cancer in an Agricultural Region in Brazil. Environ Sci Technol. 2024;58(24):10470-81.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-P\u0026eacute;rez J, P\u0026eacute;rez-Abad N, Lope V, Castell\u0026oacute; A, Poll\u0026aacute;n M, Gonz\u0026aacute;lez-S\u0026aacute;nchez M, et al.Breast and prostate cancer mortality and industrial pollution. Environ Pollut. 2016;214:394-9.\u003c/li\u003e\n\u003cli\u003eVenema CM, Bense RD, Steenbruggen TG, Nienhuis HH, Qiu SQ, van Kruchten M, et al.Consideration of breast cancer subtype in targeting the androgen receptor. Pharmacol Ther. 2019;200:135-47.\u003c/li\u003e\n\u003cli\u003eCollins LC, Cole KS, Marotti JD, Hu R, Schnitt SJ, Tamimi RM\u003cstrong\u003e. \u003c/strong\u003eAndrogen receptor expression in breast cancer in relation to molecular phenotype: results from the Nurses\u0026apos; Health Study. Mod Pathol. 2011;24(7):924-31.\u003c/li\u003e\n\u003cli\u003eSafarpour D, Pakneshan S, Tavassoli FA\u003cstrong\u003e. \u003c/strong\u003eAndrogen receptor (AR) expression in 400 breast carcinomas: is routine AR assessment justified? Am J Cancer Res. 2014;4(4):353-68.\u003c/li\u003e\n\u003cli\u003eGucalp A, Tolaney S, Isakoff SJ, Ingle JN, Liu MC, Carey LA, et al.Phase II trial of bicalutamide in patients with androgen receptor-positive, estrogen receptor-negative metastatic Breast Cancer. Clin Cancer Res. 2013;19(19):5505-12.\u003c/li\u003e\n\u003cli\u003eHynes NE, MacDonald G\u003cstrong\u003e. \u003c/strong\u003eErbB receptors and signaling pathways in cancer. Curr Opin Cell Biol. 2009;21(2):177-84.\u003c/li\u003e\n\u003cli\u003eCooke T, Reeves J, Lanigan A, Stanton P\u003cstrong\u003e. \u003c/strong\u003eHER2 as a prognostic and predictive marker for breast cancer. Ann Oncol. 2001;12 Suppl 1:S23-8.\u003c/li\u003e\n\u003cli\u003eSwain SM, Shastry M, Hamilton E\u003cstrong\u003e. \u003c/strong\u003eTargeting HER2-positive breast cancer: advances and future directions. Nat Rev Drug Discov. 2023;22(2):101-26.\u003c/li\u003e\n\u003cli\u003eMaguire P, Margolin S, Skoglund J, Sun XF, Gustafsson JA, B\u0026oslash;rresen-Dale AL, et al.Estrogen receptor beta (ESR2) polymorphisms in familial and sporadic breast cancer. Breast Cancer Res Treat. 2005;94(2):145-52.\u003c/li\u003e\n\u003cli\u003ePiperigkou Z, Koutsandreas A, Franchi M, Zolota V, Kletsas D, Passi A, et al.ESR2 Drives Mesenchymal-to-Epithelial Transition in Triple-Negative Breast Cancer and Tumorigenesis In Vivo. Front Oncol. 2022;12:917633.\u003c/li\u003e\n\u003cli\u003eCai Q, Wen W, Qu S, Li G, Egan KM, Chen K, et al.Replication and functional genomic analyses of the breast cancer susceptibility locus at 6q25.1 generalize its importance in women of chinese, Japanese, and European ancestry. Cancer Res. 2011;71(4):1344-55.\u003c/li\u003e\n\u003cli\u003eAnghel A, Raica M, Marian C, Ursoniu S, Mitrasca O\u003cstrong\u003e. \u003c/strong\u003eCombined profile of the tandem repeats CAG, TA and CA of the androgen and estrogen receptor genes in breast cancer. J Cancer Res Clin Oncol. 2006;132(11):727-33.\u003c/li\u003e\n\u003cli\u003eTsezou A, Tzetis M, Gennatas C, Giannatou E, Pampanos A, Malamis G, et al.Association of repeat polymorphisms in the estrogen receptors alpha, beta (ESR1, ESR2) and androgen receptor (AR) genes with the occurrence of breast cancer. Breast. 2008;17(2):159-66.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dichlorodiphenyltrichloroethane, DDT, Breast Cancer, Network Toxicology, Molecular Docking, Molecular Dynamic Simulation","lastPublishedDoi":"10.21203/rs.3.rs-6287887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6287887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe objective of this study was to elucidate the molecular mechanisms underlying the potential contribution of the pesticide Dichlorodiphenyltrichloroethane (DDT) to the pathogenesis of breast cancer. This study aimed to highlight the complex interactions between DDT and key molecular pathways associated with the development of breast cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized multiple online databases to obtain target genes associated with DDT and breast cancer. Network toxicology and molecular docking techniques were employed to analyze the interactions between DDT and key proteins related to breast cancer.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur research successfully identified 12 targets associated with the influence of DDT on the development of breast cancer, with core targets primarily related to hormone or growth factor signaling pathways, such as AR, ESR1, ESR2, and ERBB2. These findings elucidate the molecular mechanisms by which DDT may contribute to breast cancer, providing a foundation for future therapeutic strategies aimed at mitigating the adverse effects of DDT on breast health.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMultiple studies have demonstrated a strong correlation between DDT exposure and the incidence of breast cancer. This research aims to further elucidate the molecular mechanisms by which DDT contributes to the development of breast cancer through the application of network toxicology, protein-protein interactions, and molecular docking. These findings necessitate further epidemiological and clinical investigations to fully understand the impact of DDT exposure on breast cancer risk, thereby providing valuable insights for future prevention and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Exploring the mechanism Between Pesticide DDT and Breast Cancer: Based on Network Toxicology, Molecular Docking and Molecular Dynamic Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 09:40:56","doi":"10.21203/rs.3.rs-6287887/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-18T11:51:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T04:41:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313300391211860486654984225032442200263","date":"2025-04-11T07:36:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134633441302320638909147852249439277925","date":"2025-04-11T07:35:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97526477693478192951361877310432096612","date":"2025-04-10T03:20:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T19:23:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T04:30:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166221373505604359237344493618527461063","date":"2025-03-31T21:20:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318971930327367705750833683913786740102","date":"2025-03-31T07:20:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28085908376552543841596714295087500122","date":"2025-03-30T00:06:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-29T20:59:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-29T06:40:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-26T02:48:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T04:09:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-23T11:04:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9923468-f7a1-4fc2-8e07-cf2453af527f","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47336467,"name":"Biological sciences/Cancer"},{"id":47336468,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":47336469,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47336470,"name":"Health sciences/Diseases"},{"id":47336471,"name":"Health sciences/Oncology"},{"id":47336472,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2026-03-23T16:11:10+00:00","versionOfRecord":{"articleIdentity":"rs-6287887","link":"https://doi.org/10.1038/s41598-025-20169-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-21 15:58:58","publishedOnDateReadable":"March 21st, 2026"},"versionCreatedAt":"2025-04-18 09:40:56","video":"","vorDoi":"10.1038/s41598-025-20169-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-20169-5","workflowStages":[]},"version":"v1","identity":"rs-6287887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6287887","identity":"rs-6287887","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}