Integrative Network Toxicology and Molecular Docking Elucidate the Molecular Mechanisms and Immune Implications of Diethyl Terephthalate in Bladder Cancer

Integrative Network Toxicology and Molecular Docking Elucidate the Molecular Mechanisms and Immune Implications of Diethyl Terephthalate in Bladder 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 Integrative Network Toxicology and Molecular Docking Elucidate the Molecular Mechanisms and Immune Implications of Diethyl Terephthalate in Bladder Cancer Nanjie Li, Lu Ding, Hui Dong, Xusheng Zhang, Jiawei Wang, Huifeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8349736/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 Objective Diethyl terephthalate (DET) is a diester small molecule that may originate as a by-product or a degradation/migration component from polyethylene terephthalate (PET)-related materials during their lifecycle processes such as synthesis, processing, use, and recycling. However, the potential molecular links between DET exposure and the development and progression of bladder cancer (BLCA) remain inadequately elucidated. This study aims to integrate network toxicology and bioinformatics strategies to identify potential DET targets and their intersection with BLCA-related gene networks, further construct a prognostic model to screen key genes, assess correlations with the immune microenvironment, and provide computational evidence at the structural level through molecular docking, thereby offering candidate clues for subsequent mechanistic research and experimental validation. Methods Potential DET targets were predicted by inputting its SMILES (CCOC(= O)C1 = CC = C(C = C1)C(= O)OCC) into the SwissTargetPrediction database; BLCA-related gene sets were retrieved from OMIM, TTD, and GeneCards databases and intersected with DET targets to obtain candidate common targets, which were then visualized using Cytoscape; GO and KEGG enrichment analyses were performed with significance determined by a Benjamini–Hochberg corrected FDR < 0.05. Using expression and follow-up data from the TCGA-BLCA cohort, univariate Cox, LASSO, and multivariate Cox regressions were employed to screen prognostic key genes and construct a risk score model. Model performance was evaluated using Kaplan–Meier survival analysis and time-dependent ROC analysis, and a nomogram along with calibration curves were built by integrating clinical variables to assess predictive consistency. CIBERSORT was used to estimate the infiltration proportions of 22 immune cell types and analyze the correlation between key gene expression and immune infiltration. Finally, receptor/ligand structures were obtained from PDB and PubChem, and molecular docking was performed using AutoDock Vina with PyMOL for visualizing interaction patterns. Results A total of 115 potential DET targets were predicted; intersection with BLCA-related gene sets yielded 86 candidate common targets. Enrichment analysis showed that common targets were significantly enriched in molecular function terms such as serine/threonine kinase activity and phosphatidylinositol kinase activity, and in tumor-related signaling pathways such as cAMP and ErbB pathways. Enrichment in terms like "Chemical carcinogenesis - ROS" suggested potential associations with oxidative stress and chemical carcinogenesis processes. Prognostic modeling identified six key genes (NQO1, AKR1B1, ADK, GAK, PDE5A, MAPK10). The constructed risk score model enabled high/low-risk stratification (Kaplan–Meier: P < 0.001) and demonstrated moderate discriminative ability for 1-, 3-, and 5-year survival (AUC = 0.675, 0.658, 0.651). Immune infiltration analysis indicated significant correlations between the expression of key genes and the infiltration proportions of CD8⁺ T cells, dendritic cells, mast cells, NK cells, and macrophage polarization-related subsets. Molecular docking showed that DET could achieve binding conformations with binding energies lower than − 5 kcal/mol for all six key target proteins, with the lowest binding energy observed for AKR1B1 (− 7.9 kcal/mol). Conclusion This study proposes, at a systems level, a potential association between the DET-related target network and key BLCA signaling regulation, prognostic risk stratification, and immune microenvironment characteristics, providing a candidate framework of key genes and pathways. Given that the analysis is primarily based on public cohorts and computational predictions, further assessment of causal likelihood and translational potential requires integration with population exposure measurements, in vitro/in vivo functional experiments, and external validation using independent cohorts. Diethyl terephthalate (DET) Bladder cancer (BLCA) Network toxicology Prognostic model Immune infiltration Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The