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. 2025 Jun 13;16:1084. doi: 10.1007/s12672-025-02884-7

A network toxicology approach to decipher paraben-induced molecular dysregulation in breast cancer pathogenesis

Wen Zhang 1, Rui Xiang 2, Wang Gu 2, Qiang Zhang 2, Lei Liu 2, Chenglin Wang 3, Muhu Chen 3, Yingchun Hu 3, Guihong Chen 3,✉
PMCID: PMC12165941  PMID: 40512387

Abstract

Paraben, extensively utilized as preservatives in cosmetics, pharmaceuticals, industrial products, and food due to its antimicrobial properties, represent pervasive environmental contaminants capable of bioaccumulation through dietary, dermal, and respiratory exposure, potentially leading to diseases including endocrine disruption, skin allergies, and breast cancer. As the endocrine-disrupting chemical (EDC) with estrogenic activity, paraben bind estrogen receptors (ERs), potentially disrupting hormonal homeostasis and increasing breast cancer risk. However, the molecular mechanisms linking paraben to breast carcinogenesis remain poorly defined. This study integrates network toxicology and molecular docking to systematically elucidate paraben-induced dysregulation in breast cancer pathogenesis. Paraben structures (2D/3D, SMILES) were retrieved from PubChem. Toxicological profiling employed ProTox and ADMETlab. Paraben-protein interactions were predicted via STITCH and SwissTargetPrediction, while breast cancer-associated targets were curated from GeneCards, OMIM, and TTD databases. The action targets of paraben were intersected with the breast cancer-related targets. Subsequently, the intersection targets were used to construct the compound regulatory network and perform PPI, GO, and KEGG analyses. The core targets of breast cancer caused by paraben were screened through Cytoscape. Finally, the relationship between the core targets and immune cell infiltration in breast cancer was explored, and molecular docking of paraben and the core targets was carried out. A total of 35 action targets of paraben were obtained from STITCH and SwissTargetPrediction. Meanwhile, 3,413 breast cancer-related targets were retrieved from GeneCards, OMIM, and TTD. After taking the intersection of these two sets of targets, 13 relevant targets were identified. PPI analysis revealed that proteins such as ESR1, ESR2, SERPINE1, and CA2 were located at the center of the network diagram and had close connections with other target proteins. Enrichment analysis demonstrated the molecular functions, biological processes involved, and related pathways of the intersection targets. Three core targets, namely ESR1, ESR2, and SERPINE1, were screened out using Cytoscape. Immune infiltration analysis indicated that in breast cancer, the expression of ESR1 was negatively correlated with the infiltration levels of CD8 + T cells and macrophages, while the expressions of ESR2 and SERPINE1 were positively correlated with the infiltration levels of CD8 + T cells and macrophages. Molecular docking showed that paraben had strong binding activities with ESR1, ESR2, and SERPINE1. Paraben exhibits estrogenic activity and may contribute to breast cancer development by targeting core molecules ESR1, ESR2, and SERPINE1, thereby regulating associated pathways that induce systemic immunosuppression or impede the recruitment of inflammatory responses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-02884-7.

