Integrating Mendelian randomization and network toxicology to elucidate the causal role and mechanisms of Atrazine in breast cancer

Integrating Mendelian randomization and network toxicology to elucidate the causal role and mechanisms of Atrazine in breast cancer | 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 Integrating Mendelian randomization and network toxicology to elucidate the causal role and mechanisms of Atrazine in breast cancer Zitong Zhao, Yifan Cai, Chaofan Li, Chong Du, Shuqun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6806548/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 Background Breast cancer (BC) is the leading cause of cancer-related mortality in women. Atrazine, a widely used herbicide, is increasingly recognized as an environmental pollutant due to bioaccumulation. In this study, we explored the mechanisms by which Atrazine exposure contributed to the occurrence and development of BC. Methods We integrated common targets of Atrazine in BC through multiple databases (e.g. PubChem, CTD, GeneCards, OMIM). The causal relationship between Atrazine exposure and BC was established via Mendelian randomization analysis. The protein-protein interaction (PPI) network of these targets was constructed using STRING database, with core targets analyzed via Cytoscape. GO and KEGG enrichment analyses were performed using the R package. Molecular docking simulations assessed Atrazine’s binding affinity to core targets. Results We identified 1267 potential targets for Atrazine-induced BC. Following single nucleotide polymorphism (SNP) - based selection criteria, SNPs from 1047 potential targets were utilized as instrumental variables, narrowing to 164 causally associated targets. PPI network analysis refined these to 38 core targets. KEGG enrichment highlighted the top three signaling pathways: cellular senescence pathway, human T-cell leukemia virus 1 infection, and small cell lung cancer. Molecular docking revealed strong binding affinities between Atrazine and these core targets (AKT1, CASP3, HSPA4, CCND1, and MAPK3). Conclusions Atrazine exposure is linked to BC via cellular senescence, HTLV-1 infection, and small cell lung cancer pathways, with AKT1, CASP3, HSPA4, CCND1, and MAPK3 as key targets. This study delineated a molecular framework for Atrazine-induced BC and a method to assess pollutants' toxicological effects. Atrazine breast cancer Mendelian randomization network toxicology molecular docking causal relationship Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Breast cancer (BC) has become a significant global public health concern affecting women worldwide due to its high mortality and incidence rates [ 1 ] . Epidemiological surveys have shown that breast cancer remains the leading cause of cancer burden among women globally, with incidence rates continuing to rise particularly in high-income countries [ 2 , 3 ] . However, the etiology of breast cancer has not yet been fully elucidated. Atrazine is a widely used triazine herbicide. Owing to its high efficacy in weed control and low cost, it is extensively applied in agricultural production [ 4 , 5 ] . However, its highly persistence in the environment and tendency to bioaccumulate have gradually rendered it a globally recognized environmental pollutant. Atrazine can contaminate surface water and groundwater through soil infiltration and dispersion into water bodies, and it may pose threats to ecosystems and human health via bioaccumulation in the food chain [ 6 – 8 ] . In recent years, the endocrine-disrupting effects of Atrazine have attracted considerable attention, particularly regarding its potential impact on the estrogen signaling pathway [ 9 , 10 ] . A few studies have demonstrated that Atrazine increased the risk of breast cancer by activating estrogen receptors, altering hormone signaling pathways, and affecting cell proliferation mechanisms [ 11 – 13 ] . Moreover, in vitro studies have further elucidated the multiple effects of Atrazine on the proliferation and gene expression of breast cancer cells, providing experimental evidence for its carcinogenic potential [ 11 – 14 ] . Nevertheless, there are still substantial gaps in the systematic investigation of the mechanisms and targets associated with Atrazine-induced breast cancer. Therefore, further in-depth research is needed to elucidate the toxicological mechanisms of Atrazine and its impact on breast cancer risk. Traditional toxicity assessment approaches have significant limitations in efficiently and holistically evaluating the multifaceted biological consequences of emerging environmental contaminants. Current methodologies predominantly focus on examining single or limited molecular interactions, failing to account for broader systemic impacts and interlinked toxicological pathways. Environmental contaminants, however, frequently induce complex adverse effects by interfering with diverse biological networks. Consequently, there is an urgent need for novel strategies to thoroughly and accurately assess the health risks posed by the expanding array of synthetic industrial compounds with poorly characterized toxicity profiles in ecosystems. Network toxicology, grounded in principles from network biology and pharmacology, combines advanced bioinformatics tools, large-scale data analytics, and multi-omics technologies—including genomics, proteomics, and metabolomics [ 15 – 17 ] . Its foundational framework relies on synthesizing diverse databases to map interconnected networks linking chemical agents, their toxicological effects, and biological targets [ 18 – 20 ] . By translating the intricate mechanisms of multi-target toxic substances into structured visual frameworks, this methodology enables the systematic exploration of protein interaction dynamics and predictive modeling of molecular pathways driving toxicity-associated pathologies. Such integrative models enhance mechanistic clarity and support hypothesis generation for disease-related toxicological outcomes. Mendelian randomization (MR) is a reliable and reproducible method that utilizes genetic variants as instrumental variables (IVs) to assess causal relationships between exposures and outcomes. This approach thereby facilitating the identification of potential therapeutic targets for many diseases [ 21 – 23 ] . By integrating the potential toxicity mechanisms of Atrazine provided by network toxicology, MR can validate causal associations. This study identified the common potential targets of Atrazine and breast cancer through network toxicology and explored the possible causal relationships between Atrazine's common potential targets and breast cancer using MR analysis. These findings thereby provided a promising direction for future breast cancer research and the investigation of its potential pathogenesis. Methods Identifying Atrazine-breast cancer common targets The standard structure and SMILES string of Atrazine were acquired using the PubChem database. Potential targets of Atrazine were retrieved from the Comparative Toxicogenomics Database (CTD, https://toxnet.nlm.nih.gov/newtoxnet/ctd.htm ) [ 24 ] . The SMILES string of Atrazine was uploaded to the STITCH [ 17 ] ( http://stitch.embl.de/ ) database and SwissTargetPrediction [ 25 ] ( http://www.swisstargetprediction.ch/ ) database to identify and predict neglected targets. The target names acquired were standardized using the Uniprot [ 26 ] ( https://www.uniprot.org/ ) database, and duplicates were removed. The targets of breast cancer or breast carcinoma were retrieved from the GeneCards ( https://www.genecards.org/ ) and OMIM ( https://www.omim.org/ ) databases [ 17 ] . To ensure that the obtained genes are highly correlated with breast cancer and toxic effects, the threshold value for 'score' was set at the median value. The genes with a 'score' above the median were selected to establish the breast cancer target library [ 17 ] . In addition, the common potential targets between Atrazine targets and breast cancer targets were identified and visualized using a Venn diagram. Causal associations between potential targets and breast cancer via Mendelian randomization (MR) analysis Data source The expression quantitative trait loci (eQTLs) data of the common potential targets and breast cancer (ebi-a-GCST90018799) GWAS data were obtained from the IEU OpenGWAS project database (updated to 12-15-2024; https://gwas.mrcieu.ac.uk/ ). Since the original studies had obtained informed consent from the participants, no further approval from an ethics committee was required for this component of the study. Selection of Instrumental Variables The instrumental variables (IVs) in this study satisfy the three core assumptions of MR: relevance, independence, and exclusion restriction. The IVs were selected with a significance threshold of p 10. The linkage disequilibrium (LD) coefficient r² was set at 0.3, with an LD region width of 100 kb. Additionally, the minor allele frequency (MAF) was required to be greater than 0.01. These criteria were applied to ensure the independence of the IVs and to eliminate the influence of LD on the results. Next, single nucleotide polymorphism (SNPs) associated with confounding factors and outcomes were excluded based on the GWAS Catalog [ 27 – 29 ] . The IVs were selected from within ± 500 kb of the cis-acting region of protein-coding genes. Relevant IVs were extracted from the eQTLs of protein-coding genes. Subsequently, SNPs associated with breast cancer were extracted from the GWAS summary data, with palindromic SNPs excluded. Outlier SNPs were then removed using the MR-PRESSO method. All statistical analyses were performed using the TwoSample MR and MR-PRESSO packages in R version 4.1.0. The flowchart illustrating the selection of IVs is shown in Fig. 1 . Mendelian randomization analysis Five regression models were employed using SNPs as IVs to conduct two-sample Mendelian randomization (MR) analysis: MR-Egger regression, inverse variance weighted (IVW) regression, weighted median regression, weighted mode regression, and simple mode regression. This approach was employed to assess the potential causal relationship between the potential targets and breast cancer risk. Among these methods, IVW is currently the most widely used and is generally regarded as the primary method for causal estimation. The effect of each individual SNP on the outcome was assessed using the Wald ratio method, while the remaining analyses utilized the IVW method. The false discovery rate (FDR) was calculated to adjust the p -values. The intercept term in MR-Egger regression and MR-PRESSO were employed to assess pleiotropy. The absence of pleiotropy was indicated by an MR-Egger regression intercept not significantly different from zero ( p > 0.05) and an MR-PRESSO p -value > 0.05. A leave-one-out analysis was performed to assess the sensitivity of the results. This involved sequentially removing each SNP and re-analyzing the remaining SNPs to assess the impact of each SNP on the overall results. The heterogeneity of SNPs was assessed using Cochran's Q test. If the p -value is less than 0.05, the results are considered heterogeneous. I-squared (I²) is an alternative statistical measure of heterogeneity, representing the proportion of total variability attributable to heterogeneity. The range of I² values extends from 0–100%. An I² value exceeding 50% suggests the presence of heterogeneity in the results derived from the IVW regression. All analyses were conducted using the TwoSample MR package in R version 4.1.0. Construction of a protein-protein interaction network In this study, based on the positive results of MR analysis, we constructed the protein-protein interaction (PPI) network of potential target proteins using the STRING database. The confidence score was set at 0.4 as the minimum interaction score required, and the species was set as Homo sapiens [ 23 , 30 ] . Additionally, the PPI network was visualized using Cytoscape version 3.6.0 [ 31 ] , and the topological properties of the network nodes and edges were calculated, including degree, closeness centrality, betweenness centrality, and average shortest path length. Nodes corresponding to targets that simultaneously meet the following criteria were identified as the core targets of Atrazine-induced breast cancer: Betweenness centrality > median value, Closeness centrality > median value, Average shortest path length twice the median value. GO and KEGG pathway enrichment analyses Gene Ontology (GO) was employed to annotate the 38 functional core genes, especially in terms of molecular function (MF), biological process (BP), and cellular component (CC). We performed GO enrichment analysis and visualized the top ten significantly enriched terms according to their enrichment scores [ 32 ] . Additionally, we conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and visualized the top ten significantly enriched pathways based on their count values. The GO (BP/CC/MF) and KEGG enrichment analyses were performed using the R package ‘clusterProfiler’ with a significance threshold of p value < 0.05. Molecular docking for Atrazine and core targets The molecular docking approach (Autodock Vina version 1.2.2) [ 17 ] was employed to analyze the binding affinity and interaction patterns between Atrazine and the core targets. Molecular docking was performed between Atrazine and the proteins encoded by the top five prioritized target genes. The crystal structure of Atrazine was acquired from the PubChem Compound Database ( https://pubchem.ncbi.nlm.nih.gov/ ), while the structures of candidate proteins were obtained from the Protein Data Bank (PDB, http://www.rcsb.org/ ) or the AlphaFold Protein Structure Database ( https://alphafold.ebi.ac.uk/ ). Specifically, all water molecules were removed from the proteins and Atrazine files, and polar hydrogen atoms were added. The center of the Grid Box was positioned to encompass all domains of the protein and allowed unrestricted molecular movement. The results of the molecular docking were visualized using Discovery Studio 2024 Client. Results Identification of Atrazine affected breast cancer targets We identified 2360 Atrazine targets using the CTD, STITCH, and SwissTargetPrediction databases, and determined 8203 targets highly associated with breast cancer through the GeneCards and OMIM databases. By intersecting the targets of Atrazine and breast cancer, we obtained 1267 common targets, which were considered as the potential targets for Atrazine-affected breast cancer (Fig. 2 ). Causal associations between potential targets and breast cancer via Mendelian randomization analysis SNP selection A total of 1267 potential targets were identified in the IEU OpenGWAS project database, and the expression quantitative trait loci (eQTLs) for 1047 genes were ultimately obtained. The data were derived exclusively from individuals of European ancestry, with a summary of the information provided in Supplemental Table S1 . Following the selection process of the aforementioned criteria, a total of 30100 single-nucleotide polymorphisms (SNPs) associated with breast cancer were identified from the 1047 eQTLs data of the potential targets (genome-wide statistical signifcance threshold, p 10, indicating that weak IVs bias was excluded. After removing palindromic SNPs and outlier SNPs using the MR-PRESSO test and MR-Egger regression, the absence of horizontal pleiotropy in the IVs was confirmed (both MR-PRESSO p > 0.05 and MR-Egger regression p > 0.05). Heterogeneity among the SNPs was assessed using Cochran's Q test and I 2 statistic, and the results showed no significant heterogeneity (Cochran's Q test p > 0.05 and I 2 < 50%). Causal effects of potential targets on the development of breast cancer Through Mendelian randomization analysis of the 1047 potential targets and breast cancer, we identified 198 targets with statistically significant associations with breast cancer. After excluding results that were not robust due to heterogeneity and pleiotropy, and following correction for multiple testing using the false discovery rate (FDR), we ultimately identified 164 targets with causal associations with breast cancer ( Table 1 ). For example, the MR results indicated that TP53BP1 (IVW OR = 1.456, 95% CI: 1.218–1.740, p < 0.001, FDR < 0.001); BZW1 (IVW OR = 1.318, 95% CI: 1.188–1.463, p < 0.001, FDR < 0.001); IDH1 (IVW OR = 1.147, 95% CI: 1.091–1.205, p < 0.001, FDR < 0.001); ATG2B (IVW OR = 1.134, 95% CI: 1.051–1.224, p = 0.001, FDR = 0.008); and TLE3 (IVW OR = 1.264, 95% CI: 1.061–1.505, p = 0.009, FDR = 0.040) were risk factors for breast cancer. The volcano plot of the causal effects of identified potential targets on breast cancer is shown in Fig. 3 . Protein-protein interaction network and acquisition of core genes We constructed a PPI network, which comprises 144 nodes, 543 edges, and an average node degree of 6.62. The interactions among the 144 genes are shown in Fig. 4 . Through analysis using Cytoscape, we identified 38 core targets associated with Atrazine-induced breast cancer ( Supplemental, Table S3 ). These targets highlight their potential roles in the disease pathway and are illustrated in Fig. 5 , which shows the interactions among them. Notably, based on the ranking of degree values, the top five core targets are AKT1, CASP3, HSPA4, CCND1, and MAPK3. Function and pathway enrichment analysis of core targets To investigate the functional characteristics and biological relevance of the 38 identified core targets, we performed GO and KEGG enrichment analyses. The results of the GO enrichment analysis indicated that the biological processes (BP) associated with the 38 core targets were mainly related to the intrinsic apoptotic signaling pathway and response to ionizing radiation. The cellular components (CC) mainly implicated were the protein kinase complex and transferase complex involved in transferring phosphorus-containing groups. The molecular functions (MF) primarily involved were cyclin-dependent protein serine/threonine kinase regulator activity and protein serine/threonine kinase activity (Fig. 6 ). The KEGG enrichment analysis indicated that the top three signaling pathways associated with the 38 core targets were cellular senescence, human T-cell leukemia virus 1 infection, and small cell lung cancer pathways (Fig. 7 ). Molecular docking for Atrazine and core target proteins of breast cancer We obtained the binding sites and interactions between Atrazine and the proteins encoded by the top five core target genes in the PPI network. The binding energy for each interaction was generated. These five core target proteins all exhibited high affinity with Atrazine (binding energies < -5 kcal/moL) ( Table 2 ). Atrazine formed connections with the target proteins through visible hydrogen bonds and strong electrostatic interactions. The docking results for the five proteins with Atrazine are shown in Fig. 8 . Discussion In our study, we innovatively and synergistically integrated multiple biomedical databases with differential gene expression datasets using network evaluation tools to elucidate the underlying mechanisms linking Atrazine exposure to breast cancer incidence. We performed a Mendelian randomization analysis, which revealed 164 potential targets closely implicated in Atrazine-triggered breast cancer development. We established a multi-layered interaction network through network toxicological analysis, through which we ultimately identified 38 potential targets of Atrazine-induced breast cancer. The biological processes of these targets are mainly enriched in the intrinsic apoptotic signaling pathway and the response to ionizing radiation. Their molecular functions primarily indicate cyclin-dependent protein serine/threonine kinase regulator activity and protein serine/threonine kinase activity. The top three signaling pathways associated with these potential targets are enriched in cellular senescence, human T-cell leukemia virus 1 infection, and small cell lung cancer pathways. Notably, the top five core targets (AKT1, CASP3, HSPA4, CCND1, and MAPK3) exhibit strong binding affinities with Atrazine, indicating that they may play central roles in the pathological mechanisms of Atrazine-induced breast cancer. As a widely used herbicide in agriculture, the potential carcinogenic effects of Atrazine have attracted attention in recent years. However, only a handful of studies have investigated its exposure effect on the development of breast cancer. Epidemiological studies revealed that Atrazine may disrupt the endocrine system due to its estrogenic properties, thereby causing breast cancer [ 14 , 33 ] . However, this hypothesis has not been fully supported in epidemiological studies, highlighting the necessity of mechanism research. At the cellular and molecular level, multiple experiments have revealed the multi-pathway cancer-promoting potential of Atrazine. Jessica et al. reported that Atrazine showed a trend toward increased cell viability at a concentration of 10 nM. This proliferative effect may be related to the presence of ER [ 34 ] . Jean-Paul et al. revealed that Atrazine induced oxidative stress, DNA damage, and cell morphological changes by down-regulating key functional proteins in MCF-7 cells, which may promote the pathological process [ 35 ] . Similarly, Sasikala et al. demonstrated that Atrazine caused higher DNA cleavage and increased the risk for developing breast cancer [ 36 ] . Notably, Mengqi Wang et al. revealed that Atrazine exposure significantly increased 4T1 cell proliferation and tumor volume via upregulation of MMP2, MMP7, and MMP9. Immunologically, Atrazine decreased the proportions of CD4 + and CD3 + lymphocytes in the spleen and lymph nodes and lowered the CD4+/CD8 + ratio. Furthermore, tumor-infiltrating immune cells, including CD4 + T cells, CD8 + T cells, and NK cells were diminished, while regulatory T cells (Tregs) increased. Additionally, elevated IL-4 levels and reduced IFN-γ and TNF-α were observed in both serum and the tumor microenvironment, suggesting that Atrazine promotes tumor progression by suppressing anti-tumor immunity and enhancing immune escape mechanisms [ 12 ] . In addition, several studies reported that Atrazine resulted in reproductive dysfunction and increased BC risk, especially by inhibiting cAMP-specific phosphodiesterase-4 via the hypothalamic-pituitary-gonadal (HPG) axis [ 13 , 37 ] . Some animal studies have shown that long-term exposure to Atrazine increased the incidence of BC [ 33 ] . Although existing evidence shows that Atrazine still has significant biological activity at low concentrations relevant to the environment, there are still many gaps in the cross-regulatory network of its core targets and signaling pathways. In summary, Atrazine may affect breast cancer progression through multiple mechanisms such as endocrine disruption, genomic toxicity, immunosuppression and multi-system interaction, but the specific molecular pathways have not been fully elucidated. In the future, it is necessary to integrate exposure omics, multi-omics analysis and cross-species models to systematically reveal dose-effect relationships. Regarding AKT1, whose mutations are found in approximately 1% of all cancers, these mutations induced continuous activation of AKT signaling in cancer cells [ 38 , 39 ] . The human AKT family kinases consist of AKT1, AKT2, and AKT3. Studies have revealed that AKT1 is crucial for driving tumorigenesis, development, and metastatic potential in breast cancer [ 40 , 41 ] . Experimental studies have demonstrated that targeted silencing of AKT1 in breast cancer models effectively suppresses tumorigenesis by impairing cell cycle progression and activating apoptotic pathways [ 42 – 44 ] . Similarly, overexpression and/or activation of AKT1 contributes to inhibiting pro-apoptotic signals and activating survival signals in mammary epithelial cells [ 40 , 45 ] . Notably, Bijesh George revealed an unexpected role of AKT1. AKT1 exhibited the ability for bidirectional gene expression regulation. AKT1 not only positively regulated cancer-promoting genes (such as TFF1, EEF2, and SCD), but also inhibited the expression of specific genes (such as TMEM213, VSIG1, and CYP4F8). These suppressed genes are normally downregulated in breast cancers with high AKT1 expression and are associated with patient prognosis. AKT1 knockdown or inhibition significantly altered the splicing patterns [ 46 – 48 ] , which may influence tumor phenotypes by regulating post-transcriptional modifications. These splicing events AKT1 plays a central role in metabolic reprogramming, cell proliferation, and heterogeneity of breast cancer by regulating gene expression and splicing variation. Its unique inhibitory transcriptional regulation and splicing function provide a new direction for the development of AKT1-specific therapies and emphasize the clinical value of targeting splicing mechanisms. Our MR results indicated that AKT1 (IVW OR = 0.933, 95% CI: 0.906–0.961, p < 0.001, FDR < 0.001) was a protective factor. This inconsistent result may be partially explained by the unique function of AKT1, as mentioned above, which suggest that further in-depth experimental studies are needed. CASP3 is one of the proteases in the caspase family that plays a crucial role in maintaining cellular homeostasis through the regulation of cell death and inflammatory responses. Several studies have indicated that caspase-3 levels in breast cancer (BC) cells are reduced compared to those in normal cells [ 49 ] . Yang et al. demonstrated that caspase-3 can trigger apoptosis, enhance the cleavage of cell death-related substrates, and induce DNA fragmentation. They further showed that introducing caspase-3 cDNA into cells significantly increased their sensitivity to apoptosis induced by chemotherapeutic agents such as doxorubicin and etoposide [ 50 ] . Similarly, reconstitution of caspase-3 in MCF-7 BC cells enhanced their responsiveness to these drugs, implicating caspase-3 deficiency as a potential contributor to chemoresistance [ 51 , 52 ] . Consistent with these findings, Devarajan et al. further confirmed that restoring caspase-3 expression in caspase-3-deficient BC cells led to increased sensitivity to doxorubicin and etoposide [ 53 ] . Our MR results indicated that CASP3 was a risk factor for BC (IVW OR = 1.032, 95% CI: 1.011–1.054, p = 0.003, FDR = 0.017). This finding is consistent with those of previous studies. Together, these studies support the notion that caspase-3 deficiency may serve as a mechanism underlying resistance to chemotherapy in breast cancer patients. Caspase-3 holds potential as a valuable target for cancer treatment strategies. HSPA4 belongs to HSP70 family and is a tumor-associated membrane protein. Heat shock proteins (HSPs) are frequently overexpressed in numerous malignancies and are associated with the progression of various tumors [ 54 ] . HSPA4 has been proposed as a potential tumor antigen in lung and liver cancers [ 55 ] . The study by Yan Gu demonstrated that pathogenic anti-HSPA4 IgG binds to membrane-associated glycosylated HSPA4, thereby activating the CXCR4/SDF1α axis through the NF-κB signaling pathway. This interaction ultimately facilitates lymph node metastasis in breast cancer [ 56 ] . They also revealed that elevated HSPA4 levels were robustly correlated with the occurrence of axillary lymph node metastasis in invasive ductal carcinoma [ 56 ] . High expression of HSPA4 in BC was not only associated with reduced progression-free survival and overall survival [ 57 ] ; moreover, elevated levels of HSPA4 and anti-HSPA4 IgG in BC were correlated with lymph node metastasis in clinical cohorts [ 56 ] . Consistent with these findings, our MR results showed that HSPA4 (IVW OR = 1.040, 95% CI: 1.014–1.067, p = 0.003, FDR = 0.015) was a risk factor for breast cancer. Collectively, the characterization of tumor-associated antigens and their corresponding pathogenic antibodies highlights novel diagnostic and therapeutic avenues for cancer, potentially leading to significant improvements in cancer patient survival outcomes. Regarding CCND1, which is located on the long arm of chromosome 11, it encodes the cyclin D1 protein [ 58 ] . Numerous studies have demonstrated that Cyclin D1 (encoded by the CCND1 gene) can modulate steroid hormone receptor signaling. Specifically, it can activate the oestrogen receptor (ER) and suppress the androgen receptor activity in breast epithelial cells [ 59 – 61 ] . CCND1 is overexpressed in approximately 50% of breast cancers [ 62 , 63 ] , while gene amplification occurs in 9–15% of cases [ 62 , 64 – 66 ] . Notably, CCND1 amplification has been linked to a higher risk of recurrence [ 62 , 65 , 66 ] and reduced responsiveness to chemotherapy [ 64 ] . Moreover, it is associated with increased proliferative activity, higher histopathological grades, and a predominance of the Luminal B molecular subtype [ 62 ] . Curtis et al. further identified a high-risk, ER-positive breast cancer subgroup characterized by high CCND1 copy number variations [ 67 ] . Similarly, a study by Sarah at al. reported a significant correlation between CCND1 amplification and positive ER status (OR:1.70, 95% CI:1.19–2.43, p = 0.004), as well as Cyclin D1 protein overexpression (OR: 5.64, 95% CI: 2.32–13.74, p = 0.0001). This study also demonstrated that CCND1 amplification was independently associated with shorter recurrence-free survival (RFS) and overall survival (OS) in breast cancer patients, particularly in those receiving endocrine therapy [ 68 ] . Taken together, these findings suggest that CCND1 gene amplification may serve as a prognostic and predictive biomarker in breast cancer, especially in the context of ER-positive tumors and endocrine treatment resistance. MAPK3 is a key member of the mitogen-activated protein kinase (MAPK) family and is mainly involved in the regulation of biological processes such as cell proliferation, differentiation, survival, apoptosis, and stress response [ 69 , 70 ] . The MAPK pathway, also referred to as the RAS/RAF/MEK/ERK cascade, transmits extracellular signals from cell surface receptors to nuclear transcription factors, thereby modulating cellular responses [ 71 ] . The activation of the MAPK signaling cascade promotes tumor growth, enhances metastatic potential, and induces neovascularization in malignancies [ 72 ] . The MAPK pathway is activated in about 50% of breast cancers [ 73 – 75 ] . It has been reported that the activation of the MAPK pathway is associated with the repression of ER gene transcription, thereby contributing to endocrine resistance in BC [ 76 ] . Other studies have also demonstrated that the MAPK/ERK signaling cascade plays a significant role in both cancer metastasis and the development of drug resistance [ 77 – 79 ] . These findings suggest that inhibition of MAPK pathway oncoproteins (RAF, MEK, and ERK) may represent a potential therapeutic approach to overcome endocrine resistance mediated by MAPK pathway alterations. In this study, we identified 38 core targets of Atrazine-induced breast cancer, and KEGG analysis revealed that one of the top three enriched pathways of these core target genes was cellular senescence. Although the cellular senescence pathway has always been a highly complicated phenotype, several studies have elucidated that a population of variant human mammary epithelial cells (HMECs) can escape cellular senescence via methylation-dependent CDKN2A gene silencing [ 80 – 82 ] . Notably, this process shows parallels with the cytogenetic profiles characteristic of preinvasive and early-stage neoplastic transformations in mammary tissue [ 83 , 84 ] . Berman et al. demonstrated that mammary-epithelial cells can escape senescence (M0) via downregulation of the p16 protein. This process leads to the formation of premalignant lesions and, ultimately, results in invasive carcinoma [ 85 ] . During the immortalization process of normal human mammary epithelial cells (HMECs), the escape or bypass of senescence is associated with the hypermethylation of the p16 promoter. Additionally, several signaling pathways and proteins have been identified as contributing factors [ 86 ] . Upregulation of prostaglandin cyclooxygenase-2 (COX2) has been documented in HMECs that have escaped or bypassed senescence. Moreover, COX2 expression is often observed in regions that exhibit hypermethylation of the CDKN2A gene [ 86 ] . As previously described, cellular senescence serves as an early impediment to human carcinogenesis and plays a crucial role in the initial stages of cellular immortalization and neoplastic transformation [ 87 ] . Notably, several studies have