profound influence of human activities on the Earth system has led to the concept of the “Anthropocene” [ 1 ]. In this context, plastics have been produced and used at an unprecedented scale because of their low weight, durability, low cost, and broad applicability, thereby becoming an increasingly important source of environmental exposure [ 2 – 4 ]. Common plastic polymers include polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), polycarbonate (PC), polyvinyl chloride (PVC), and polystyrene (PS) [ 5 – 7 ]. Across their production–use–disposal/recycling lifecycle, plastics can continuously release contaminants, including polymer fragments and chemical additives. Microplastics (MPs; <5 mm) have attracted particular attention due to their environmental persistence, widespread distribution, and potential for bioaccumulation [ 8 – 10 ]. Recent studies have detected MPs in multiple human specimens, including placenta, lung, and liver tissues, as well as sputum, breast milk, feces, urine, and blood [ 11 , 12 ]. These findings indicate that MPs and plastic-associated chemicals can enter the body via ingestion, inhalation, or dermal contact and may accumulate over time. Consistent with this exposure potential, experimental evidence suggests that plastic-related exposures can elicit inflammation, oxidative stress, and immune dysregulation—processes implicated in several hallmarks of tumorigenesis [ 13 – 15 , 27 – 29 ]. Among potential health outcomes linked to plastic-related exposures, urological malignancies warrant close attention. Bladder cancer (BLCA) is a common urological cancer characterized by complex gene-regulatory programs and tumor microenvironment (TME) remodeling, including dysregulated signaling, metabolic reprogramming, and immune evasion [ 16 – 18 ]. Although environmental exposures are recognized as important exogenous contributors to BLCA risk and prognosis, systematic molecular investigations connecting specific plastic-associated chemicals to BLCA remain limited [ 19 – 21 ]. Diethyl terephthalate (DET), a terephthalate diester, can serve as a representative candidate chemical within PET-related systems for computational toxicology analyses. However, the molecular mechanisms potentially linking DET exposure to BLCA have not been comprehensively characterized, and candidate targets for downstream validation remain insufficiently defined. Recent advances in network toxicology and computational biology enable integrative frameworks that combine target prediction, transcriptomic profiling, statistical modeling, and molecular docking to interrogate chemical–disease associations at a systems level [ 22 – 26 ]. Accordingly, this study investigated DET using an integrated pipeline encompassing target prediction, intersection with disease genes, network and pathway analyses, prognostic modeling, immune infiltration assessment, and molecular docking. Specifically, we: (1) identified putative DET targets and intersected them with BLCA-related genes to define candidate shared targets; (2) characterized potential biological functions and signaling pathways using protein–protein interaction (PPI) and enrichment analyses; (3) constructed and evaluated a prognostic risk model in the TCGA-BLCA cohort; (4) quantified associations between key genes and immune cell infiltration patterns; and (5) performed molecular docking to provide structural-level computational evidence for potential ligand–target interactions. Collectively, this work aims to generate testable hypotheses regarding the DET-related target network in BLCA and to nominate candidates for subsequent mechanistic validation and translational investigation. 2. Methods 2.1 Acquisition of Potential Toxicity Targets and Disease-Related Genes Potential human targets of DET were predicted using SwissTargetPrediction by inputting the SMILES string (CCOC(= O)C1 = CC = C(C = C1)C(= O)OCC). After mapping to gene symbols and removing duplicates, 115 unique genes were obtained. BLCA-related genes were retrieved from OMIM, TTD, and GeneCards using the keyword “bladder cancer”. All disease-associated genes were merged and deduplicated to generate a BLCA gene set. The intersection between predicted DET targets and the BLCA gene set was defined as the candidate shared target set for downstream analyses. 2.2 Network construction A network was constructed to illustrate the potential linkages between DET exposure, toxicological targets, and bladder cancer. The network was built using Cytoscape. Nodes represented DET, toxicological targets, and bladder cancer, while edges represented the relationships between them 2.3 GO and KEGG Enrichment Analysis Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the candidate shared target set. Multiple testing was controlled using the Benjamini–Hochberg procedure, and an FDR < 0.05 was considered statistically significant. 