Keywords: Paraben, Network toxicology, Breast cancer, Estrogen, Endocrine system

Introduction

Paraben, belonging to a widely used family of preservatives, have long been regarded as effective antimicrobial additives due to its favorable antibacterial properties, low cost, and chemical stability. It is extensively employed in cosmetics, pharmaceuticals, industrial products, and other commodities. Common parabens include methylparaben (MP), ethylparaben (EP), propylparaben (PP), and butylparaben (BP) [1]. Additionally, paraben may extensively enter the human body through ingestion, inhalation, or dermal exposure [2]. Studies have confirmed its detection in wastewater, rivers, soil, indoor dust, as well as human tissues and body fluids [3]. Since the late 20th century, emerging research has revealed associations between paraben and endocrine disruption, infertility, skin allergies, and various cancers, sparking controversies over its safety and usage. Understanding these risks is crucial for safeguarding human health and maintaining ecological balance [4]. Paraben exhibits weak estrogenic activity and was classified as an EDC. It can mimic endogenous estrogen by binding to estrogen receptors, thereby interfering with normal endocrine functions [5]. Such disruption may affect hormone synthesis, secretion, metabolism, and signal transduction. For instance, plasma levels of MP show negative correlations with glucagon, leptin, and plasminogen activator inhibitor-1 (PAI-1), suggesting its potential obesogenic effects through endocrine homeostasis interference [6]. Paraben also acts as EDC on the hypothalamic-pituitary-thyroid (HPT) axis, disrupting thyroid hormone signaling pathways and impairing physiological processes such as cellular metabolism, growth, and differentiation [7]. For example, high-dose and prolonged exposure to butylparaben induces structural and functional thyroid abnormalities in rats, including thyroid follicular epithelial cell swelling indicative of hypothyroidism [8]. Furthermore, paraben has been detected in breast and breast cancer tissues, where it enhances proliferation of estrogen-sensitive breast cancer cells and accelerate tumor progression [9]. Current evidence indicates that paraben may adversely interfere with endocrine targets associated with breast carcinogenesis, promoting tumor cell growth and proliferation, thereby increasing breast cancer risk [10]. Due to the complex and unclear mechanisms underlying the role of paraben in breast cancer, this study employs network toxicology and molecular docking techniques to elucidate the molecular mechanisms involved. The specific process is shown in Fig. 1.

Fig. 1.

Fig. 1

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Flow chart. Paraben structures were retrieved from PubChem. Toxicological profiling employed ProTox and ADMETlab. Paraben-protein interactions were predicted via STITCH and SwissTargetPrediction, while breast cancer-associated targets were curated from GeneCards, OMIM, and TTD databases. Subsequently, the intersection targets were used to perform PPI, GO, and KEGG analyses. The core targets of breast cancer caused by paraben were screened through Cytoscape. Finally, the relationship between the core targets and immune cell infiltration in breast cancer was explored, and molecular docking of paraben and the core targets was carried out

Methods

Acquisition of the molecular structure of paraben

PubChem is an open repository for chemical structures, biological activities, and biomedical annotations created and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Institutes of Health (NIH) in the United States. PubChem serves as a valuable resource for researchers across various fields such as chemistry, biology, and pharmacology, supporting diverse data science projects including virtual screening, computational toxicology, and drug targeting. PubChem’s information content originates from hundreds of data sources and is organized into several data collections, including Substance, Compound, BioAssay, Gene Protein, Pathway, and Patent [11, 12]. When searching for “Paraben” in the PubChem search bar, one can retrieve the 2D, 3D, and SMILES representations of paraben, and use these structures for subsequent analysis.

Toxicity analysis of paraben

ProTox (https://tox.charite.de/protox3/) is an online in silico platform designed for predicting the toxicity of small molecules. This computational model facilitates the reduction of animal testing in drug development while enhancing the efficiency of toxicological evaluations [13]. The ProTox 3.0 version integrates multiple approaches, including molecular similarity, pharmacophores, fragment propensity, and machine learning models, providing predictions across 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, and adverse outcome pathways [14]. ADMETlab (http://admetmesh.scbdd.com/), a comprehensive online platform, evaluates absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters, physicochemical properties, and pharmaceutical characteristics to accelerate drug discovery [15]. Go to the homepage of ProTox-3.0, select “TOX PREDICTION”, and enter the SMILES structure of paraben into the search box to obtain the toxicity analysis report. Similarly, the SMILES structure of paraben was entered in ADMETlab to obtain toxicity predictions.