elucidated the presence of cellular senescence within the breast cancer microenvironment, specifically in at least three distinct cell populations: near-normal epithelia, fibroblasts, and lymphocytes. These populations play significant roles in cancer development and progression [ 88 – 92 ] . Collectively, these findings highlight the intricate nature of the cellular senescence phenotype, the challenges associated with detecting it, and its multifaceted impact on both senescent cells and the surrounding microenvironment. To further investigate the effects of Atrazine on breast cancer, we conducted molecular docking analysis to explore its interaction with the top five core target proteins. All core target proteins exhibited high affinity for Atrazine, characterized by binding energies less than zero. This suggests that Atrazine can spontaneously bind to each core target protein, thereby facilitating the exploration of the molecular mechanisms underlying its potential role in promoting breast cancer. Although the binding energy data support the hypothesis of target interaction, they need to be further verified by experiments. The clinical relevance of targeting these proteins is underscored by recent therapeutic advancements. Particularly compelling evidence emerges from AKT1-targeted therapies: The LOTUS phase II clinical trial demonstrated that combining ipatasertib (an ATP-competitive pan-AKT inhibitor) with paclitaxel significantly improved progression-free survival (PFS) in triple-negative breast cancer (TNBC) patients (6.2 vs 4.9 months; HR 0.60, 95% CI 0.37–0.98; P = 0.037), especially in those carrying the PIK3CA/AKT1/PTEN mutation [ 93 ] . Notably, ipatasertib showed an increase in overall survival (OS) at 50% OS events, from 18.4 to 23.1 months (HR 0.62, 95% CI 0.37–1.05), prompting an ongoing phase III trial (NCT03337724) [ 94 ] . Similarly, another PAKT phase II clinical trial showed that capivasertib combined with paclitaxel exerted an advantage in median PFS from 4.2 to 5.9 months (HR 0.75, 95% CI 0.52–1.08; one-sided P = 0.06). In addition, a median OS benefit was observed from 12.6 to 19.1 months in the capivasertib group (HR 0.64, 95% CI 0.40–1.01; one-sided P = 0.02) [ 95 ] . Regarding CASP3, currently no clinical drugs directly target CASP3. However, drugs that indirectly activate CASP3 by inducing apoptosis via the mitochondrial pathway show promising clinical application [ 96 ] . For example, the highly selective BCL-2 inhibitor venetoclax has already been approved in the United States for the treatment of chronic lymphocytic leukaemia with 17p deletion. Moreover, an exploratory analysis of a randomized phase II clinical trial (VERONICA) in ER-positive metastatic breast cancer indicated a slightly improved clinical benefit rate (CBR) and PFS with venetoclax in tumors that exhibited strong BCL2 expression (IHC 3+), a BCL2/BCLXL Histoscore ratio ≥ 1, or PIK3CA-wild-type status (particularly those with high BCL2 expression) [ 97 ] . In addition, MAPK3 (also known as ERK1), it has a high structural similarity (approximately 85% homology) with MAPK1 (ERK2). Due to this similarity, there are currently no approved drugs that specifically target MAPK3. Research has predominantly focused on inhibitors targeting both ERK1 and ERK2. Robert and other studies have reported that Ulixertinib, MK-8353, and GDC-0994 are effective and specific inhibitors of ERK1/2 and are in early clinical trials for the treatment of various advanced or metastatic solid tumors [ 98 – 101 ] . Taken together, our findings suggest that AKT1, CASP3 and MAPK3 are promising targets for the treatment of breast cancer and hold significant potential for broader applications that need further in-depth exploration. Our study elucidates the adverse health implications of Atrazine contamination through advanced epidemiological and toxicological approaches. By employing MR analysis integrated with network toxicology, we established robust evidence supporting the herbicide's pathogenic role in breast carcinogenesis. To enhance the reliability of our MR findings, we carefully eliminated the impact of potential confounders. Additionally, by elucidating the specific molecular pathways through which Atrazine may contribute to breast cancer onset, our findings underscore the critical importance of reevaluating exposure thresholds for agricultural chemicals in environmental toxicology. The demonstrated oncogenic properties of this widely-used herbicide necessitate urgent interdisciplinary efforts to reassess its biosafety profile and epidemiological impact. This comprehensive investigation not only advances our comprehension of environmental carcinogens but also establishes a novel paradigm for assessing chemical toxicity through integrated omics approaches. Network toxicology combines with molecular docking technology reveal multi-target synergism and is highly efficient. The capacity for identifying and evaluating the toxicological impacts of chemical substances has markedly advanced. However, it has limitations, as it cannot simulate physiological differences observed in animal experiments nor can it model long-term low-dose exposure. Therefore, further in vivo experiments are required to validate the function of the identified targets and to establish long-term low-dose exposure models for more comprehensive exploration. Conclusions In conclusion, we have developed a comprehensive analytical model that effectively elucidates the direct causal link between Atrazine and breast cancer, as well as the intricate molecular pathways involved. This work addresses a critical knowledge gap in environmental oncology by providing mechanistic evidence for Atrazine's carcinogenic potential, particularly relevant given its persistence as a pervasive environmental contaminant with documented bioaccumulation in human populations. Abbreviations BC breast cancer MR Mendelian randomization IVs instrumental variables eQTLs expression quantitative trait loci SNPs single-nucleotide polymorphisms LD linkage disequilibrium MAF minor allele frequency IVW inverse variance weighted FDR false discovery rate PPI Protein-protein interaction KEGG Kyoto Encyclopedia of Genes and Genomes GO Gene Ontology BP biological process MF molecular function CC cell component. Declarations The authors declare that they have no competing financial interests. Author Contribution Zitong Zhao: Conceptualization, Methodology, Data analysis, Visualization, Writing – original draft, Writing – review & editing. Yifan Cai: Methodology, Literature. Chaofan Li: Methodology, Literature. Chong Du: Writing – review & editing. Shuqun Zhang: Writing – review & editing. Acknowledgements The authors would like to acknowledge the support of the Key Laboratory of Integration of Traditional Chinese Medicine and Western Medicine (No.2022-ZXY-SYS-002), the National Natural Science Foundation of China (No.82174164), and the Shaanxi Provincial Fund (No.2024SF-YBXM-515) for this study. References Bray, F., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024. 74(3): p. 229-263. Britt, K.L., J. Cuzick, and K.A. Phillips, Key steps for effective breast cancer prevention. Nat Rev Cancer, 2020. 20(8): p. 417-436. Colleoni, M., et al., Annual Hazard Rates of Recurrence for Breast Cancer During 24 Years of Follow-Up: Results From the International Breast Cancer Study Group Trials I to V. J Clin Oncol, 2016. 34(9): p. 927-35. Oliveira, W.L., et al., Does the atrazine increase animal mortality: Unraveling through a meta-analytic study. Sci Total Environ, 2024. 951: p. 175553. Deng, S., et al., Advances in understanding and mitigating Atrazine's environmental and health impact: A comprehensive review. J Environ Manage, 2024. 365: p. 121530. Hu, Y., et al., Impact of atrazine on soil microbial properties: A meta-analysis. Environ Pollut, 2023. 323: p. 121337. Arar, M., et al., Microbial hydrolysis of atrazine in contaminated groundwater. Chemosphere, 2023. 322: p. 138226. Chang, J., et al., Toxicological effects, environmental behaviors and remediation technologies of herbicide atrazine in soil and sediment: A comprehensive review. Chemosphere, 2022. 307(Pt 3): p. 136006. Zhao, H., et al., Endocrine toxicity of atrazine and its underlying mechanisms. Toxicology, 2024. 505: p. 153846. Lu, Y.S., et al., Integrated metabolomics and transcriptomics analysis reveals new biomarkers and mechanistic insights on atrazine exposures in MCF‑7 cells. Ecotoxicol Environ Saf, 2022. 232: p. 113244. Yang, Y.F., et al., Accumulated inflammation and fibrosis participate in atrazine induced ovary toxicity in mice. Environ Pollut, 2024. 360: p. 124672. Wang, M., et al., Atrazine promotes breast cancer development by suppressing immune function and upregulating MMP expression. Ecotoxicol Environ Saf, 2023. 253: p. 114691. Kass, L., A.L. Gomez, and G.A. Altamirano, Relationship between agrochemical compounds and mammary gland development and breast cancer. Mol Cell Endocrinol, 2020. 508: p. 110789. Panis, C., et al., Exposure to Pesticides and Breast Cancer in an Agricultural Region in Brazil. Environ Sci Technol, 2024. 58(24): p. 10470-10481. Zhao, Z., et al., An integrated strategy combining network toxicology and feature-based molecular networking for exploring hepatotoxic constituents and mechanism of Epimedii Folium-induced hepatotoxicity in vitro. Food Chem Toxicol, 2023. 176: p. 113785. Huang, S., Analysis of environmental pollutant Bisphenol F elicited prostate injury targets and underlying mechanisms through network toxicology, molecular docking, and multi-level bioinformatics data integration. Toxicology, 2024. 506: p. 153847. Huang, S., Efficient analysis of toxicity and mechanisms of environmental pollutants with network toxicology and molecular docking strategy: Acetyl tributyl citrate as an example. Sci Total Environ, 2023. 905: p. 167904. He, J., et al., Network toxicological and molecular docking to investigate the mechanisms of toxicity of agricultural chemical Thiabendazole. Chemosphere, 2024. 363: p. 142711. Li, J. and H. Bi, Clarification of the molecular mechanisms underlying glyphosate-induced major depressive disorder: a network toxicology approach. Ann Gen Psychiatry, 2024. 23(1): p. 8. Xiao, F., J. Qiu, and Y. Zhao, Exploring the Potential Toxicological Mechanisms of Vine Tea on the Liver Based on Network Toxicology and Transcriptomics. Front Pharmacol, 2022. 13: p. 855926. Lin, J., J. Zhou, and Y. Xu, Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain, 2023. 146(8): p. 3364-3372. Ference, B.A., Interpreting the Clinical Implications of Drug-Target Mendelian Randomization Studies. J Am Coll Cardiol, 2022. 80(7): p. 663-665. Cao, Y., et al., Identification of potential drug targets for rheumatoid arthritis from genetic insights: a Mendelian randomization study. J Transl Med, 2023. 21(1): p. 616. Davis, A.P., et al., Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res, 2023. 51(D1): p. D1257-d1262. Chen, S., et al., Uncovering the mechanism of resveratrol in the treatment of diabetic kidney disease based on network pharmacology, molecular docking, and experimental validation. J Transl Med, 2023. 21(1): p. 380. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res, 2023. 51(D1): p. D523-d531. Sollis, E., et al., The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res, 2023. 51(D1): p. D977-d985. Sun, Y.S., et al., Risk Factors and Preventions of Breast Cancer. Int J Biol Sci, 2017. 13(11): p. 1387-1397. Veronesi, U., et al., Breast cancer. Lancet, 2005. 365(9472): p. 1727-41. Szklarczyk, D., et al., The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res, 2023. 51(D1): p. D638-d646. Otasek, D., et al., Cytoscape Automation: empowering workflow-based network analysis. Genome Biol, 2019. 20(1): p. 185. Kersey, A.L., I. Singh, and A.K. Gaharwar, Inorganic ions activate lineage-specific gene regulatory networks. Acta Biomater, 2024. 183: p. 371-386. Wan, M.L.Y., V.A. Co, and H. El-Nezami, Endocrine disrupting chemicals and breast cancer: a systematic review of epidemiological studies. Crit Rev Food Sci Nutr, 2022. 62(24): p. 6549-6576. Rich, J.D., S.M. Gabriel, and J.R. Schultz-Norton, In vitro effects of herbicides and insecticides on human breast cells. ISRN Toxicol, 2012. 2012: p. 232461. Lasserre, J.P., et al., Effects of the endocrine disruptors atrazine and PCB 153 on the protein expression of MCF-7 human cells. J Proteome Res, 2009. 8(12): p. 5485-96. Sasikala, S., et al., Predicting the relationship between pesticide genotoxicity and breast cancer risk in South Indian women in in vitro and in vivo experiments. Sci Rep, 2023. 13(1): p. 9712. Wirbisky, S.E. and J.L. Freeman, Atrazine Exposure and Reproductive Dysfunction through the Hypothalamus-Pituitary-Gonadal (HPG) Axis. Toxics, 2015. 3(4): p. 414-450. Carpten, J.D., et al., A transforming mutation in the pleckstrin homology domain of AKT1 in cancer. Nature, 2007. 448(7152): p. 439-44. Landgraf, K.E., C. Pilling, and J.J. Falke, Molecular mechanism of an oncogenic mutation that alters membrane targeting: Glu17Lys modifies the PIP lipid specificity of the AKT1 PH domain. Biochemistry, 2008. 47(47): p. 12260-9. Dillon, R.L., et al., Akt1 and akt2 play distinct roles in the initiation and metastatic phases of mammary tumor progression. Cancer Res, 2009. 69(12): p. 5057-64. Riggio, M., et al., PI3K/AKT pathway regulates phosphorylation of steroid receptors, hormone independence and tumor differentiation in breast cancer. Carcinogenesis, 2012. 33(3): p. 509-18. Hinz, N. and M. Jücker, Distinct functions of AKT isoforms in breast cancer: a comprehensive review. Cell Commun Signal, 2019. 17(1): p. 154. Watson, K.L. and R.A. Moorehead, Loss of Akt1 or Akt2 delays mammary tumor onset and suppresses tumor growth rate in MTB-IGFIR transgenic mice. BMC Cancer, 2013. 13: p. 375. Gargini, R., et al., Cancer stem cell-like phenotype and survival are coordinately regulated by Akt/FoxO/Bim pathway. Stem Cells, 2015. 33(3): p. 646-60. Hutchinson, J., et al., Activation of Akt (protein kinase B) in mammary epithelium provides a critical cell survival signal required for tumor progression. Mol Cell Biol, 2001. 21(6): p. 2203-12. George, B., et al., AKT1 Transcriptomic Landscape in Breast Cancer Cells. Cells, 2022. 11(15). Whitsett, T.G., et al., Molecular determinants of lung cancer metastasis to the central nervous system. Transl Lung Cancer Res, 2013. 2(4): p. 273-83. Speers, C., et al., Identification of novel kinase targets for the treatment of estrogen receptor-negative breast cancer. Clin Cancer Res, 2009. 15(20): p. 6327-40. Krüger, S., T. Fahrenkrog, and H. Müller, Proliferative and apoptotic activity in lobular breast carcinoma. Int J Mol Med, 1999. 4(2): p. 171-4. Yang, X.H., et al., Reconstitution of caspase 3 sensitizes MCF-7 breast cancer cells to doxorubicin- and etoposide-induced apoptosis. Cancer Res, 2001. 61(1): p. 348-54. Fulda, S., et al., Molecular ordering of apoptosis induced by anticancer drugs in neuroblastoma cells. Cancer Res, 1998. 58(19): p. 4453-60. Blanc, C., et al., Caspase-3 is essential for procaspase-9 processing and cisplatin-induced apoptosis of MCF-7 breast cancer cells. Cancer Res, 2000. 60(16): p. 4386-90. Devarajan, E., et al., Down-regulation of caspase 3 in breast cancer: a possible mechanism for chemoresistance. Oncogene, 2002. 21(57): p. 8843-51. Srivastava, P.K., M.K. Callahan, and M.M. Mauri, Treating human cancers with heat shock protein-peptide complexes: the road ahead. Expert Opin Biol Ther, 2009. 9(2): p. 179-86. Gotoh, K., et al., Apg-2 has a chaperone-like activity similar to Hsp110 and is overexpressed in hepatocellular carcinomas. FEBS Lett, 2004. 560(1-3): p. 19-24. Gu, Y., et al., Tumor-educated B cells selectively promote breast cancer lymph node metastasis by HSPA4-targeting IgG. Nat Med, 2019. 25(2): p. 312-322. Takeshita, T., et al., Transcriptomic and functional pathway features were associated with survival after pathological complete response to neoadjuvant chemotherapy in breast cancer. Am J Cancer Res, 2020. 10(8): p. 2555-2569. Valla, M., et al., CCND1 Amplification in Breast Cancer -associations With Proliferation, Histopathological Grade, Molecular Subtype and Prognosis. J Mammary Gland Biol Neoplasia, 2022. 27(1): p. 67-77. Neuman, E., et al., Cyclin D1 stimulation of estrogen receptor transcriptional activity independent of cdk4. Mol Cell Biol, 1997. 