2.4 Identification of Prognostic Genes and Construction of the Risk Score Model Transcriptomic profiles and clinical follow-up information (overall survival time and status) for TCGA-BLCA were obtained and preprocessed (sample matching, missing-value handling, and expression normalization). Using the candidate shared target set as the initial gene pool, univariate Cox proportional hazards regression was performed to screen prognostic genes (P < 0.05). Significant candidates were then subjected to LASSO regression to reduce collinearity and overfitting, with the optimal penalty parameter (λ) selected by 10-fold cross-validation. Genes retained after LASSO were entered into a multivariable Cox model to derive regression coefficients (β). The risk score was calculated as: Risk score = Σ(expression_i × β_i). 2.5 Model Validation and Nomogram Evaluation Patients were stratified into high-risk and low-risk groups based on the median risk score. Kaplan–Meier survival analysis with the log-rank test was used to evaluate the stratification capability. Time-dependent ROC analysis was employed to assess the model's discriminative ability for 1-, 3-, and 5-year survival outcomes, and AUC values were calculated. Univariate and multivariate Cox regressions incorporating clinical variables (e.g., age, gender, TNM stage) were performed to evaluate the independent prognostic value of the risk score. Based on this, a nomogram was constructed to predict individualized 1-, 3-, and 5-year survival probabilities, and calibration curves were used to evaluate prediction consistency. 2.6 Immune Cell Infiltration Analysis CIBERSORT was applied to deconvolute the BLCA transcriptomic data to estimate the relative infiltration proportions of 22 immune cell types; only samples with a CIBERSORT output P-value < 0.05 were retained for subsequent analysis. If the analysis involved multi-cohort or multi-batch data, the ComBat method was used for batch effect correction; if only a single-source cohort was used, no batch correction was performed, as stated in the methods. Subsequently, Spearman correlation analysis was used to assess the association between the expression levels of key genes and immune cell infiltration proportions. 2.7 Molecular Docking Three-dimensional structures of key target proteins were obtained from the Protein Data Bank (PDB), and the DET molecular structure was obtained from PubChem. Molecular docking was performed using AutoDock Vina: receptor and ligand structures underwent preprocessing (e.g., removal of extraneous ligands/water molecules, addition of hydrogen atoms, assignment of charges, format conversion), a grid box was set to cover the potential binding pocket, representative binding conformations and predicted binding energies were output, and PyMOL was used to visualize the interaction patterns. To ensure reproducibility, key parameters such as PDB IDs, grid center coordinates and size, and exhaustiveness are summarized in the supplementary materials. 2.8 Statistical Analysis All statistical analyses were conducted in R 4.5.2 unless otherwise specified. All tests were two-sided. For continuous variables and correlation analyses, P < 0.05 was considered statistically significant. When multiple comparisons were performed, false discovery rate (FDR) correction was applied. Survival analyses were conducted using Cox regression and Kaplan–Meier methods. Predictive performance was evaluated using time-dependent ROC analyses and calibration assessments. 3. Results 3.1 Identification of PET Exposure and Bladder Cancer-Related Target Genes To investigate the potential molecular link between PET exposure and BLCA, we performed a Venn diagram analysis comparing 115 DET-related targets with 15,969 BLCA-related differentially expressed genes (DEGs), identifying a total of 86 overlapping genes. These genes may serve as key molecular targets through which long-term PET exposure participates in the pathogenesis of BLCA. Furthermore, we used Cytoscape for visualization, constructing a "DET-Toxicity Targets-Bladder Cancer" interaction network. In this network, DET acts as a central node connected to multiple toxicological targets, which in turn establish associations with bladder cancer-related genes, revealing that DET may synergistically participate in bladder carcinogenesis through multi-target interactions. 