Target identification for paraben

STITCH (http://stitch.embl.de) is a multi-source online database that integrates metabolites, drugs, and other chemicals, elucidating chemical-protein interactions through experimental evidence, text mining, and predictive models [16, 17]. SwissTargetPrediction (http://www.swisstargetprediction.ch) predicts potential protein targets of small molecules by leveraging 2D/3D similarity metrics to infer targets based on known ligand interactions [18]. Select “Chemical structure(s)” on the STITCH website, enter the SMILES structure of paraben, click “SEARCH”, and set the parameter to “minimum required interaction score: low confidence (0.150)” to obtain the target of paraben. Similarly, in the SwissTargetPrediction website, select Homo sapiens as the species, enter the SMILES structure of the paraben, and click “Predict targets” to obtain the target of the paraben.

Breast cancer-related target screening

GeneCards (https://www.genecards.org/) consolidates genomic, transcriptomic, proteomic, genetic, and clinical data to explore human gene functions and mechanisms [19, 20]. OMIM (https://omim.org/) catalogs human genes and genetic phenotypes, detailing disease characteristics, molecular mechanisms, and inheritance patterns [21]. TTD (https://db.idrblab.net/ttd/) provides drug-target interactions to support drug development and mechanistic studies [22]. The keyword “breast cancer” was queried in GeneCards, OMIM, and TTD with a relevance score threshold > 10. Retrieved targets were deduplicated and merged to establish a breast cancer-associated target dataset.

Identification of paraben-associated targets in breast cancer

Potential targets involved in the pathogenesis of breast cancer induced by paraben were identified by the “ggvenn” package in R 4.4.2. The overlapping targets were considered potential candidates for further investigation into the mechanisms of breast cancer development.

Construction of compound regulatory network

Cytoscape 3.8.0, an open-source platform for visualizing molecular interaction networks and biological pathways, was utilized to construct a regulatory network integrating paraben, intersection targets, and breast cancer. This platform enables the integration of multi-omics data, including protein-protein interactions, protein-DNA interactions, and gene expression profiles [23].

Protein-protein interaction (PPI) analysis

The STRING Database (https://string-db.org/) is a comprehensive online resource for analyzing and visualizing protein-protein interaction (PPI) networks. It integrates data from multiple public databases (e.g., UniProt, KEGG, NCBI, and Gene Ontology) to generate a comprehensive database of protein interaction networks that demonstrate protein-protein interactions [24]. The screened intersecting targets were input into the STRING platform and a PPI network was constructed to demonstrate the connections between the intersecting targets. Set the parameter to minimum required interaction score: low confidence (0.150) and enable 3D bubble design.

Functional enrichment analysis

Enrichment analysis was performed to identify biologically significant patterns and pathways within the target gene set. Gene Ontology (GO) analysis was conducted to evaluate the enrichment of biological processes (BP), molecular functions (MF), and cellular components (CC), providing insights into the coordinated roles of these genes [25]. KEGG (https://www.kegg.jp), a database for genomic and systemic functional analysis, was used to map pathways associated with the targets [26]. Both GO and KEGG analyses were performed using Metascape (https://metascape.org/gp/#/main/step1). Submit the intersecting target to the input box, select “H. sapiens” as species, and click “Express Analysis” to get the analysis report.

Screening of core targets

CytoHubba, a commonly used plugin of Cytoscape, provides multiple methods for identifying core targets, including Degree, Betweenness Centrality, and Closeness Centrality [27]. Degree values, the simplest and most intuitive method, calculates the number of edges connected to each node and assigns scores to genes accordingly. Targets are then ranked based on their scores, with the top-ranked targets typically considered core genes. In this study, cytoHubba was employed to calculate the degree values of intersecting targets, followed by ranking and screening to identify core genes.

Immune infiltration analysis

Immune infiltration refers to the process by which immune cells migrate from the bloodstream and accumulate in specific tissues or organs under physiological or pathological conditions. Infiltrating immune cells, as critical components of the tumor microenvironment (TME), may serve as vital biomarkers for evaluating patient prognosis and predicting responses to immunotherapy [28]. Using the ggplot2 package in R 4.2.1, this study performed correlation analysis between core targets and immune infiltration matrix data to elucidate how the expression patterns of core genes influence immune cell infiltration in breast cancer. This analysis aimed to uncover potential mechanisms underlying breast cancer pathogenesis. Based on the ssGSEA algorithm provided in R package-GSVA [1.46.0] [29], the markers of 24 immune cells provided in previous articles [30] were used to calculate the immune infiltration of the corresponding cloud data, and the corresponding references can be viewed for the specific 24 immune cells. The statistical method was Spearman.