17(9): p. 5338-47. Zwijsen, R.M., et al., CDK-independent activation of estrogen receptor by cyclin D1. Cell, 1997. 88(3): p. 405-15. Petre-Draviam, C.E., et al., Specificity of cyclin D1 for androgen receptor regulation. Cancer Res, 2003. 63(16): p. 4903-13. Ortiz, A.B., et al., Prognostic significance of cyclin D1 protein expression and gene amplification in invasive breast carcinoma. PLoS One, 2017. 12(11): p. e0188068. Bartkova, J., et al., Cyclin D1 protein expression and function in human breast cancer. Int J Cancer, 1994. 57(3): p. 353-61. Yang, L., et al., Somatic alterations of TP53, ERBB2, PIK3CA and CCND1 are associated with chemosensitivity for breast cancers. Cancer Sci, 2019. 110(4): p. 1389-1400. Lundgren, K., et al., Effects of cyclin D1 gene amplification and protein expression on time to recurrence in postmenopausal breast cancer patients treated with anastrozole or tamoxifen: a TransATAC study. Breast Cancer Res, 2012. 14(2): p. R57. Bièche, I., et al., Prognostic value of CCND1 gene status in sporadic breast tumours, as determined by real-time quantitative PCR assays. Br J Cancer, 2002. 86(4): p. 580-6. Curtis, C., et al., The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 2012. 486(7403): p. 346-52. Jeffreys, S.A., et al., Prognostic and Predictive Value of CCND1/Cyclin D1 Amplification in Breast Cancer With a Focus on Postmenopausal Patients: A Systematic Review and Meta-Analysis. Front Endocrinol (Lausanne), 2022. 13: p. 895729. Yue, J. and J.M. López, Understanding MAPK Signaling Pathways in Apoptosis. Int J Mol Sci, 2020. 21(7). Yang, Z., et al., The signaling pathways and targets of traditional Chinese medicine and natural medicine in triple-negative breast cancer. J Ethnopharmacol, 2021. 264: p. 113249. Saini, K.S., et al., Targeting the PI3K/AKT/mTOR and Raf/MEK/ERK pathways in the treatment of breast cancer. Cancer Treat Rev, 2013. 39(8): p. 935-46. Raheem, F., et al., Metastatic ER+ Breast Cancer: Mechanisms of Resistance and Future Therapeutic Approaches. Int J Mol Sci, 2023. 24(22). Sivaraman, V.S., et al., Hyperexpression of mitogen-activated protein kinase in human breast cancer. J Clin Invest, 1997. 99(7): p. 1478-83. von Lintig, F.C., et al., Ras activation in human breast cancer. Breast Cancer Res Treat, 2000. 62(1): p. 51-62. Mueller, H., et al., Potential prognostic value of mitogen-activated protein kinase activity for disease-free survival of primary breast cancer patients. Int J Cancer, 2000. 89(4): p. 384-8. Stoner, M., et al., Hypoxia induces proteasome-dependent degradation of estrogen receptor alpha in ZR-75 breast cancer cells. Mol Endocrinol, 2002. 16(10): p. 2231-42. Pereira, S.S., et al., MAPK/ERK pathway inhibition is a promising treatment target for adrenocortical tumors. J Cell Biochem, 2019. 120(1): p. 894-906. Li, F., et al., CD147 promotes breast cancer migration and invasion by inducing epithelial-mesenchymal transition via the MAPK/ERK signaling pathway. BMC Cancer, 2023. 23(1): p. 1214. Xu, T., et al., Upregulation of CD147 promotes cell invasion, epithelial-to-mesenchymal transition and activates MAPK/ERK signaling pathway in colorectal cancer. Int J Clin Exp Pathol, 2014. 7(11): p. 7432-41. Brenner, A.J. and C.M. Aldaz, Chromosome 9p allelic loss and p16/CDKN2 in breast cancer and evidence of p16 inactivation in immortal breast epithelial cells. Cancer Res, 1995. 55(13): p. 2892-5. Brenner, A.J., M.R. Stampfer, and C.M. Aldaz, Increased p16 expression with first senescence arrest in human mammary epithelial cells and extended growth capacity with p16 inactivation. Oncogene, 1998. 17(2): p. 199-205. Bean, G.R., et al., Morphologically normal-appearing mammary epithelial cells obtained from high-risk women exhibit methylation silencing of INK4a/ARF. Clin Cancer Res, 2007. 13(22 Pt 1): p. 6834-41. Romanov, S.R., et al., Normal human mammary epithelial cells spontaneously escape senescence and acquire genomic changes. Nature, 2001. 409(6820): p. 633-7. Tlsty, T.D., et al., Loss of chromosomal integrity in human mammary epithelial cells subsequent to escape from senescence. J Mammary Gland Biol Neoplasia, 2001. 6(2): p. 235-43. Berman, H.K., M.L. Gauthier, and T.D. Tlsty, Premalignant breast neoplasia: a paradigm of interlesional and intralesional molecular heterogeneity and its biological and clinical ramifications. Cancer Prev Res (Phila), 2010. 3(5): p. 579-87. Sirinian, C., et al., Cellular Senescence in Normal Mammary Gland and Breast Cancer. Implications for Cancer Therapy. Genes (Basel), 2022. 13(6). Bartkova, J., et al., Oncogene-induced senescence is part of the tumorigenesis barrier imposed by DNA damage checkpoints. Nature, 2006. 444(7119): p. 633-7. Krtolica, A. and J. Campisi, Cancer and aging: a model for the cancer promoting effects of the aging stroma. Int J Biochem Cell Biol, 2002. 34(11): p. 1401-14. Haviv, I., et al., Origin of carcinoma associated fibroblasts. Cell Cycle, 2009. 8(4): p. 589-95. Ye, J., et al., Tumor-derived γδ regulatory T cells suppress innate and adaptive immunity through the induction of immunosenescence. J Immunol, 2013. 190(5): p. 2403-14. Solinas, C., et al., Tumor-infiltrating lymphocytes in breast cancer according to tumor subtype: Current state of the art. Breast, 2017. 35: p. 142-150. Martins, D. and F. Schmitt, Microenvironment in breast tumorigenesis: Friend or foe? Histol Histopathol, 2019. 34(1): p. 13-24. Kim, S.B., et al., Ipatasertib plus paclitaxel versus placebo plus paclitaxel as first-line therapy for metastatic triple-negative breast cancer (LOTUS): a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Oncol, 2017. 18(10): p. 1360-1372. Pascual, J. and N.C. Turner, Targeting the PI3-kinase pathway in triple-negative breast cancer. Ann Oncol, 2019. 30(7): p. 1051-1060. Schmid, P., et al., Capivasertib Plus Paclitaxel Versus Placebo Plus Paclitaxel As First-Line Therapy for Metastatic Triple-Negative Breast Cancer: The PAKT Trial. J Clin Oncol, 2020. 38(5): p. 423-433. Ashkenazi, A., et al., From basic apoptosis discoveries to advanced selective BCL-2 family inhibitors. Nat Rev Drug Discov, 2017. 16(4): p. 273-284. Lindeman, G.J., et al., VERONICA: Randomized Phase II Study of Fulvestrant and Venetoclax in ER-Positive Metastatic Breast Cancer Post-CDK4/6 Inhibitors - Efficacy, Safety, and Biomarker Results. Clin Cancer Res, 2022. 28(15): p. 3256-3267. Roskoski, R., Jr., Targeting ERK1/2 protein-serine/threonine kinases in human cancers. Pharmacol Res, 2019. 142: p. 151-168. Sullivan, R.J., et al., First-in-Class ERK1/2 Inhibitor Ulixertinib (BVD-523) in Patients with MAPK Mutant Advanced Solid Tumors: Results of a Phase I Dose-Escalation and Expansion Study. Cancer Discov, 2018. 8(2): p. 184-195. Lakhani, N.J., et al., A phase 1b study of the ERK inhibitor MK-8353 plus pembrolizumab in patients with advanced solid tumors. Invest New Drugs, 2024. 42(5): p. 581-589. Weekes, C., et al., A Phase Ib Study to Evaluate the MEK Inhibitor Cobimetinib in Combination with the ERK1/2 Inhibitor GDC-0994 in Patients with Advanced Solid Tumors. Oncologist, 2020. 25(10): p. 833-e1438. Tables Tables 1-2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table 1 Mendelian randomization analysis results Table2.xlsx Table 2 Docking results of available proteins with small molecules SupplementalTableS1.xlsx SupplementalTableS2.xlsx SupplementalTableS3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6806548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476540044,"identity":"c1d4759c-34ee-4ca0-82e0-6271a8790c5a","order_by":0,"name":"Zitong Zhao","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zitong","middleName":"","lastName":"Zhao","suffix":""},{"id":476540045,"identity":"f908b774-f765-4f7d-8112-36ca0fe82739","order_by":1,"name":"Yifan Cai","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Cai","suffix":""},{"id":476540046,"identity":"2dc93f18-6246-4ddc-874f-0f8bea3fc98b","order_by":2,"name":"Chaofan Li","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Chaofan","middleName":"","lastName":"Li","suffix":""},{"id":476540047,"identity":"36d63bad-55a4-4840-9901-0091e0c6be68","order_by":3,"name":"Chong Du","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Du","suffix":""},{"id":476540048,"identity":"5e8f30da-1446-449d-bc02-ff604bb369ef","order_by":4,"name":"Shuqun Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDACCShtACISKv7z8DMzH35AvJYHZ5hlJNvZ0gyI1sL4sI3ZxuA8j4IEPh3ys3sMb/PuYLA3Zz97+EViGxuP8WEeoP4am2hcWgzunDG25j3DwGzZk5dmkXCOh8fsMO+BBwzH0nIbcGmRyDGT5m1jYDM4kGNmkFAmAdTCl2DA2HAYpxb5GRAtPAbn3wC1sBnwGDfzGEjg08JwA6JFwuBGjvGDhLYEHgNmAloMbqQVW85tYzAwuPHGjCHhzAEeicPAQE7A4xf5Gckbb7xtY7A3OJ9j/PFHxQF7/v7Dhx98qLHB7TAGcNT8B9FsiOhIwKMcqgUMmD8QUDgKRsEoGAUjFAAAmu5U5hM6fvIAAAAASUVORK5CYII=","orcid":"","institution":"the Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Shuqun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-03 03:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6806548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6806548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85555393,"identity":"867da762-09c6-48cc-8b77-c675a54d67df","added_by":"auto","created_at":"2025-06-27 11:00:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25456,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis framework flowchart for evaluating the effects of 1047 genes on breast cancer\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/988dcd65740a16909b8513fd.png"},{"id":85555394,"identity":"a427e234-f69d-4d6a-b811-5b5a0f20de5f","added_by":"auto","created_at":"2025-06-27 11:00:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15725,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of the targets of Atrazine-breast cancer\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/5c286f5ef8c418535229563f.jpg"},{"id":85555398,"identity":"72dbdaa1-2290-4403-9608-fa6e85d834d1","added_by":"auto","created_at":"2025-06-27 11:00:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136969,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of the causal effects of targets on breast cancer. Red dots represent genes significantly associated with breast cancer and having positive effect sizes (β \u0026gt; 0). Blue dots indicate genes significantly associated with breast cancer and having negative effect sizes (β \u0026lt; 0). Gray dots correspond to genes that are not significantly associated with breast cancer.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/e8dc5aa9a6c198e094179533.jpg"},{"id":85555646,"identity":"2054ab13-b453-4bcc-a001-830ffcd9a19a","added_by":"auto","created_at":"2025-06-27 11:08:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":240574,"visible":true,"origin":"","legend":"\u003cp\u003eThe PPI network diagram of 144 potential targets\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/a744e092aeb2841d6be0e7e7.jpg"},{"id":85555402,"identity":"51bb2ce2-bd23-47de-a48a-410ac163e0db","added_by":"auto","created_at":"2025-06-27 11:00:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":323614,"visible":true,"origin":"","legend":"\u003cp\u003eThe PPI network diagram of 38 core targets\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/8edcd792a94a1a276384ac1a.jpg"},{"id":85555412,"identity":"6edb4c6f-5f9b-422f-8c01-3d8a86d61384","added_by":"auto","created_at":"2025-06-27 11:00:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":490351,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of core targets (top 10)\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/1fe9b842ef120cec64ccba01.jpg"},{"id":85555406,"identity":"338bcda3-2f10-4fcb-ba7a-b80b762c8512","added_by":"auto","created_at":"2025-06-27 11:00:15","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":995785,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of core targets (top 10)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/00e464089273c3aa8aa19d5f.jpg"},{"id":85555650,"identity":"0c3c1fab-af67-4b8a-80a8-fb7f30030763","added_by":"auto","created_at":"2025-06-27 11:08:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":290882,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results of target proteins with Atrazine. A: Atrazine-AKT1; B: Atrazine-CASP3; C: Atrazine-HSPA4; D: Atrazine-CCND1; E: Atrazine-MAPK3.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/7e3d1b097cc58f2c4d64f9bd.png"},{"id":90744434,"identity":"64a6e9d8-2964-419a-a29c-ac586a6ceb1e","added_by":"auto","created_at":"2025-09-07 06:17:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3536020,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/d776e0a4-d623-430f-af9a-ca9ed3c137cf.pdf"},{"id":85556462,"identity":"2a008262-8515-4ea5-9356-2dbb9fc88860","added_by":"auto","created_at":"2025-06-27 11:16:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29995,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1 Mendelian randomization analysis results\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/1a943fca9905a946d46b6a5c.xlsx"},{"id":85555395,"identity":"372badfa-a74e-414a-b68e-b953bd831332","added_by":"auto","created_at":"2025-06-27 11:00:14","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10131,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2 Docking results of available proteins with small molecules\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/8feb7c43002c4ad5b967b1d9.xlsx"},{"id":85555400,"identity":"976838f5-2513-4135-b1ff-17e7d4dc3663","added_by":"auto","created_at":"2025-06-27 11:00:15","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":46482,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/be5819f1a1531a8ca6358f22.xlsx"},{"id":85555410,"identity":"fdc828d0-2c55-4d41-8502-50441fb4187b","added_by":"auto","created_at":"2025-06-27 11:00:15","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1312713,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/0c4df96617ef26539e062084.xlsx"},{"id":85556464,"identity":"2eba5e67-d06b-41b2-9502-b28cc59231f6","added_by":"auto","created_at":"2025-06-27 11:16:15","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12467,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6806548/v1/3809c9680a83cce181c50c0f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Mendelian randomization and network toxicology to elucidate the causal role and mechanisms of Atrazine in breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) has become a significant global public health concern affecting women worldwide due to its high mortality and incidence rates\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Epidemiological surveys have shown that breast cancer remains the leading cause of cancer burden among women globally, with incidence rates continuing to rise particularly in high-income countries\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. However, the etiology of breast cancer has not yet been fully elucidated.\u003c/p\u003e \u003cp\u003eAtrazine is a widely used triazine herbicide. Owing to its high efficacy in weed control and low cost, it is extensively applied in agricultural production\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, its highly persistence in the environment and tendency to bioaccumulate have gradually rendered it a globally recognized environmental pollutant. Atrazine can contaminate surface water and groundwater through soil infiltration and dispersion into water bodies, and it may pose threats to ecosystems and human health via bioaccumulation in the food chain\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In recent years, the endocrine-disrupting effects of Atrazine have attracted considerable attention, particularly regarding its potential impact on the estrogen signaling pathway\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. A few studies have demonstrated that Atrazine increased the risk of breast cancer by activating estrogen receptors, altering hormone signaling pathways, and affecting cell proliferation mechanisms\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Moreover, in vitro studies have further elucidated the multiple effects of Atrazine on the proliferation and gene expression of breast cancer cells, providing experimental evidence for its carcinogenic potential\u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, there are still substantial gaps in the systematic investigation of the mechanisms and targets associated with Atrazine-induced breast cancer. Therefore, further in-depth research is needed to elucidate the toxicological mechanisms of Atrazine and its impact on breast cancer risk.