3.2 Functional Enrichment Analysis Reveals Significant Enrichment in Signal Transduction and Tumor-Related Pathways GO and KEGG enrichment analyses were conducted for the 86 shared targets. GO terms were significantly enriched in biological processes such as cellular response to chemical stress , positive regulation of protein phosphorylation , and peptidyl-serine phosphorylation , indicating that these targets are closely associated with phosphorylation control and signal transduction. KEGG analysis further demonstrated significant enrichment in tumor-associated pathways, including the cAMP signaling pathway and the ErbB signaling pathway . In addition, enrichment of “ Chemical carcinogenesis - ROS ” suggested potential involvement of oxidative stress and chemical carcinogenesis-related processes. Collectively, these results provide systems-level support for an association between the DET-related target network and key signaling programs implicated in BLCA. Figure 1. Identification of DET-associated candidate targets in BLCA, PPI network and functional enrichment. (A) Venn diagram showing 86 intersecting genes between the predicted DET-related targets and BLCA-related genes. (B) “DET–targets–BLCA” interaction network visualized in Cytoscape (DET and BLCA as phenotype nodes; genes as shared nodes). (C) Bubble plots showing the top 10 enriched GO terms in BP, CC and MF categories. (D) Top 10 significantly enriched KEGG pathways. Bubble size indicates gene count and color indicates statistical significance. 3.3 Screening of Key Prognostic Genes and Construction of the Risk Score Model Univariate Cox regression was applied to the 86 shared targets, identifying 22 genes significantly associated with overall survival (P < 0.05). These candidates were then subjected to LASSO regression for further feature reduction, and multivariate Cox regression ultimately retained six key prognostic genes (NQO1, AKR1B1, ADK, GAK, PDE5A, and MAPK10). A risk score model was established using these six genes, and patients were stratified into high- and low-risk groups according to the median risk score. Kaplan–Meier analysis demonstrated significantly poorer overall survival in the high-risk group compared with the low-risk group (P < 0.001). Time-dependent ROC analysis suggested moderate predictive performance for 1-, 3-, and 5-year survival, with AUC values of 0.675, 0.658, and 0.651, respectively. A nomogram integrating the risk score with clinical characteristics further showed general concordance between predicted and observed survival probabilities based on calibration curves, indicating potential utility for individualized outcome estimation. Figure 2 Screening of key prognostic genes, construction of the risk score model, and clinical prediction evaluation. (A) Univariate Cox regression results for the candidate common targets (forest plot or bar chart displaying significant genes). (B) Trajectory plot of LASSO regression coefficients. (C) Cross-validation curve to determine the optimal λ. (D) Multivariate Cox regression screening identifying 6 key genes and construction of the risk score model. (E) Kaplan–Meier overall survival curves grouped by median risk score. (F) Time-dependent ROC curves evaluating 1-/3-/5-year survival prediction performance. (G) Univariate/Multivariate Cox regression incorporating clinical variables (age, gender, TNM stage, etc.) validating the independent prognostic value of the risk score. (H) Nomogram and calibration curves: Predicting 1-/3-/5-year survival probabilities based on the risk score (and clinical variables, if included) and assessing prediction consistency. 3.4 Correlation Between Key Gene Expression and Immune Infiltration Characteristics CIBERSORT was used to estimate the relative fractions of 22 immune cell types, followed by correlation analyses. Overall, the six prognostic genes exhibited significant relationships with multiple immune cell subsets. Specifically, NQO1 showed positive correlations with resting mast cells and resting dendritic cells, but negative correlations with CD8⁺ T cell infiltration and activated mast cells. AKR1B1 was positively associated with M2 macrophages and negatively associated with M1 macrophages. ADK displayed a positive correlation with activated dendritic cells, whereas GAK correlated positively with neutrophil infiltration. PDE5A was positively correlated with resting memory CD4⁺ T cells and resting mast cells but negatively correlated with resting NK cells, activated memory CD4⁺ T cells, and CD8⁺ T cell infiltration. MAPK10 was positively related to activated dendritic cells, monocytes, mast cells, and resting memory CD4⁺ T cells, while showing negative associations with CD8⁺ T cell infiltration, resting NK cells, activated mast cells, and activated memory CD4⁺ T cells. Collectively, these findings suggest that the DET-associated prognostic gene network may be linked to the immune infiltration landscape in BLCA, thereby offering candidate directions for mechanistic investigation of immune microenvironment regulation. Figure 3 Risk stratification and correlation analysis of key genes with immune infiltration. (A) Differences in the infiltration proportions of 22 immune cell types between the high-risk and low-risk groups (box plots/violin plots; significance indicated by asterisks). (B–G) Lollipop plots showing the Spearman correlation between the expression of key genes (NQO1, AKR1B1, ADK, GAK, PDE5A, MAPK10) and the infiltration of 22 immune cell types; dot size indicates the absolute value of the correlation coefficient, and color indicates statistical significance (e.g., P < 0.05). 