Molecular docking analysis

The Protein Data Bank (PDB) provides experimentally determined three-dimensional structural data for proteins, nucleic acids, and other macromolecules, facilitating research on protein function, drug design, and disease mechanisms [31]. DOCK, a widely used molecular docking software, predicts interaction patterns between small-molecule ligands and macromolecular receptors [32]. Molecular structures of core targets were retrieved from the PDB database, while the 3D structure of paraben was submitted to the CB-Dock2 online platform (https://cadd.labshare.cn/cb-dock2/php/index.php) to explore spatial binding relationships between paraben and core targets.

Results

Molecular structure of paraben

The 2D and 3D structures of paraben were obtained from the PubChem database (Fig. 2A, B). The SMILES notation for paraben is COC(= O)C1 = CC = C(C = C1)O.

Fig. 2.

Fig. 2

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Molecular structure of paraben. A Two-dimensional (2D) structure of paraben. B Three-dimensional (3D) structure of paraben

Toxicity prediction of paraben

ProTox toxicity analysis revealed the physicochemical properties and toxicity predictions of paraben: Predicted LD50: 2000 mg/kg, Predicted Toxicity Class: 4, Average similarity: 100%, and Prediction accuracy: 100% (Fig. 3A, B). ADMETlab analysis further displayed 13 physicochemical properties of paraben, including their Upper Limit, Lower Limit, and Compound Properties (Fig. 4A).

Fig. 3.

Fig. 3

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Toxicity analysis of paraben. A ProTox-based evaluation of the physicochemical properties of paraben. B Toxicity prediction of paraben

Fig. 4.

Fig. 4

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Screening of paraben targets. A ADMETlab analysis of the physicochemical properties, including upper limit, lower limit, and compound properties of paraben. B Union of paraben targets predicted by STITCH and SwissTargetPrediction databases, yielding 35 candidate targets

Target identification for paraben

The STITCH database identified 19 potential targets of paraben, including INS-IGF2, ESR1, and MDH2. SwissTargetPrediction screening yielded 22 additional targets, such as CA2, CA7, and CA1. After removing six overlapping targets, a union of the remaining targets resulted in 35 paraben-related targets (Fig. 4B).

Breast cancer-related targets

GeneCards provided 3,359 breast cancer-associated genes (e.g., BRCA2, BRCA1, ATM), OMIM contributed 169 targets (e.g., PLA2G2A, EPHB2), and TTD retrieved 94 targets (e.g., EGFR, FGFR1). After deduplication, a total of 3,413 breast cancer-related targets were identified (Fig. 5A).

Fig. 5.

Fig. 5

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Identification of breast cancer-related and overlapping targets. A Union of breast cancer-related targets from GeneCards, OMIM, and TTD databases, resulting in 3,413 targets. B Intersection of 35 paraben targets and 3,413 breast cancer-related targets, identifying 13 overlapping targets

Screening of paraben-induced breast cancer targets

Intersection analysis between the 35 paraben targets and 3,413 breast cancer-related genes revealed 13 overlapping targets (e.g., MDH2, CA12), which were identified as potential core targets linking paraben exposure to breast cancer (Fig. 5B).

Compound regulatory network

A compound regulatory network illustrated the interaction between paraben, the 13 overlapping targets, and breast cancer. The network suggests that paraben may promote breast cancer by modulating targets such as MDH2 and CA12 (Fig. 6A).

Fig. 6.