\u003c/p\u003e \u003cp\u003eTraditional toxicity assessment approaches have significant limitations in efficiently and holistically evaluating the multifaceted biological consequences of emerging environmental contaminants. Current methodologies predominantly focus on examining single or limited molecular interactions, failing to account for broader systemic impacts and interlinked toxicological pathways. Environmental contaminants, however, frequently induce complex adverse effects by interfering with diverse biological networks. Consequently, there is an urgent need for novel strategies to thoroughly and accurately assess the health risks posed by the expanding array of synthetic industrial compounds with poorly characterized toxicity profiles in ecosystems. Network toxicology, grounded in principles from network biology and pharmacology, combines advanced bioinformatics tools, large-scale data analytics, and multi-omics technologies\u0026mdash;including genomics, proteomics, and metabolomics\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Its foundational framework relies on synthesizing diverse databases to map interconnected networks linking chemical agents, their toxicological effects, and biological targets\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. By translating the intricate mechanisms of multi-target toxic substances into structured visual frameworks, this methodology enables the systematic exploration of protein interaction dynamics and predictive modeling of molecular pathways driving toxicity-associated pathologies. Such integrative models enhance mechanistic clarity and support hypothesis generation for disease-related toxicological outcomes.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a reliable and reproducible method that utilizes genetic variants as instrumental variables (IVs) to assess causal relationships between exposures and outcomes. This approach thereby facilitating the identification of potential therapeutic targets for many diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. By integrating the potential toxicity mechanisms of Atrazine provided by network toxicology, MR can validate causal associations.\u003c/p\u003e \u003cp\u003eThis study identified the common potential targets of Atrazine and breast cancer through network toxicology and explored the possible causal relationships between Atrazine's common potential targets and breast cancer using MR analysis. These findings thereby provided a promising direction for future breast cancer research and the investigation of its potential pathogenesis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying Atrazine-breast cancer common targets\u003c/h2\u003e \u003cp\u003eThe standard structure and SMILES string of Atrazine were acquired using the PubChem database. Potential targets of Atrazine were retrieved from the Comparative Toxicogenomics Database (CTD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://toxnet.nlm.nih.gov/newtoxnet/ctd.htm\u003c/span\u003e\u003cspan address=\"https://toxnet.nlm.nih.gov/newtoxnet/ctd.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The SMILES string of Atrazine was uploaded to the STITCH\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://stitch.embl.de/\u003c/span\u003e\u003cspan address=\"http://stitch.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database and SwissTargetPrediction\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e (\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) database to identify and predict neglected targets. The target names acquired were standardized using the Uniprot\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database, and duplicates were removed. The targets of breast cancer or breast carcinoma were retrieved from the 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) 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) databases\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. To ensure that the obtained genes are highly correlated with breast cancer and toxic effects, the threshold value for 'score' was set at the median value. The genes with a 'score' above the median were selected to establish the breast cancer target library\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In addition, the common potential targets between Atrazine targets and breast cancer targets were identified and visualized using a Venn diagram.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCausal associations between potential targets and breast cancer via Mendelian randomization (MR) analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe expression quantitative trait loci (eQTLs) data of the common potential targets and breast cancer (ebi-a-GCST90018799) GWAS data were obtained from the IEU OpenGWAS project database (updated to 12-15-2024; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Since the original studies had obtained informed consent from the participants, no further approval from an ethics committee was required for this component of the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSelection of Instrumental Variables\u003c/h3\u003e\n\u003cp\u003eThe instrumental variables (IVs) in this study satisfy the three core assumptions of MR: relevance, independence, and exclusion restriction. The IVs were selected with a significance threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and F-statistics\u0026thinsp;\u0026gt;\u0026thinsp;10. The linkage disequilibrium (LD) coefficient r\u0026sup2; was set at 0.3, with an LD region width of 100 kb. Additionally, the minor allele frequency (MAF) was required to be greater than 0.01. These criteria were applied to ensure the independence of the IVs and to eliminate the influence of LD on the results. Next, single nucleotide polymorphism (SNPs) associated with confounding factors and outcomes were excluded based on the GWAS Catalog\u003csup\u003e[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The IVs were selected from within \u0026plusmn;\u0026thinsp;500 kb of the cis-acting region of protein-coding genes. Relevant IVs were extracted from the eQTLs of protein-coding genes. Subsequently, SNPs associated with breast cancer were extracted from the GWAS summary data, with palindromic SNPs excluded. Outlier SNPs were then removed using the MR-PRESSO method. All statistical analyses were performed using the TwoSample MR and MR-PRESSO packages in R version 4.1.0. The flowchart illustrating the selection of IVs is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMendelian randomization analysis\u003c/h3\u003e\n\u003cp\u003eFive regression models were employed using SNPs as IVs to conduct two-sample Mendelian randomization (MR) analysis: MR-Egger regression, inverse variance weighted (IVW) regression, weighted median regression, weighted mode regression, and simple mode regression. This approach was employed to assess the potential causal relationship between the potential targets and breast cancer risk. Among these methods, IVW is currently the most widely used and is generally regarded as the primary method for causal estimation. The effect of each individual SNP on the outcome was assessed using the Wald ratio method, while the remaining analyses utilized the IVW method. The false discovery rate (FDR) was calculated to adjust the \u003cem\u003ep\u003c/em\u003e-values.\u003c/p\u003e \u003cp\u003eThe intercept term in MR-Egger regression and MR-PRESSO were employed to assess pleiotropy. The absence of pleiotropy was indicated by an MR-Egger regression intercept not significantly different from zero (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and an MR-PRESSO \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eA leave-one-out analysis was performed to assess the sensitivity of the results. This involved sequentially removing each SNP and re-analyzing the remaining SNPs to assess the impact of each SNP on the overall results.\u003c/p\u003e \u003cp\u003eThe heterogeneity of SNPs was assessed using Cochran's Q test. If the \u003cem\u003ep\u003c/em\u003e-value is less than 0.05, the results are considered heterogeneous. I-squared (I\u0026sup2;) is an alternative statistical measure of heterogeneity, representing the proportion of total variability attributable to heterogeneity. The range of I\u0026sup2; values extends from 0\u0026ndash;100%. An I\u0026sup2; value exceeding 50% suggests the presence of heterogeneity in the results derived from the IVW regression.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using the TwoSample MR package in R version 4.1.0.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a protein-protein interaction network\u003c/h2\u003e \u003cp\u003eIn this study, based on the positive results of MR analysis, we constructed the protein-protein interaction (PPI) network of potential target proteins using the STRING database. The confidence score was set at 0.4 as the minimum interaction score required, and the species was set as Homo sapiens\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Additionally, the PPI network was visualized using Cytoscape version 3.6.0\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, and the topological properties of the network nodes and edges were calculated, including degree, closeness centrality, betweenness centrality, and average shortest path length. Nodes corresponding to targets that simultaneously meet the following criteria were identified as the core targets of Atrazine-induced breast cancer: Betweenness centrality\u0026thinsp;\u0026gt;\u0026thinsp;median value, Closeness centrality\u0026thinsp;\u0026gt;\u0026thinsp;median value, Average shortest path length\u0026thinsp;\u0026lt;\u0026thinsp;median value, and Degree\u0026thinsp;\u0026gt;\u0026thinsp;twice the median value.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGO and KEGG pathway enrichment analyses\u003c/h3\u003e\n\u003cp\u003eGene Ontology (GO) was employed to annotate the 38 functional core genes, especially in terms of molecular function (MF), biological process (BP), and cellular component (CC). We performed GO enrichment analysis and visualized the top ten significantly enriched terms according to their enrichment scores\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Additionally, we conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and visualized the top ten significantly enriched pathways based on their count values. The GO (BP/CC/MF) and KEGG enrichment analyses were performed using the R package \u0026lsquo;clusterProfiler\u0026rsquo; with a significance threshold of \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eMolecular docking for Atrazine and core targets\u003c/h3\u003e\n\u003cp\u003eThe molecular docking approach (Autodock Vina version 1.2.2)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e was employed to analyze the binding affinity and interaction patterns between Atrazine and the core targets. Molecular docking was performed between Atrazine and the proteins encoded by the top five prioritized target genes. The crystal structure of Atrazine was acquired from the PubChem Compound Database (\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), while the structures of candidate proteins were obtained from the Protein Data Bank (PDB, \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) or the AlphaFold Protein Structure Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.ebi.ac.uk/\u003c/span\u003e\u003cspan address=\"https://alphafold.ebi.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, all water molecules were removed from the proteins and Atrazine files, and polar hydrogen atoms were added. The center of the Grid Box was positioned to encompass all domains of the protein and allowed unrestricted molecular movement. The results of the molecular docking were visualized using Discovery Studio 2024 Client.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Atrazine affected breast cancer targets\u003c/h2\u003e \u003cp\u003eWe identified 2360 Atrazine targets using the CTD, STITCH, and SwissTargetPrediction databases, and determined 8203 targets highly associated with breast cancer through the GeneCards and OMIM databases. By intersecting the targets of Atrazine and breast cancer, we obtained 1267 common targets, which were considered as the potential targets for Atrazine-affected breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCausal associations between potential targets and breast cancer via Mendelian randomization analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eSNP selection\u003c/h2\u003e \u003cp\u003eA total of 1267 potential targets were identified in the IEU OpenGWAS project database, and the expression quantitative trait loci (eQTLs) for 1047 genes were ultimately obtained. The data were derived exclusively from individuals of European ancestry, with a summary of the information provided in \u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. Following the selection process of the aforementioned criteria, a total of 30100 single-nucleotide polymorphisms (SNPs) associated with breast cancer were identified from the 1047 eQTLs data of the potential targets (genome-wide statistical signifcance threshold, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, \u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe F-statistics of the instrumental variables (IVs) were largely\u0026thinsp;\u0026gt;\u0026thinsp;10, indicating that weak IVs bias was excluded. After removing palindromic SNPs and outlier SNPs using the MR-PRESSO test and MR-Egger regression, the absence of horizontal pleiotropy in the IVs was confirmed (both MR-PRESSO \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and MR-Egger regression \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Heterogeneity among the SNPs was assessed using Cochran's Q test and I\u003csup\u003e2\u003c/sup\u003e statistic, and the results showed no significant heterogeneity (Cochran's Q test p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50%).