3.5 Molecular Docking Results of DET with Key Target Proteins To assess potential ligand–protein interactions at the structural level, DET was docked to the six key target proteins. Docking results indicated that DET adopted feasible conformations within predicted binding pockets for all targets, with binding energies below −5 kcal/mol. Among the targets, AKR1B1 exhibited the strongest predicted affinity (−7.9 kcal/mol), followed by NQO1 (−5.9), GAK (−6.3), ADK (−6.1), PDE5A (−7.4), and MAPK10 (−6.4). These results support the possibility of DET binding to these proteins; nonetheless, docking constitutes computational evidence only, and biological relevance requires further confirmation through molecular dynamics simulations and in vitro binding and functional assays. Figure 4 Molecular docking results of DET with key target proteins. (A–F) Docking conformations of DET with ADK, GAK, MAPK10, NQO1, PDE5A, and AKR1B1, respectively. Each panel can sequentially display: protein surface and ligand position (left), enlarged view of the binding pocket with key amino acid residue interactions (middle), and a 2D interaction diagram (right). Corresponding binding energies (kcal/mol): ADK −6.1, GAK −6.3, MAPK10 −6.4, NQO1 −5.9, PDE5A −7.4, AKR1B1 −7.9. 4. Discussion The potential link between microplastic (MP) exposure and tumorigenesis has attracted increasing scientific interest. MPs are thought to promote tumor progression through mechanisms such as oxidative stress, inflammation, and endocrine disruption. Previous studies indicate that MPs can induce hepatotoxicity and carcinogenesis, contribute to endocrine dysregulation in cancers such as those of the breast and cervix, and correlate with neurotoxicity and brain malignancies [ 27 ]. Accordingly, contemporary research increasingly integrates epidemiological evidence with in vitro and in vivo findings to delineate molecular pathways that may connect MPs to cancer outcomes [ 28 ]. Mechanistically, MPs may activate signaling cascades that enhance cell proliferation and tumor-specific invasion, thereby fostering a tumor-promoting microenvironment. Moreover, MP-associated carcinogenesis may involve biomarkers indicative of both plastic exposure and tumor progression [ 29 ]. Despite these advances, the mechanistic relationship between diethyl terephthalate (DET) and bladder cancer remains poorly characterized. Therefore, the present study employed a network toxicology framework to investigate molecular associations between DET exposure and the development, progression, and prognosis of bladder cancer. Enrichment analyses of DET-related toxicological targets differentially expressed in BLCA revealed several pathways central to BLCA biology. Notably, significant enrichment was observed for serine/threonine kinases, phosphatidylinositol kinases, the cAMP signaling pathway, and the ErbB signaling pathway. Serine/threonine kinases propagate signals through phosphorylation of downstream substrates, regulating diverse cellular processes [ 30 ]. Phosphatidylinositol kinases modulate phosphatidylinositol metabolism to generate second messengers, thereby governing key cellular functions [ 31 ]. Dysregulated cAMP signaling has been implicated in metabolic reprogramming in cancer cells, potentially promoting proliferation and invasion [ 32 ]. Aberrant ErbB signaling contributes to multiple malignancies and is highly relevant to oncogenic processes [ 33 ] (Fig. S1 ). Consistent with these observations, MPs have been reported to exert cytotoxic effects in urinary tract-related cell lines by increasing inflammatory responses, reducing viability, and perturbing pathways such as mitogen-activated protein kinase (MAPK) signaling [ 34 ]. Although direct evidence linking DET to bladder cancer remains limited, its polymer PET has been shown to disrupt key molecular and cellular pathways in diabetic nephropathy, impairing the regulation of apoptosis, immune responses, and cellular homeostasis [ 35 ]. Together, these findings support the hypothesis that DET-associated chemical exposure may contribute to BLCA initiation and progression by modulating growth-related signaling and amplifying proliferative programs. To further explore DET–BLCA molecular connections, we identified six hub genes: GAK, NQO1, AKR1B1, ADK, PDE5A, and MAPK10. GAK is a cyclin-dependent serine/threonine protein kinase