Fig. 6

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Compound-target network and PPI analysis. A Network diagram illustrating the molecular mechanism by which paraben acts on overlapping targets to promote breast cancer. B PPI network highlighting ESR1, ESR2, SERPINE1, and CA2 as central nodes with extensive connectivity

PPI analysis

The PPI results revealed that ESR1, ESR2, SERPINE1, and CA2 were located at the center of the network diagram and exhibited extensive interactions with other proteins. The primary molecular functions associated with these proteins were color-coded as follows: yellow for carbonate dehydratase activity, green for hydro-lyase activity, red for nuclear estrogen receptor activity, blue for estrogen response element binding, and purple for transition metal ion binding (Fig. 6B). The PPI network comprised 13 nodes and 30 edges, with a significant enrichment p-value of 0.000643.

Enrichment analysis

Enrichment analysis demonstrated that the overlapping targets were predominantly enriched in the molecular function of “hydro-lyase activity”. Regarding biological processes, these targets were involved in “male sex differentiation, hormone metabolic process, defense response to bacterium, and morphogenesis of an epithelium”. At the pathway level, the targets were primarily associated with “nitrogen metabolism” and “chemical carcinogenesis-receptor activation” (Figs. 7 and 8).

Fig. 7.

Fig. 7

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GO enrichment analysis. The molecular function of the intersection target is hydro-lyase activity. The biological processes are: male sex differentiation, hormone metabolic process, defense response to bacterium, morphogenesis of an epithelium

Fig. 8.

Fig. 8

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KEGG pathway analysis. The overlapping targets are primarily enriched in the nitrogen metabolism, and chemical carcinogenesis-receptor activation pathways

Core target screening

Using the cytoHubba plugin with Degree values ranking, three core targets—ESR1, ESR2, and SERPINE1—were identified (scores listed in Table 1). These targets occupied central positions in the network diagram and showed dense connections with multiple proteins (Fig. 9).

Table 1.

Screening of core genes by cytoscape

Hub genes Degree BetweennessCentrality ClosenessCentrality
ESR1 11 0.4025 0.9231
ESR2 8 0.0907 0.7500
SERPINE1 8 0.2197 0.7500
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Fig. 9.

Fig. 9

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Core target identification. Top three core targets ranked by degree values: ESR1, ESR2, and SERPINE1, positioned centrally within the network and exhibiting robust interactions with other proteins

Degree, Betweenness Centrality, and Closeness Centrality values for the three core genes (ESR1, ESR2, SERPINE1) calculated using the cytoHubba plugin.

Immune infiltration analysis

The immune infiltration analysis indicated that in breast cancer, ESR1 expression was negatively correlated with the infiltration levels of CD8T cells and macrophages, while ESR2 and SERPINE1 expression exhibited positive correlations with the infiltration of these immune cells (Fig. 10A–C).

Fig. 10.

Fig. 10

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Immune infiltration analysis. A ESR1 expression negatively correlates with infiltration levels of CD8 T cells and macrophages in breast cancer. B, C ESR2 and SERPINE1 expression positively correlates with infiltration levels of CD8 T cells and macrophages

Molecular docking

Molecular docking models illustrated the binding interactions between paraben and the three core targets (ESR1, ESR2, SERPINE1). Both surface models and ribbon diagrams demonstrated strong binding affinities between the ligands (paraben) and receptors (Fig. 11A–F).

Fig. 11.

Fig. 11

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Molecular docking. A, B Surface and ribbon models of paraben binding to ESR1. C, D Surface and ribbon models of paraben binding to ESR2. E, F Surface and ribbon models of paraben binding to SERPINE1