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCausal effects of potential targets on the development of breast cancer\u003c/h2\u003e \u003cp\u003eThrough Mendelian randomization analysis of the 1047 potential targets and breast cancer, we identified 198 targets with statistically significant associations with breast cancer. After excluding results that were not robust due to heterogeneity and pleiotropy, and following correction for multiple testing using the false discovery rate (FDR), we ultimately identified 164 targets with causal associations with breast cancer (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). For example, the MR results indicated that TP53BP1 (IVW OR\u0026thinsp;=\u0026thinsp;1.456, 95% CI: 1.218\u0026ndash;1.740, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001); BZW1 (IVW OR\u0026thinsp;=\u0026thinsp;1.318, 95% CI: 1.188\u0026ndash;1.463, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001); IDH1 (IVW OR\u0026thinsp;=\u0026thinsp;1.147, 95% CI: 1.091\u0026ndash;1.205, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001); ATG2B (IVW OR\u0026thinsp;=\u0026thinsp;1.134, 95% CI: 1.051\u0026ndash;1.224, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, FDR\u0026thinsp;=\u0026thinsp;0.008); and TLE3 (IVW OR\u0026thinsp;=\u0026thinsp;1.264, 95% CI: 1.061\u0026ndash;1.505, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, FDR\u0026thinsp;=\u0026thinsp;0.040) were risk factors for breast cancer. The volcano plot of the causal effects of identified potential targets on breast cancer is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction network and acquisition of core genes\u003c/h2\u003e \u003cp\u003eWe constructed a PPI network, which comprises 144 nodes, 543 edges, and an average node degree of 6.62. The interactions among the 144 genes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Through analysis using Cytoscape, we identified 38 core targets associated with Atrazine-induced breast cancer (\u003cb\u003eSupplemental, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). These targets highlight their potential roles in the disease pathway and are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which shows the interactions among them. Notably, based on the ranking of degree values, the top five core targets are AKT1, CASP3, HSPA4, CCND1, and MAPK3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunction and pathway enrichment analysis of core targets\u003c/h2\u003e \u003cp\u003eTo investigate the functional characteristics and biological relevance of the 38 identified core targets, we performed GO and KEGG enrichment analyses. The results of the GO enrichment analysis indicated that the biological processes (BP) associated with the 38 core targets were mainly related to the intrinsic apoptotic signaling pathway and response to ionizing radiation. The cellular components (CC) mainly implicated were the protein kinase complex and transferase complex involved in transferring phosphorus-containing groups. The molecular functions (MF) primarily involved were cyclin-dependent protein serine/threonine kinase regulator activity and protein serine/threonine kinase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe KEGG enrichment analysis indicated that the top three signaling pathways associated with the 38 core targets were cellular senescence, human T-cell leukemia virus 1 infection, and small cell lung cancer pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking for Atrazine and core target proteins of breast cancer\u003c/h2\u003e \u003cp\u003eWe obtained the binding sites and interactions between Atrazine and the proteins encoded by the top five core target genes in the PPI network. The binding energy for each interaction was generated. These five core target proteins all exhibited high affinity with Atrazine (binding energies \u0026lt; -5 kcal/moL) (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Atrazine formed connections with the target proteins through visible hydrogen bonds and strong electrostatic interactions. The docking results for the five proteins with Atrazine are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we innovatively and synergistically integrated multiple biomedical databases with differential gene expression datasets using network evaluation tools to elucidate the underlying mechanisms linking Atrazine exposure to breast cancer incidence. We performed a Mendelian randomization analysis, which revealed 164 potential targets closely implicated in Atrazine-triggered breast cancer development. We established a multi-layered interaction network through network toxicological analysis, through which we ultimately identified 38 potential targets of Atrazine-induced breast cancer. The biological processes of these targets are mainly enriched in the intrinsic apoptotic signaling pathway and the response to ionizing radiation. Their molecular functions primarily indicate cyclin-dependent protein serine/threonine kinase regulator activity and protein serine/threonine kinase activity. The top three signaling pathways associated with these potential targets are enriched in cellular senescence, human T-cell leukemia virus 1 infection, and small cell lung cancer pathways. Notably, the top five core targets (AKT1, CASP3, HSPA4, CCND1, and MAPK3) exhibit strong binding affinities with Atrazine, indicating that they may play central roles in the pathological mechanisms of Atrazine-induced breast cancer.\u003c/p\u003e \u003cp\u003eAs a widely used herbicide in agriculture, the potential carcinogenic effects of Atrazine have attracted attention in recent years. However, only a handful of studies have investigated its exposure effect on the development of breast cancer. Epidemiological studies revealed that Atrazine may disrupt the endocrine system due to its estrogenic properties, thereby causing breast cancer\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. However, this hypothesis has not been fully supported in epidemiological studies, highlighting the necessity of mechanism research. At the cellular and molecular level, multiple experiments have revealed the multi-pathway cancer-promoting potential of Atrazine. Jessica et al. reported that Atrazine showed a trend toward increased cell viability at a concentration of 10 nM. This proliferative effect may be related to the presence of ER\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Jean-Paul et al. revealed that Atrazine induced oxidative stress, DNA damage, and cell morphological changes by down-regulating key functional proteins in MCF-7 cells, which may promote the pathological process\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Similarly, Sasikala et al. demonstrated that Atrazine caused higher DNA cleavage and increased the risk for developing breast cancer\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Notably, Mengqi Wang et al. revealed that Atrazine exposure significantly increased 4T1 cell proliferation and tumor volume via upregulation of MMP2, MMP7, and MMP9. Immunologically, Atrazine decreased the proportions of CD4\u0026thinsp;+\u0026thinsp;and CD3\u0026thinsp;+\u0026thinsp;lymphocytes in the spleen and lymph nodes and lowered the CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio. Furthermore, tumor-infiltrating immune cells, including CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and NK cells were diminished, while regulatory T cells (Tregs) increased. Additionally, elevated IL-4 levels and reduced IFN-γ and TNF-α were observed in both serum and the tumor microenvironment, suggesting that Atrazine promotes tumor progression by suppressing anti-tumor immunity and enhancing immune escape mechanisms\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In addition, several studies reported that Atrazine resulted in reproductive dysfunction and increased BC risk, especially by inhibiting cAMP-specific phosphodiesterase-4 via the hypothalamic-pituitary-gonadal (HPG) axis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Some animal studies have shown that long-term exposure to Atrazine increased the incidence of BC\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Although existing evidence shows that Atrazine still has significant biological activity at low concentrations relevant to the environment, there are still many gaps in the cross-regulatory network of its core targets and signaling pathways. In summary, Atrazine may affect breast cancer progression through multiple mechanisms such as endocrine disruption, genomic toxicity, immunosuppression and multi-system interaction, but the specific molecular pathways have not been fully elucidated. In the future, it is necessary to integrate exposure omics, multi-omics analysis and cross-species models to systematically reveal dose-effect relationships.\u003c/p\u003e \u003cp\u003eRegarding AKT1, whose mutations are found in approximately 1% of all cancers, these mutations induced continuous activation of AKT signaling in cancer cells\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The human AKT family kinases consist of AKT1, AKT2, and AKT3. Studies have revealed that AKT1 is crucial for driving tumorigenesis, development, and metastatic potential in breast cancer\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Experimental studies have demonstrated that targeted silencing of AKT1 in breast cancer models effectively suppresses tumorigenesis by impairing cell cycle progression and activating apoptotic pathways\u003csup\u003e[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Similarly, overexpression and/or activation of AKT1 contributes to inhibiting pro-apoptotic signals and activating survival signals in mammary epithelial cells\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Notably, Bijesh George revealed an unexpected role of AKT1. AKT1 exhibited the ability for bidirectional gene expression regulation. AKT1 not only positively regulated cancer-promoting genes (such as TFF1, EEF2, and SCD), but also inhibited the expression of specific genes (such as TMEM213, VSIG1, and CYP4F8). These suppressed genes are normally downregulated in breast cancers with high AKT1 expression and are associated with patient prognosis. AKT1 knockdown or inhibition significantly altered the splicing patterns\u003csup\u003e[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, which may influence tumor phenotypes by regulating post-transcriptional modifications. These splicing events AKT1 plays a central role in metabolic reprogramming, cell proliferation, and heterogeneity of breast cancer by regulating gene expression and splicing variation. Its unique inhibitory transcriptional regulation and splicing function provide a new direction for the development of AKT1-specific therapies and emphasize the clinical value of targeting splicing mechanisms. Our MR results indicated that AKT1 (IVW OR\u0026thinsp;=\u0026thinsp;0.933, 95% CI: 0.906\u0026ndash;0.961, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was a protective factor. This inconsistent result may be partially explained by the unique function of AKT1, as mentioned above, which suggest that further in-depth experimental studies are needed.\u003c/p\u003e \u003cp\u003eCASP3 is one of the proteases in the caspase family that plays a crucial role in maintaining cellular homeostasis through the regulation of cell death and inflammatory responses. Several studies have indicated that caspase-3 levels in breast cancer (BC) cells are reduced compared to those in normal cells\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Yang et al. demonstrated that caspase-3 can trigger apoptosis, enhance the cleavage of cell death-related substrates, and induce DNA fragmentation. They further showed that introducing caspase-3 cDNA into cells significantly increased their sensitivity to apoptosis induced by chemotherapeutic agents such as doxorubicin and etoposide\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Similarly, reconstitution of caspase-3 in MCF-7 BC cells enhanced their responsiveness to these drugs, implicating caspase-3 deficiency as a potential contributor to chemoresistance\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Consistent with these findings, Devarajan et al. further confirmed that restoring caspase-3 expression in caspase-3-deficient BC cells led to increased sensitivity to doxorubicin and etoposide\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Our MR results indicated that CASP3 was a risk factor for BC (IVW OR\u0026thinsp;=\u0026thinsp;1.032, 95% CI: 1.011\u0026ndash;1.054, p\u0026thinsp;=\u0026thinsp;0.003, FDR\u0026thinsp;=\u0026thinsp;0.017). This finding is consistent with those of previous studies. Together, these studies support the notion that caspase-3 deficiency may serve as a mechanism underlying resistance to chemotherapy in breast cancer patients. Caspase-3 holds potential as a valuable target for cancer treatment strategies.\u003c/p\u003e \u003cp\u003eHSPA4 belongs to HSP70 family and is a tumor-associated membrane protein. Heat shock proteins (HSPs) are frequently overexpressed in numerous malignancies and are associated with the progression of various tumors\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. HSPA4 has been proposed as a potential tumor antigen in lung and liver cancers\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. The study by Yan Gu demonstrated that pathogenic anti-HSPA4 IgG binds to membrane-associated glycosylated HSPA4, thereby activating the CXCR4/SDF1α axis through the NF-κB signaling pathway. This interaction ultimately facilitates lymph node metastasis in breast cancer\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. They also revealed that elevated HSPA4 levels were robustly correlated with the occurrence of axillary lymph node metastasis in invasive ductal carcinoma\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. High expression of HSPA4 in BC was not only associated with reduced progression-free survival and overall survival\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e; moreover, elevated levels of HSPA4 and anti-HSPA4 IgG in BC were correlated with lymph node metastasis in clinical cohorts\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Consistent with these findings, our MR results showed that HSPA4 (IVW OR\u0026thinsp;=\u0026thinsp;1.040, 95% CI: 1.014\u0026ndash;1.067, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, FDR\u0026thinsp;=\u0026thinsp;0.015) was a risk factor for breast cancer. Collectively, the characterization of tumor-associated antigens and their corresponding pathogenic antibodies highlights novel diagnostic and therapeutic avenues for cancer, potentially leading to significant improvements in cancer patient survival outcomes.\u003c/p\u003e \u003cp\u003eRegarding CCND1, which is located on the long arm of chromosome 11, it encodes the cyclin D1 protein\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have demonstrated that Cyclin D1 (encoded by the CCND1 gene) can modulate steroid hormone receptor signaling. Specifically, it can activate the oestrogen receptor (ER) and suppress the androgen receptor activity in breast epithelial cells\u003csup\u003e[\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. CCND1 is overexpressed in approximately 50% of breast cancers\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e, while gene amplification occurs in 9\u0026ndash;15% of cases\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. Notably, CCND1 amplification has been linked to a higher risk of recurrence\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e and reduced responsiveness to chemotherapy\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. Moreover, it is associated with increased proliferative activity, higher histopathological grades, and a predominance of the Luminal B molecular subtype\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Curtis et al. further identified a high-risk, ER-positive breast cancer subgroup characterized by high CCND1 copy number variations\u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. Similarly, a study by Sarah at al. reported a significant correlation between CCND1 amplification and positive ER status (OR:1.70, 95% CI:1.19\u0026ndash;2.43, p\u0026thinsp;=\u0026thinsp;0.004), as well as Cyclin D1 protein overexpression (OR: 5.64, 95% CI: 2.32\u0026ndash;13.74, p\u0026thinsp;=\u0026thinsp;0.0001). This study also demonstrated that CCND1 amplification was independently associated with shorter recurrence-free survival (RFS) and overall survival (OS) in breast cancer patients, particularly in those receiving endocrine therapy\u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e. Taken together, these findings suggest that CCND1 gene amplification may serve as a prognostic and predictive biomarker in breast cancer, especially in the context of ER-positive tumors and endocrine treatment resistance.\u003c/p\u003e \u003cp\u003eMAPK3 is a key member of the mitogen-activated protein kinase (MAPK) family and is mainly involved in the regulation of biological processes such as cell proliferation, differentiation, survival, apoptosis, and stress response\u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. The MAPK pathway, also referred to as the RAS/RAF/MEK/ERK cascade, transmits extracellular signals from cell surface receptors to nuclear transcription factors, thereby modulating cellular responses\u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e. The activation of the MAPK signaling cascade promotes tumor growth, enhances metastatic potential, and induces neovascularization in malignancies\u003csup\u003e[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e. The MAPK pathway is activated in about 50% of breast cancers\u003csup\u003e[\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. It has been reported that the activation of the MAPK pathway is associated with the repression of ER gene transcription, thereby contributing to endocrine resistance in BC\u003csup\u003e[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e. Other studies have also demonstrated that the MAPK/ERK signaling cascade plays a significant role in both cancer metastasis and the development of drug resistance\u003csup\u003e[\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that inhibition of MAPK pathway oncoproteins (RAF, MEK, and ERK) may represent a potential therapeutic approach to overcome endocrine resistance mediated by MAPK pathway alterations.