implicated in cell cycle progression and cancer development [ 36 ]. NQO1 exhibits context-dependent (“Janus”) functions, is upregulated in multiple solid tumors, and higher expression is associated with poorer prognosis [ 37 ]. AKR1B1-mediated endogenous fructose metabolism can promote proliferation and migration through regulation of cyclins and the RhoA–ROCK2 axis, thereby facilitating tumorigenesis [ 38 ]. Tumor cell survival and expansion are closely linked to adenosine metabolism [ 39 ]. As a regulator of adenosine levels, ADK may promote oncogenic progression [ 40 ] and influence immune responses via overactivation of the Gs protein-coupled A2A receptor on immune cells [ 41 ]. PDE5A hydrolyzes cyclic guanosine monophosphate (cGMP), a key intracellular second messenger [ 42 ]. In gastric cancer, PDE5A-positive cancer-associated fibroblasts (CAFs) have been reported to promote epithelial–mesenchymal transition by remodeling the extracellular matrix and to activate PI3K/AKT/mTOR signaling, inducing CXCL12 release; CXCL12 subsequently binds CXCR4, recruits CD8⁺ exhausted T cells (TEX LAG3⁺ T cells), and contributes to an immunosuppressive TME [ 43 ]. MAPK10, a member of the MAPK pathway, has been reported to function as a tumor suppressor when activated during bladder cancer progression [ 44 ]. Molecular docking is widely used to predict binding modes and affinity, supporting target identification in drug discovery and materials research [ 45 ]. Prior docking studies have reported high-affinity binding between DET and core targets [ 46 ]. In our docking analyses, DET exhibited feasible binding conformations at the active sites of the six hub proteins, with favorable predicted affinities. These results suggest that DET may influence BLCA through these targets. Notably, the six hub genes also demonstrated relatively high AUC values, supporting their potential diagnostic relevance and clinical applicability. MPs can shape the tumor microenvironment (TME), thereby promoting tumor progression [ 47 ]. In pancreatic cancer, MP-infiltrated tumor immune microenvironment (TIME) has been associated with decreased CD8⁺ T cells, natural killer cells, and dendritic cells, alongside increased neutrophil infiltration [ 48 ], underscoring a role for MPs in shaping immune contexture. Consistent with this, our immune infiltration analyses suggest that DET exposure may remodel the BLCA immune microenvironment by modulating immune cell recruitment and activation. Specifically, NQO1 and MAPK10 may be involved in dendritic cell, mast cell, and CD8⁺ T cell responses; MAPK10 may negatively regulate neutrophil abundance; PDE5A may modulate CD4⁺ T cells, CD8⁺ T cells, mast cells, and natural killer cells; ADK may promote dendritic cell activation; and AKR1B1 appears primarily associated with M1/M2 macrophage polarization and may facilitate immune escape. Although direct evidence linking DET to the tumor immune microenvironment remains limited, exposure to its polymer PET in diabetic nephropathy has been associated with reduced CD8⁺ T cells, monocytes, and neutrophils, and increased Tregs and M2 macrophages [ 49 ]. In periodontitis, PET has been reported to affect immune-related pathways including C-type lectin receptor signaling, VEGF receptor signaling, and TNF signaling [ 50 ]. Collectively, these observations suggest that DET-related exposures may be closely linked to immune microenvironment regulation, potentially representing a mechanistic axis contributing to bladder cancer progression. It is important to note that molecular docking does not imply stable in vivo binding or confirm pathogenic effects. Actual biological outcomes may depend on DET exposure levels, metabolic transformation, tissue distribution, target accessibility, and co-exposure to other environmental factors. To strengthen the evidentiary chain, future work should prioritize: (1) exposure assessment, including measurement of DET or its metabolites (e.g., serum/urine biomarkers) in population samples and correlation with clinical outcomes and molecular phenotypes; (2) mechanistic validation via controlled-dose exposure experiments in bladder epithelial/tumor and immune cell systems to assess oxidative stress, inflammatory signaling, and immune-regulatory pathways; and (3) causal inference and external validation using independent cohorts and multi-omics integration (transcriptomics, proteomics, metabolomics, methylation) to evaluate the causal plausibility and translational potential of DET-related networks in BLCA progression. This study has several limitations. First, TCGA-BLCA is a retrospective tumor cohort lacking individual-level DET exposure measurements or systematic environmental/lifestyle covariates, which constrains causal inference and increases susceptibility to confounding and co-exposure