Discussion

Paraben, widely utilized as preservatives in the food, pharmaceutical, and cosmetic industries, exhibit potential toxicity and can enter the human body through various environmental contaminants such as food, water, and air. It readily bioaccumulates in organisms and has been detected in human blood, urine, and breast tissues [33]. Paraben is associated with multiple health risks including endocrine disruption, carcinogenic effects, infertility, reduced sperm count and motility, adipogenesis, and skin allergies [34]. As one of the most prevalent EDCs in personal care products and cosmetics, paraben exhibits weak estrogenic activity by binding to estrogen receptors, thereby interfering with normal endocrine functions, disrupting hormonal regulation, and potentially contributing to adverse reproductive outcomes [35–37]. For instance, paraben may impair thyroid function by disrupting the HPT axis, subsequently affecting the regulation and biosynthesis of thyroid hormones (TH) [38]. Additionally, paraben demonstrate potential neuroendocrine toxicity in zebrafish larvae, manifested as reduced swimming distance and average speed, along with dysregulation of the hypothalamic-pituitary-interrenal (HPI) axis [39]. Chronic exposure to paraben may lead to estrogen imbalance in women, increasing breast cancer risk. This carcinogenic potential arises through two mechanisms: (1) Their endocrine-disrupting properties may promote tumor cell growth and proliferation by disturbing hormonal homeostasis [40] ; and (2) In vitro studies have demonstrated that paraben can induce genetic mutations and oxidative stress, thereby elevating cancer risk [41]. Notably, elevated concentrations of paraben have been detected in breast cancer tissues [42], suggesting their potential involvement in breast carcinogenesis.

This study employed network toxicology to explore the molecular mechanisms underlying paraben-induced breast carcinogenesis. Three potential core targets were identified: ESR1, ESR2, and SERPINE1. Immune infiltration analysis revealed significant negative correlations between ESR1 expression and infiltration levels of CD8T cells and macrophages in breast cancer tissues. We hypothesize that ESR1 may impair normal anti-tumor immune responses by suppressing effector T cell infiltration, thereby potentially facilitating tumor immune evasion. In contrast, ESR2 and SERPINE1 expression demonstrated positive correlations with infiltration of CD8T cells and macrophages, suggesting their potential role in recruiting these immune cells to tumor sites to enhance immune surveillance and cytotoxic effects against malignant cells. Molecular docking analysis visually demonstrated strong binding affinities between paraben and the core target proteins.

The ESR1 (Estrogen Receptor 1) gene encodes the estrogen receptor α (ERα) protein, which plays a critical role in promoting the initiation and progression of estrogen receptor (ER)-positive breast cancer. ER-positive breast cancer accounts for approximately 70% of all breast cancer cases, making ESR1 a central focus in breast cancer research [43]. Paraben, which exhibits estrogenic activity, can bind to ESR1 by mimicking the action of natural estrogen, thereby disrupting its normal function. This interaction may lead to hyperactivation or aberrant activity of ESR1, subsequently altering transcriptional regulation of downstream genes. Consequently, paraben may contribute to mammary cell abnormalities and elevate cancer risk through ESR1-mediated pathways. While endocrine therapy has significantly improved survival rates in ER + breast cancer patients, prolonged treatment has been associated with a marked increase in ESR1 point mutations compared to untreated patients, enabling constitutive activation of ERα in the absence of estrogen, driving tumor proliferation and survival, and represent a major mechanism underlying endocrine therapy resistance [44]. In response to these ESR1 mutations, there is an urgent clinical need for next-generation ER-targeted therapies. Current drug development efforts focus on novel agents such as PROTACs, CERANs, and SERCA, which aim to overcome endocrine resistance caused by ESR1 mutations [45].

ESR2 (Estrogen Receptor 2), also known as ERβ, plays a crucial role in maintaining normal physiological functions of breast tissue. ESR2 participates in regulating the proliferation, differentiation, and apoptosis of mammary cells, maintaining tissue homeostasis and ensuring normal growth and development of mammary cells. Studies have shown that ESR2 is typically expressed at low levels in breast cancer, and its elevated expression may correlate with improved prognosis [46]. Paraben may mimic estrogen by interacting with ESR2, thereby interfering with its normal physiological functions and disrupting ESR2-mediated signaling pathways, ultimately leading to a series of pathological cellular responses. Consequently, paraben might disrupt the normal hormonal regulatory balance in mammary cells by affecting ESR2, altering cellular growth, differentiation, and apoptosis processes, potentially enabling mammary cells to progressively develop into cancer cells. Multiple studies indicate that genetic polymorphisms in ESR2 may alter the structure and function of the ESR2 protein, influencing its estrogen-binding affinity and activation of downstream signaling pathways, thereby modifying individual susceptibility to breast cancer [47, 48]. These polymorphisms could also affect breast cancer patients’ responses to endocrine therapy, and targeted therapies developed based on these findings have opened new avenues for breast cancer treatment.