\u003c/p\u003e \u003cp\u003eIn this study, we identified 38 core targets of Atrazine-induced breast cancer, and KEGG analysis revealed that one of the top three enriched pathways of these core target genes was cellular senescence. Although the cellular senescence pathway has always been a highly complicated phenotype, several studies have elucidated that a population of variant human mammary epithelial cells (HMECs) can escape cellular senescence via methylation-dependent CDKN2A gene silencing\u003csup\u003e[\u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/sup\u003e. Notably, this process shows parallels with the cytogenetic profiles characteristic of preinvasive and early-stage neoplastic transformations in mammary tissue\u003csup\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/sup\u003e. Berman et al. demonstrated that mammary-epithelial cells can escape senescence (M0) via downregulation of the p16 protein. This process leads to the formation of premalignant lesions and, ultimately, results in invasive carcinoma\u003csup\u003e[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/sup\u003e. During the immortalization process of normal human mammary epithelial cells (HMECs), the escape or bypass of senescence is associated with the hypermethylation of the p16 promoter. Additionally, several signaling pathways and proteins have been identified as contributing factors\u003csup\u003e[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/sup\u003e. Upregulation of prostaglandin cyclooxygenase-2 (COX2) has been documented in HMECs that have escaped or bypassed senescence. Moreover, COX2 expression is often observed in regions that exhibit hypermethylation of the CDKN2A gene\u003csup\u003e[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/sup\u003e. As previously described, cellular senescence serves as an early impediment to human carcinogenesis and plays a crucial role in the initial stages of cellular immortalization and neoplastic transformation\u003csup\u003e[\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/sup\u003e. Notably, several studies have elucidated the presence of cellular senescence within the breast cancer microenvironment, specifically in at least three distinct cell populations: near-normal epithelia, fibroblasts, and lymphocytes. These populations play significant roles in cancer development and progression\u003csup\u003e[\u003cspan additionalcitationids=\"CR89 CR90 CR91\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]\u003c/sup\u003e. Collectively, these findings highlight the intricate nature of the cellular senescence phenotype, the challenges associated with detecting it, and its multifaceted impact on both senescent cells and the surrounding microenvironment.\u003c/p\u003e \u003cp\u003eTo further investigate the effects of Atrazine on breast cancer, we conducted molecular docking analysis to explore its interaction with the top five core target proteins. All core target proteins exhibited high affinity for Atrazine, characterized by binding energies less than zero. This suggests that Atrazine can spontaneously bind to each core target protein, thereby facilitating the exploration of the molecular mechanisms underlying its potential role in promoting breast cancer. Although the binding energy data support the hypothesis of target interaction, they need to be further verified by experiments.\u003c/p\u003e \u003cp\u003eThe clinical relevance of targeting these proteins is underscored by recent therapeutic advancements. Particularly compelling evidence emerges from AKT1-targeted therapies: The LOTUS phase II clinical trial demonstrated that combining ipatasertib (an ATP-competitive pan-AKT inhibitor) with paclitaxel significantly improved progression-free survival (PFS) in triple-negative breast cancer (TNBC) patients (6.2 vs 4.9 months; HR 0.60, 95% CI 0.37\u0026ndash;0.98; P\u0026thinsp;=\u0026thinsp;0.037), especially in those carrying the PIK3CA/AKT1/PTEN mutation\u003csup\u003e[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/sup\u003e. Notably, ipatasertib showed an increase in overall survival (OS) at 50% OS events, from 18.4 to 23.1 months (HR 0.62, 95% CI 0.37\u0026ndash;1.05), prompting an ongoing phase III trial (NCT03337724)\u003csup\u003e[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/sup\u003e. Similarly, another PAKT phase II clinical trial showed that capivasertib combined with paclitaxel exerted an advantage in median PFS from 4.2 to 5.9 months (HR 0.75, 95% CI 0.52\u0026ndash;1.08; one-sided P\u0026thinsp;=\u0026thinsp;0.06). In addition, a median OS benefit was observed from 12.6 to 19.1 months in the capivasertib group (HR 0.64, 95% CI 0.40\u0026ndash;1.01; one-sided P\u0026thinsp;=\u0026thinsp;0.02)\u003csup\u003e[\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]\u003c/sup\u003e. Regarding CASP3, currently no clinical drugs directly target CASP3. However, drugs that indirectly activate CASP3 by inducing apoptosis via the mitochondrial pathway show promising clinical application\u003csup\u003e[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]\u003c/sup\u003e. For example, the highly selective BCL-2 inhibitor venetoclax has already been approved in the United States for the treatment of chronic lymphocytic leukaemia with 17p deletion. Moreover, an exploratory analysis of a randomized phase II clinical trial (VERONICA) in ER-positive metastatic breast cancer indicated a slightly improved clinical benefit rate (CBR) and PFS with venetoclax in tumors that exhibited strong BCL2 expression (IHC 3+), a BCL2/BCLXL Histoscore ratio\u0026thinsp;\u0026ge;\u0026thinsp;1, or PIK3CA-wild-type status (particularly those with high BCL2 expression)\u003csup\u003e[\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]\u003c/sup\u003e. In addition, MAPK3 (also known as ERK1), it has a high structural similarity (approximately 85% homology) with MAPK1 (ERK2). Due to this similarity, there are currently no approved drugs that specifically target MAPK3. Research has predominantly focused on inhibitors targeting both ERK1 and ERK2. Robert and other studies have reported that Ulixertinib, MK-8353, and GDC-0994 are effective and specific inhibitors of ERK1/2 and are in early clinical trials for the treatment of various advanced or metastatic solid tumors\u003csup\u003e[\u003cspan additionalcitationids=\"CR99 CR100\" citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]\u003c/sup\u003e. Taken together, our findings suggest that AKT1, CASP3 and MAPK3 are promising targets for the treatment of breast cancer and hold significant potential for broader applications that need further in-depth exploration.\u003c/p\u003e \u003cp\u003eOur study elucidates the adverse health implications of Atrazine contamination through advanced epidemiological and toxicological approaches. By employing MR analysis integrated with network toxicology, we established robust evidence supporting the herbicide's pathogenic role in breast carcinogenesis. To enhance the reliability of our MR findings, we carefully eliminated the impact of potential confounders. Additionally, by elucidating the specific molecular pathways through which Atrazine may contribute to breast cancer onset, our findings underscore the critical importance of reevaluating exposure thresholds for agricultural chemicals in environmental toxicology. The demonstrated oncogenic properties of this widely-used herbicide necessitate urgent interdisciplinary efforts to reassess its biosafety profile and epidemiological impact. This comprehensive investigation not only advances our comprehension of environmental carcinogens but also establishes a novel paradigm for assessing chemical toxicity through integrated omics approaches.\u003c/p\u003e \u003cp\u003eNetwork toxicology combines with molecular docking technology reveal multi-target synergism and is highly efficient. The capacity for identifying and evaluating the toxicological impacts of chemical substances has markedly advanced. However, it has limitations, as it cannot simulate physiological differences observed in animal experiments nor can it model long-term low-dose exposure. Therefore, further in vivo experiments are required to validate the function of the identified targets and to establish long-term low-dose exposure models for more comprehensive exploration.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we have developed a comprehensive analytical model that effectively elucidates the direct causal link between Atrazine and breast cancer, as well as the intricate molecular pathways involved. This work addresses a critical knowledge gap in environmental oncology by providing mechanistic evidence for Atrazine's carcinogenic potential, particularly relevant given its persistence as a pervasive environmental contaminant with documented bioaccumulation in human populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstrumental variables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeQTLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eexpression quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eminor allele frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einverse variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein-protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebiological process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emolecular function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecell component.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003eThe authors declare that they have no competing financial interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZitong Zhao: Conceptualization, Methodology, Data analysis, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Yifan Cai: Methodology, Literature. Chaofan Li: Methodology, Literature. Chong Du: Writing \u0026ndash; review \u0026amp; editing. Shuqun Zhang: Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to acknowledge the support of the Key Laboratory of Integration of Traditional Chinese Medicine and Western Medicine (No.2022-ZXY-SYS-002), the National Natural Science Foundation of China (No.82174164), and the Shaanxi Provincial Fund (No.2024SF-YBXM-515) for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024. 74(3): p. 229-263.\u003c/li\u003e\n\u003cli\u003eBritt, K.L., J. Cuzick, and K.A. Phillips, Key steps for effective breast cancer prevention. Nat Rev Cancer, 2020. 20(8): p. 417-436.\u003c/li\u003e\n\u003cli\u003eColleoni, M., et al., Annual Hazard Rates of Recurrence for Breast Cancer During 24 Years of Follow-Up: Results From the International Breast Cancer Study Group Trials I to V. J Clin Oncol, 2016. 34(9): p. 927-35.\u003c/li\u003e\n\u003cli\u003eOliveira, W.L., et al., Does the atrazine increase animal mortality: Unraveling through a meta-analytic study. Sci Total Environ, 2024. 951: p. 175553.\u003c/li\u003e\n\u003cli\u003eDeng, S., et al., Advances in understanding and mitigating Atrazine\u0026apos;s environmental and health impact: A comprehensive review. J Environ Manage, 2024. 365: p. 121530.\u003c/li\u003e\n\u003cli\u003eHu, Y., et al., Impact of atrazine on soil microbial properties: A meta-analysis. Environ Pollut, 2023. 323: p. 121337.\u003c/li\u003e\n\u003cli\u003eArar, M., et al., Microbial hydrolysis of atrazine in contaminated groundwater. Chemosphere, 2023. 322: p. 138226.\u003c/li\u003e\n\u003cli\u003eChang, J., et al., Toxicological effects, environmental behaviors and remediation technologies of herbicide atrazine in soil and sediment: A comprehensive review. Chemosphere, 2022. 307(Pt 3): p. 136006.\u003c/li\u003e\n\u003cli\u003eZhao, H., et al., Endocrine toxicity of atrazine and its underlying mechanisms. Toxicology, 2024. 505: p. 153846.\u003c/li\u003e\n\u003cli\u003eLu, Y.S., et al., Integrated metabolomics and transcriptomics analysis reveals new biomarkers and mechanistic insights on atrazine exposures in MCF‑7 cells. Ecotoxicol Environ Saf, 2022. 232: p. 113244.\u003c/li\u003e\n\u003cli\u003eYang, Y.F., et al., Accumulated inflammation and fibrosis participate in atrazine induced ovary toxicity in mice. Environ Pollut, 2024. 360: p. 124672.\u003c/li\u003e\n\u003cli\u003eWang, M., et al., Atrazine promotes breast cancer development by suppressing immune function and upregulating MMP expression. Ecotoxicol Environ Saf, 2023. 253: p. 114691.\u003c/li\u003e\n\u003cli\u003eKass, L., A.L. Gomez, and G.A. Altamirano, Relationship between agrochemical compounds and mammary gland development and breast cancer. Mol Cell Endocrinol, 2020. 508: p. 110789.\u003c/li\u003e\n\u003cli\u003ePanis, C., et al., Exposure to Pesticides and Breast Cancer in an Agricultural Region in Brazil. Environ Sci Technol, 2024. 58(24): p. 10470-10481.\u003c/li\u003e\n\u003cli\u003eZhao, Z., et al., An integrated strategy combining network toxicology and feature-based molecular networking for exploring hepatotoxic constituents and mechanism of Epimedii Folium-induced hepatotoxicity in vitro. Food Chem Toxicol, 2023. 176: p. 113785.\u003c/li\u003e\n\u003cli\u003eHuang, S., Analysis of environmental pollutant Bisphenol F elicited prostate injury targets and underlying mechanisms through network toxicology, molecular docking, and multi-level bioinformatics data integration. Toxicology, 2024. 506: p. 153847.\u003c/li\u003e\n\u003cli\u003eHuang, S., Efficient analysis of toxicity and mechanisms of environmental pollutants with network toxicology and molecular docking strategy: Acetyl tributyl citrate as an example. Sci Total Environ, 2023. 905: p. 167904.\u003c/li\u003e\n\u003cli\u003eHe, J., et al., Network toxicological and molecular docking to investigate the mechanisms of toxicity of agricultural chemical Thiabendazole. Chemosphere, 2024. 363: p. 142711.\u003c/li\u003e\n\u003cli\u003eLi, J. and H. Bi, Clarification of the molecular mechanisms underlying glyphosate-induced major depressive disorder: a network toxicology approach. Ann Gen Psychiatry, 2024. 23(1): p. 8.\u003c/li\u003e\n\u003cli\u003eXiao, F., J. Qiu, and Y. Zhao, Exploring the Potential Toxicological Mechanisms of Vine Tea on the Liver Based on Network Toxicology and Transcriptomics. Front Pharmacol, 2022. 13: p. 855926.\u003c/li\u003e\n\u003cli\u003eLin, J., J. Zhou, and Y. Xu, Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain, 2023. 146(8): p. 3364-3372.\u003c/li\u003e\n\u003cli\u003eFerence, B.A., Interpreting the Clinical Implications of Drug-Target Mendelian Randomization Studies. J Am Coll Cardiol, 2022. 80(7): p. 663-665.\u003c/li\u003e\n\u003cli\u003eCao, Y., et al., Identification of potential drug targets for rheumatoid arthritis from genetic insights: a Mendelian randomization study. J Transl Med, 2023. 21(1): p. 616.\u003c/li\u003e\n\u003cli\u003eDavis, A.P., et al., Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res, 2023. 51(D1): p. D1257-d1262.\u003c/li\u003e\n\u003cli\u003eChen, S., et al., Uncovering the mechanism of resveratrol in the treatment of diabetic kidney disease based on network pharmacology, molecular docking, and experimental validation. J Transl Med, 2023. 21(1): p. 380.\u003c/li\u003e\n\u003cli\u003eUniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res, 2023. 51(D1): p. D523-d531.\u003c/li\u003e\n\u003cli\u003eSollis, E., et al., The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res, 2023. 51(D1): p. D977-d985.\u003c/li\u003e\n\u003cli\u003eSun, Y.S., et al., Risk Factors and Preventions of Breast Cancer. Int J Biol Sci, 2017. 13(11): p. 1387-1397.\u003c/li\u003e\n\u003cli\u003eVeronesi, U., et al., Breast cancer. Lancet, 2005. 365(9472): p. 1727-41.\u003c/li\u003e\n\u003cli\u003eSzklarczyk, D., et al., The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res, 2023. 51(D1): p. D638-d646.\u003c/li\u003e\n\u003cli\u003eOtasek, D., et al., Cytoscape Automation: empowering workflow-based network analysis. Genome Biol, 2019. 20(1): p. 185.\u003c/li\u003e\n\u003cli\u003eKersey, A.L., I. Singh, and A.K. Gaharwar, Inorganic ions activate lineage-specific gene regulatory networks. Acta Biomater, 2024. 183: p. 371-386.\u003c/li\u003e\n\u003cli\u003eWan, M.L.Y., V.A. Co, and H. El-Nezami, Endocrine disrupting chemicals and breast cancer: a systematic review of epidemiological studies. Crit Rev Food Sci Nutr, 2022. 62(24): p. 6549-6576.\u003c/li\u003e\n\u003cli\u003eRich, J.D., S.M. Gabriel, and J.R. Schultz-Norton, In vitro effects of herbicides and insecticides on human breast cells. ISRN Toxicol, 2012. 