effects. Second, the prognostic model and immune infiltration analyses were developed primarily within the same public cohort; external validation is necessary to assess generalizability. Third, CIBERSORT deconvolution relies on reference immune signatures and may be influenced by tumor purity, batch effects, and sample heterogeneity; future studies should employ multiple algorithms and/or experimental validation (e.g., flow cytometry, immunohistochemistry, spatial omics) for triangulation. Finally, docking provides static, structure-based predictions; molecular dynamics simulations and in vitro binding assays (e.g., SPR, MST) are recommended to validate binding stability and functional relevance. Overall, this study proposes and evaluates an integrated analytical framework for interrogating systemic “environmental chemical–tumor” associations. It identifies candidate shared targets between DET and BLCA, delineates key enriched signaling pathways, constructs a prognostic gene signature, and links these genes to immune microenvironment features. These findings provide actionable directions for mechanistic studies, functional validation, and biomarker development. 5. Conclusion Using an integrated strategy combining network toxicology and bioinformatics, this study systematically identified overlap between predicted DET targets and the BLCA-related gene network, characterized the corresponding PPI network and functional enrichment patterns, and constructed a prognostic risk model comprising NQO1, AKR1B1, ADK, GAK, PDE5A, and MAPK10. Associations between key genes and the immune infiltration landscape were further delineated, and molecular docking provided computational support for potential DET binding to hub proteins. Given that the analyses relied primarily on public cohorts and in silico predictions, future work should integrate exposure measurements, experimental models, and external cohort validation to more rigorously assess causal plausibility and translational potential. Declarations Author’s contribution Conceptualization: Nanjie Li,Jiawei Wang Data curation: Nanjie Li,Jiawei Wang Formal analysis: Nanjie Li, Xusheng Zhang Methodology: Nanjie Li,Jiawei Wang Project administration: Hui Dong,Huifeng Wang Software: Nanjie Li Supervision: Lu Ding, Hui Dong Writing – original draft: Nanjie Li，Xusheng Zhang Writing – review & editing: Lu Ding,Hui Dong,HuiFeng Wang Disclosure statement No potential conflict of interest was reported by the authors. Clinical trial number not applicable Funding The study was supported by the Ningxia Key R&D Program (2023BEG03033). Ethics declaration not applicable Data availability statement The datasets generated and/or analysed during the current study are available in the https://zenodo.org/records/18195465. References Trischler H. The Anthropocene: A Challenge for the History of Science, Technology, and the Environment. NTM. 2016;24(3):309–335. English. 10.1007/s00048-016-0146-3 . PMID: 27567637. Rangel-Buitrago N, Neal W, Williams A. The Plasticene: Time and rocks. Mar Pollut Bull. 2022;185(Pt B):114358. doi: 10.1016/j.marpolbul.2022.114358. Epub 2022 Nov 16. PMID: 36401945. Pesonen M, Vähäkangas K. Contribution of common plastic-related endocrine disruptors to epithelial-mesenchymal transition (EMT) and tumor progression. Chemosphere. 2022;309(Pt 1):136560. 10.1016/j.chemosphere.2022.136560 . Epub 2022 Sep 21. PMID: 36152835. Williams AT, Rangel-Buitrago N. The past, present, and future of plastic pollution. Mar Pollut Bull. 2022;176:113429. 10.1016/j.marpolbul.2022.113429 . Epub 2022 Feb 24. PMID: 35217417. Sun MY, Guo JY, Wang XY, Chang X. Sources and distributions of microplastics and the hazards to plants, animals and human health:A review. Ying Yong Sheng Tai Xue Bao. 2024;35(8):2301–2312. English. 10.13287/j.1001-9332.202408.028 . PMID: 39419815. Sangkham S, Faikhaw O, Munkong N, Sakunkoo P, Arunlertaree C, Chavali M, Mousazadeh M, Tiwari A. A review on microplastics and nanoplastics in the environment: Their occurrence, exposure routes, toxic studies, and potential effects on human health. Mar Pollut Bull. 2022;181:113832. Epub 2022 Jun 15. PMID: 35716489. Barceló D, Picó Y, Alfarhan AH, Microplastics. Detection in human samples, cell line studies, and health impacts. Environ Toxicol Pharmacol. 2023;101:104204. 10.1016/j.etap.2023.104204 . Epub 2023 Jun 28. PMID: 37391049. Przygoda-Kuś P, Kosiorowska KE, Urbanek AK, Mirończuk AM. Current Approaches to Microplastics Detection and Plastic Biodegradation. Molecules. 2025;30(11):2462. 10.3390/molecules30112462 . PMID: 40509349; PMCID: PMC12156231. Kelly 9WSL. FJ. Plastic and Human Health: A Micro Issue? Environ Sci Technol. 2017;51(12):6634–6647. 