SERPINE1 (also known as PAI-1, plasminogen activator inhibitor-1), a serine protease inhibitor involved in various physiological and pathological processes such as blood coagulation, fibrinolysis, and extracellular matrix remodeling, has been associated with poor prognosis in multiple cancers due to its elevated expression [49, 50]. In obesity-related breast cancer studies, researchers have identified SERPINE1’s participation in DNA damage response mechanisms. This protein facilitates the repair of radiation-induced DNA double-strand breaks in cancer cells, thereby enhancing their radioresistance [51]. The miR-1185-2-3p-GOLPH3L pathway has been shown to stabilize p53-induced SERPINE1, promoting glucose metabolism in breast cancer and suggesting novel therapeutic targets for human breast malignancies [52]. These findings collectively highlight SERPINE1’s critical role in breast cancer pathogenesis. Although direct interaction evidence between SERPINE1 and paraben remains lacking, both entities contribute significantly to breast cancer initiation and progression through distinct mechanisms: endocrine disruption and involvement in different tumorigenic stages. Consequently, the endocrine-disrupting effects of paraben may potentially modulate SERPINE1-associated signaling pathways, thereby promoting breast carcinogenesis.

Inspired by the association between cosmetics and breast cancer, this study employed network toxicology and molecular docking technology to investigate the molecular mechanisms underlying the induction of breast cancer by paraben, aiming to provide theoretical support for therapeutic strategies targeting specific biomarkers. The limitation of this study is that it only analyzes the existing data, lacking experimental validation and further mechanistic exploration at the animal and cellular levels, which is what we will continue to do next.

Limitations

The limitation of this study is that it only analyzes the existing data, and lacks experimental validation and further mechanism exploration at the animal and cellular levels.

Conclusion

Paraben contributes to breast carcinogenesis by targeting ESR1, ESR2, and SERPINE1 to dysregulate associated signaling pathways, disrupts cellular physiological activities, and induces pathological alterations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (24.3KB, txt)
Supplementary Material 2 (1.2KB, txt)
Supplementary Material 3 (123B, txt)
Supplementary Material 4 (132B, txt)
Supplementary Material 5 (613B, txt)

Acknowledgements

Thanks to the public database for the raw data (PubChem, ProTox, ADMETlab, STITCH, SwissTargetPrediction, GeneCards, OMIM, and TTD databases).

Author contributions

Wen Zhang contributed to drafting of the manuscript and data analysis. Rui Xiang, Wang Gu, Qiang Zhang, Lei Liu, Chenglin Wang, Muhu Chen, Yingchun Hu contributed to data collection and data analysis. Guihong Chen contributed to study design and guidance of the study. All authors reviewed the manuscript.

Funding

This study was supported by the Key Clinical Specialty Construction Project of Sichuan Province, IIT Project of Health Commission of Sichuan Province (Project No.: 23LCYJ001).

Data availability

All data for this study are from public databases, relevant data are available on the website of the Methods section, and relevant documents have been uploaded as attachments.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (24.3KB, txt)
Supplementary Material 2 (1.2KB, txt)
Supplementary Material 3 (123B, txt)
Supplementary Material 4 (132B, txt)
Supplementary Material 5 (613B, txt)

Data Availability Statement

All data for this study are from public databases, relevant data are available on the website of the Methods section, and relevant documents have been uploaded as attachments.

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