2012: p. 232461.\u003c/li\u003e\n\u003cli\u003eLasserre, J.P., et al., Effects of the endocrine disruptors atrazine and PCB 153 on the protein expression of MCF-7 human cells. J Proteome Res, 2009. 8(12): p. 5485-96.\u003c/li\u003e\n\u003cli\u003eSasikala, S., et al., Predicting the relationship between pesticide genotoxicity and breast cancer risk in South Indian women in in vitro and in vivo experiments. Sci Rep, 2023. 13(1): p. 9712.\u003c/li\u003e\n\u003cli\u003eWirbisky, S.E. and J.L. Freeman, Atrazine Exposure and Reproductive Dysfunction through the Hypothalamus-Pituitary-Gonadal (HPG) Axis. Toxics, 2015. 3(4): p. 414-450.\u003c/li\u003e\n\u003cli\u003eCarpten, J.D., et al., A transforming mutation in the pleckstrin homology domain of AKT1 in cancer. Nature, 2007. 448(7152): p. 439-44.\u003c/li\u003e\n\u003cli\u003eLandgraf, K.E., C. Pilling, and J.J. Falke, Molecular mechanism of an oncogenic mutation that alters membrane targeting: Glu17Lys modifies the PIP lipid specificity of the AKT1 PH domain. Biochemistry, 2008. 47(47): p. 12260-9.\u003c/li\u003e\n\u003cli\u003eDillon, R.L., et al., Akt1 and akt2 play distinct roles in the initiation and metastatic phases of mammary tumor progression. Cancer Res, 2009. 69(12): p. 5057-64.\u003c/li\u003e\n\u003cli\u003eRiggio, M., et al., PI3K/AKT pathway regulates phosphorylation of steroid receptors, hormone independence and tumor differentiation in breast cancer. Carcinogenesis, 2012. 33(3): p. 509-18.\u003c/li\u003e\n\u003cli\u003eHinz, N. and M. J\u0026uuml;cker, Distinct functions of AKT isoforms in breast cancer: a comprehensive review. Cell Commun Signal, 2019. 17(1): p. 154.\u003c/li\u003e\n\u003cli\u003eWatson, K.L. and R.A. Moorehead, Loss of Akt1 or Akt2 delays mammary tumor onset and suppresses tumor growth rate in MTB-IGFIR transgenic mice. BMC Cancer, 2013. 13: p. 375.\u003c/li\u003e\n\u003cli\u003eGargini, R., et al., Cancer stem cell-like phenotype and survival are coordinately regulated by Akt/FoxO/Bim pathway. Stem Cells, 2015. 33(3): p. 646-60.\u003c/li\u003e\n\u003cli\u003eHutchinson, J., et al., Activation of Akt (protein kinase B) in mammary epithelium provides a critical cell survival signal required for tumor progression. Mol Cell Biol, 2001. 21(6): p. 2203-12.\u003c/li\u003e\n\u003cli\u003eGeorge, B., et al., AKT1 Transcriptomic Landscape in Breast Cancer Cells. Cells, 2022. 11(15).\u003c/li\u003e\n\u003cli\u003eWhitsett, T.G., et al., Molecular determinants of lung cancer metastasis to the central nervous system. Transl Lung Cancer Res, 2013. 2(4): p. 273-83.\u003c/li\u003e\n\u003cli\u003eSpeers, C., et al., Identification of novel kinase targets for the treatment of estrogen receptor-negative breast cancer. Clin Cancer Res, 2009. 15(20): p. 6327-40.\u003c/li\u003e\n\u003cli\u003eKr\u0026uuml;ger, S., T. Fahrenkrog, and H. M\u0026uuml;ller, Proliferative and apoptotic activity in lobular breast carcinoma. Int J Mol Med, 1999. 4(2): p. 171-4.\u003c/li\u003e\n\u003cli\u003eYang, X.H., et al., Reconstitution of caspase 3 sensitizes MCF-7 breast cancer cells to doxorubicin- and etoposide-induced apoptosis. Cancer Res, 2001. 61(1): p. 348-54.\u003c/li\u003e\n\u003cli\u003eFulda, S., et al., Molecular ordering of apoptosis induced by anticancer drugs in neuroblastoma cells. Cancer Res, 1998. 58(19): p. 4453-60.\u003c/li\u003e\n\u003cli\u003eBlanc, C., et al., Caspase-3 is essential for procaspase-9 processing and cisplatin-induced apoptosis of MCF-7 breast cancer cells. Cancer Res, 2000. 60(16): p. 4386-90.\u003c/li\u003e\n\u003cli\u003eDevarajan, E., et al., Down-regulation of caspase 3 in breast cancer: a possible mechanism for chemoresistance. Oncogene, 2002. 21(57): p. 8843-51.\u003c/li\u003e\n\u003cli\u003eSrivastava, P.K., M.K. Callahan, and M.M. Mauri, Treating human cancers with heat shock protein-peptide complexes: the road ahead. Expert Opin Biol Ther, 2009. 9(2): p. 179-86.\u003c/li\u003e\n\u003cli\u003eGotoh, K., et al., Apg-2 has a chaperone-like activity similar to Hsp110 and is overexpressed in hepatocellular carcinomas. FEBS Lett, 2004. 560(1-3): p. 19-24.\u003c/li\u003e\n\u003cli\u003eGu, Y., et al., Tumor-educated B cells selectively promote breast cancer lymph node metastasis by HSPA4-targeting IgG. Nat Med, 2019. 25(2): p. 312-322.\u003c/li\u003e\n\u003cli\u003eTakeshita, T., et al., Transcriptomic and functional pathway features were associated with survival after pathological complete response to neoadjuvant chemotherapy in breast cancer. Am J Cancer Res, 2020. 10(8): p. 2555-2569.\u003c/li\u003e\n\u003cli\u003eValla, M., et al., CCND1 Amplification in Breast Cancer -associations With Proliferation, Histopathological Grade, Molecular Subtype and Prognosis. J Mammary Gland Biol Neoplasia, 2022. 27(1): p. 67-77.\u003c/li\u003e\n\u003cli\u003eNeuman, E., et al., Cyclin D1 stimulation of estrogen receptor transcriptional activity independent of cdk4. Mol Cell Biol, 1997. 17(9): p. 5338-47.\u003c/li\u003e\n\u003cli\u003eZwijsen, R.M., et al., CDK-independent activation of estrogen receptor by cyclin D1. Cell, 1997. 88(3): p. 405-15.\u003c/li\u003e\n\u003cli\u003ePetre-Draviam, C.E., et al., Specificity of cyclin D1 for androgen receptor regulation. Cancer Res, 2003. 63(16): p. 4903-13.\u003c/li\u003e\n\u003cli\u003eOrtiz, A.B., et al., Prognostic significance of cyclin D1 protein expression and gene amplification in invasive breast carcinoma. PLoS One, 2017. 12(11): p. e0188068.\u003c/li\u003e\n\u003cli\u003eBartkova, J., et al., Cyclin D1 protein expression and function in human breast cancer. Int J Cancer, 1994. 57(3): p. 353-61.\u003c/li\u003e\n\u003cli\u003eYang, L., et al., Somatic alterations of TP53, ERBB2, PIK3CA and CCND1 are associated with chemosensitivity for breast cancers. Cancer Sci, 2019. 110(4): p. 1389-1400.\u003c/li\u003e\n\u003cli\u003eLundgren, K., et al., Effects of cyclin D1 gene amplification and protein expression on time to recurrence in postmenopausal breast cancer patients treated with anastrozole or tamoxifen: a TransATAC study. Breast Cancer Res, 2012. 14(2): p. R57.\u003c/li\u003e\n\u003cli\u003eBi\u0026egrave;che, I., et al., Prognostic value of CCND1 gene status in sporadic breast tumours, as determined by real-time quantitative PCR assays. Br J Cancer, 2002. 86(4): p. 580-6.\u003c/li\u003e\n\u003cli\u003eCurtis, C., et al., The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 2012. 486(7403): p. 346-52.\u003c/li\u003e\n\u003cli\u003eJeffreys, S.A., et al., Prognostic and Predictive Value of CCND1/Cyclin D1 Amplification in Breast Cancer With a Focus on Postmenopausal Patients: A Systematic Review and Meta-Analysis. Front Endocrinol (Lausanne), 2022. 13: p. 895729.\u003c/li\u003e\n\u003cli\u003eYue, J. and J.M. L\u0026oacute;pez, Understanding MAPK Signaling Pathways in Apoptosis. Int J Mol Sci, 2020. 21(7).\u003c/li\u003e\n\u003cli\u003eYang, Z., et al., The signaling pathways and targets of traditional Chinese medicine and natural medicine in triple-negative breast cancer. J Ethnopharmacol, 2021. 264: p. 113249.\u003c/li\u003e\n\u003cli\u003eSaini, K.S., et al., Targeting the PI3K/AKT/mTOR and Raf/MEK/ERK pathways in the treatment of breast cancer. Cancer Treat Rev, 2013. 39(8): p. 935-46.\u003c/li\u003e\n\u003cli\u003eRaheem, F., et al., Metastatic ER+ Breast Cancer: Mechanisms of Resistance and Future Therapeutic Approaches. Int J Mol Sci, 2023. 24(22).\u003c/li\u003e\n\u003cli\u003eSivaraman, V.S., et al., Hyperexpression of mitogen-activated protein kinase in human breast cancer. J Clin Invest, 1997. 99(7): p. 1478-83.\u003c/li\u003e\n\u003cli\u003evon Lintig, F.C., et al., Ras activation in human breast cancer. Breast Cancer Res Treat, 2000. 62(1): p. 51-62.\u003c/li\u003e\n\u003cli\u003eMueller, H., et al., Potential prognostic value of mitogen-activated protein kinase activity for disease-free survival of primary breast cancer patients. Int J Cancer, 2000. 89(4): p. 384-8.\u003c/li\u003e\n\u003cli\u003eStoner, M., et al., Hypoxia induces proteasome-dependent degradation of estrogen receptor alpha in ZR-75 breast cancer cells. Mol Endocrinol, 2002. 16(10): p. 2231-42.\u003c/li\u003e\n\u003cli\u003ePereira, S.S., et al., MAPK/ERK pathway inhibition is a promising treatment target for adrenocortical tumors. J Cell Biochem, 2019. 120(1): p. 894-906.\u003c/li\u003e\n\u003cli\u003eLi, F., et al., CD147 promotes breast cancer migration and invasion by inducing epithelial-mesenchymal transition via the MAPK/ERK signaling pathway. BMC Cancer, 2023. 23(1): p. 1214.\u003c/li\u003e\n\u003cli\u003eXu, T., et al., Upregulation of CD147 promotes cell invasion, epithelial-to-mesenchymal transition and activates MAPK/ERK signaling pathway in colorectal cancer. Int J Clin Exp Pathol, 2014. 7(11): p. 7432-41.\u003c/li\u003e\n\u003cli\u003eBrenner, A.J. and C.M. Aldaz, Chromosome 9p allelic loss and p16/CDKN2 in breast cancer and evidence of p16 inactivation in immortal breast epithelial cells. Cancer Res, 1995. 55(13): p. 2892-5.\u003c/li\u003e\n\u003cli\u003eBrenner, A.J., M.R. Stampfer, and C.M. Aldaz, Increased p16 expression with first senescence arrest in human mammary epithelial cells and extended growth capacity with p16 inactivation. Oncogene, 1998. 17(2): p. 199-205.\u003c/li\u003e\n\u003cli\u003eBean, G.R., et al., Morphologically normal-appearing mammary epithelial cells obtained from high-risk women exhibit methylation silencing of INK4a/ARF. Clin Cancer Res, 2007. 13(22 Pt 1): p. 6834-41.\u003c/li\u003e\n\u003cli\u003eRomanov, S.R., et al., Normal human mammary epithelial cells spontaneously escape senescence and acquire genomic changes. Nature, 2001. 409(6820): p. 633-7.\u003c/li\u003e\n\u003cli\u003eTlsty, T.D., et al., Loss of chromosomal integrity in human mammary epithelial cells subsequent to escape from senescence. J Mammary Gland Biol Neoplasia, 2001. 6(2): p. 235-43.\u003c/li\u003e\n\u003cli\u003eBerman, H.K., M.L. Gauthier, and T.D. Tlsty, Premalignant breast neoplasia: a paradigm of interlesional and intralesional molecular heterogeneity and its biological and clinical ramifications. Cancer Prev Res (Phila), 2010. 3(5): p. 579-87.\u003c/li\u003e\n\u003cli\u003eSirinian, C., et al., Cellular Senescence in Normal Mammary Gland and Breast Cancer. Implications for Cancer Therapy. Genes (Basel), 2022. 13(6).\u003c/li\u003e\n\u003cli\u003eBartkova, J., et al., Oncogene-induced senescence is part of the tumorigenesis barrier imposed by DNA damage checkpoints. Nature, 2006. 444(7119): p. 633-7.\u003c/li\u003e\n\u003cli\u003eKrtolica, A. and J. Campisi, Cancer and aging: a model for the cancer promoting effects of the aging stroma. Int J Biochem Cell Biol, 2002. 34(11): p. 1401-14.\u003c/li\u003e\n\u003cli\u003eHaviv, I., et al., Origin of carcinoma associated fibroblasts. Cell Cycle, 2009. 8(4): p. 589-95.\u003c/li\u003e\n\u003cli\u003eYe, J., et al., Tumor-derived \u0026gamma;\u0026delta; regulatory T cells suppress innate and adaptive immunity through the induction of immunosenescence. J Immunol, 2013. 190(5): p. 2403-14.\u003c/li\u003e\n\u003cli\u003eSolinas, C., et al., Tumor-infiltrating lymphocytes in breast cancer according to tumor subtype: Current state of the art. Breast, 2017. 35: p. 142-150.\u003c/li\u003e\n\u003cli\u003eMartins, D. and F. Schmitt, Microenvironment in breast tumorigenesis: Friend or foe? Histol Histopathol, 2019. 34(1): p. 13-24.\u003c/li\u003e\n\u003cli\u003eKim, S.B., et al., Ipatasertib plus paclitaxel versus placebo plus paclitaxel as first-line therapy for metastatic triple-negative breast cancer (LOTUS): a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Oncol, 2017. 18(10): p. 1360-1372.\u003c/li\u003e\n\u003cli\u003ePascual, J. and N.C. Turner, Targeting the PI3-kinase pathway in triple-negative breast cancer. Ann Oncol, 2019. 30(7): p. 1051-1060.\u003c/li\u003e\n\u003cli\u003eSchmid, P., et al., Capivasertib Plus Paclitaxel Versus Placebo Plus Paclitaxel As First-Line Therapy for Metastatic Triple-Negative Breast Cancer: The PAKT Trial. J Clin Oncol, 2020. 38(5): p. 423-433.\u003c/li\u003e\n\u003cli\u003eAshkenazi, A., et al., From basic apoptosis discoveries to advanced selective BCL-2 family inhibitors. Nat Rev Drug Discov, 2017. 16(4): p. 273-284.\u003c/li\u003e\n\u003cli\u003eLindeman, G.J., et al., VERONICA: Randomized Phase II Study of Fulvestrant and Venetoclax in ER-Positive Metastatic Breast Cancer Post-CDK4/6 Inhibitors - Efficacy, Safety, and Biomarker Results. Clin Cancer Res, 2022. 28(15): p. 3256-3267.\u003c/li\u003e\n\u003cli\u003eRoskoski, R., Jr., Targeting ERK1/2 protein-serine/threonine kinases in human cancers. Pharmacol Res, 2019. 142: p. 151-168.\u003c/li\u003e\n\u003cli\u003eSullivan, R.J., et al., First-in-Class ERK1/2 Inhibitor Ulixertinib (BVD-523) in Patients with MAPK Mutant Advanced Solid Tumors: Results of a Phase I Dose-Escalation and Expansion Study. Cancer Discov, 2018. 8(2): p. 184-195.\u003c/li\u003e\n\u003cli\u003eLakhani, N.J., et al., A phase 1b study of the ERK inhibitor MK-8353 plus pembrolizumab in patients with advanced solid tumors. Invest New Drugs, 2024. 42(5): p. 581-589.\u003c/li\u003e\n\u003cli\u003eWeekes, C., et al., A Phase Ib Study to Evaluate the MEK Inhibitor Cobimetinib in Combination with the ERK1/2 Inhibitor GDC-0994 in Patients with Advanced Solid Tumors. Oncologist, 2020. 25(10): p. 833-e1438.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1-2 are available in the Supplementary Files section.\u003c/p\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":"Atrazine, breast cancer, Mendelian randomization, network toxicology, molecular docking, causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-6806548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6806548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer (BC) is the leading cause of cancer-related mortality in women. Atrazine, a widely used herbicide, is increasingly recognized as an environmental pollutant due to bioaccumulation. In this study, we explored the mechanisms by which Atrazine exposure contributed to the occurrence and development of BC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe integrated common targets of Atrazine in BC through multiple databases (e.g. PubChem, CTD, GeneCards, OMIM). The causal relationship between Atrazine exposure and BC was established via Mendelian randomization analysis. The protein-protein interaction (PPI) network of these targets was constructed using STRING database, with core targets analyzed via Cytoscape. GO and KEGG enrichment analyses were performed using the R package. Molecular docking simulations assessed Atrazine\u0026rsquo;s binding affinity to core targets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 1267 potential targets for Atrazine-induced BC. Following single nucleotide polymorphism (SNP) - based selection criteria, SNPs from 1047 potential targets were utilized as instrumental variables, narrowing to 164 causally associated targets. PPI network analysis refined these to 38 core targets. KEGG enrichment highlighted the top three signaling pathways: cellular senescence pathway, human T-cell leukemia virus 1 infection, and small cell lung cancer. Molecular docking revealed strong binding affinities between Atrazine and these core targets (AKT1, CASP3, HSPA4, CCND1, and MAPK3).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAtrazine exposure is linked to BC via cellular senescence, HTLV-1 infection, and small cell lung cancer pathways, with AKT1, CASP3, HSPA4, CCND1, and MAPK3 as key targets. This study delineated a molecular framework for Atrazine-induced BC and a method to assess pollutants' toxicological effects.\u003c/p\u003e","manuscriptTitle":"Integrating Mendelian randomization and network toxicology to elucidate the causal role and mechanisms of Atrazine in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-27 11:00:10","doi":"10.21203/rs.3.rs-6806548/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":"6cd71e1f-26c6-4acc-948e-bc25117a4a4e","owner":[],"postedDate":"June 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-07T05:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-27 11:00:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6806548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6806548","identity":"rs-6806548","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}