10.1021/acs.est.7b00423 . Epub 2017 Jun 7. PMID: 28531345. Ningrum PT, Keman S, Sulistyorini L, Sudiana IK, Hidayat A, Negoro AHS, Junaidi H, Kustin K. A systematic review of the effects of airborne microplastic contamination on human lungs. Afr J Reprod Health. 2024;28(10s):430–448. 10.29063/ajrh2024/v28i10s.45 . PMID: 39641437. Bäuerlein PS, Hofman-Caris RCHM, Pieke EN, Ter Laak TL. Fate of microplastics in the drinking water production. Water Res. 2022;221:118790. 10.1016/j.watres.2022.118790 . Epub 2022 Jun 24. PMID: 35780766. Zhang Y, Yang Y, Sun Y, Wei Z, Wang D, Chen S, Yang F, Wang J, Kang X. Assessing the toxicological impact of PET-MPs exposure on IVDD: Insights from network toxicology and molecular docking. J Environ Manage. 2025;373:123830. Epub 2024 Dec 29. PMID: 39736229. Prata JC, da Costa JP, Lopes I, Duarte AC, Rocha-Santos T. Environmental exposure to microplastics: An overview on possible human health effects. Sci Total Environ. 2020;702:134455. 10.1016/j.scitotenv.2019.134455 . Epub 2019 Oct 4. PMID: 31733547. Yee MS, Hii LW, Looi CK, Lim WM, Wong SF, Kok YY, Tan BK, Wong CY, Leong CO. Impact of Microplastics and Nanoplastics on Human Health. Nanomaterials (Basel). 2021;11(2):496. 10.3390/nano11020496 . PMID: 33669327; PMCID: PMC7920297. Zhang Q, He Y, Cheng R, Li Q, Qian Z, Lin X. Recent advances in toxicological research and potential health impact of microplastics and nanoplastics in vivo. Environ Sci Pollut Res Int. 2022;29(27):40415–48. 10.1007/s11356-022-19745-3 . Epub 2022 Mar 26. PMID: 35347608. Malafaia G, Barceló D. Microplastics in human samples: Recent advances, hot-spots, and analytical challenges. Trends Anal Chem. 2023;161:117016. 10.1016/j.trac.2023.117016 . Kutralam-Muniasamy G, Shruti VC, Pérez-Guevara F, Roy PD. Microplastic diagnostics in humans: The 3Ps Progress, problems, and prospects. Sci Total Environ. 2023;856(Pt 2):159164. 10.1016/j.scitotenv.2022.159164 . Epub 2022 Oct 3. PMID: 36195147. Fuller R, Landrigan PJ, Balakrishnan K, Bathan G, Bose-O'Reilly S, Brauer M, Caravanos J, Chiles T, Cohen A, Corra L, Cropper M, Ferraro G, Hanna J, Hanrahan D, Hu H, Hunter D, Janata G, Kupka R, Lanphear B, Lichtveld M, Martin K, Mustapha A, Sanchez-Triana E, Sandilya K, Schaefli L, Shaw J, Seddon J, Suk W, Téllez-Rojo MM, Yan C. Pollution and health: a progress update. Lancet Planet Health. 2022;6(6):e535-e547. 10.1016/S2542-5196(22)00090-0 . Epub 2022 May 18. Erratum in: Lancet Planet Health. 2022;6(7):e553. doi: 10.1016/S2542-5196(22)00145-0. PMID: 35594895; PMCID: PMC11995256. Ebrahimi P, Abbasi S, Pashaei R, Bogusz A, Oleszczuk P. Investigating impact of physicochemical properties of microplastics on human health: A short bibliometric analysis and review. Chemosphere. 2022;289:133146. 10.1016/j.chemosphere.2021.133146 . Epub 2021 Dec 3. PMID: 34871607. Niccolai E, Colzi I, Amedei A. Adverse Effects of Micro- and Nanoplastics on Humans and the Environment. Int J Mol Sci. 2023;24(21):15822. 10.3390/ijms242115822 . PMID: 37958802; PMCID: PMC10647433. Wang YL, Lee YH, Chiu IJ, Lin YF, Chiu HW. Potent Impact of Plastic Nanomaterials and Micromaterials on the Food Chain and Human Health. Int J Mol Sci. 2020;21(5):1727. 10.3390/ijms21051727 . PMID: 32138322; PMCID: PMC7084205. German Federal Institute for Risk Assessment (BfR) Department of Food Safety, Unit Effect-Based Analytics and Toxicogenomics Unit and Nanotoxicology Junior Research Group, Berlin, Germany, Shopova S, Sieg H, Braeuning A. Risk assessment and toxicological research on micro- and nanoplastics after oral exposure via food products. EFSA J. 2020;18((Suppl. S1)):e181102. doi: 10.2903/j.efsa.2020.e181102. [DOI] [PMC free article] [PubMed] [Google Scholar]. Wang C, Zhao J, Xing B. Environmental source, fate, and toxicity of microplastics. J Hazard Mater. 2021;407:124357. 10.1016/j.jhazmat.2020.124357 . Epub 2020 Oct 22. PMID: 33158648. Zuccarello P, Ferrante M, Cristaldi A, Copat C, Grasso A, Sangregorio D, Fiore M, Oliveri Conti G. Exposure to microplastics (< 10 µm) associated to plastic bottles mineral water consumption: The first quantitative study. Water Res. 2019;157:365–71. Epub 2019 Mar 29. PMID: 30974285. Li X, Lin L, Pang L, Pu K, Fu J, Shen Y, Zhang W, Xu H, Niu Y. Application and development trends of network toxicology in the safety assessment of traditional Chinese medicine. J Ethnopharmacol. 2025;343:119480. doi: 10.1016/j.jep.2025.119480. Epub 2025 Feb 11. PMID: 39947372. Zhang X, Zhu X, Gu W, Li X, Niu T, Mao P, Yuan H. Elucidating the mechanism of phthalates induced osteoporosis through network toxicology and molecular docking. Ecotoxicol Environ Saf. 2025;291:117820. 10.1016/j.ecoenv.2025.117820 . Epub 2025 Jan 30. PMID: 39884015. Nivedita J, Megha P, Daisy PS, Sivachandran R, Ramprasath A, Rajkumar M, Anitha TS. Microplastics and Cancer: A Comprehensive Review of Their Impact on Tumor Progression and Mechanisms of Carcinogenesis. J Environ Pathol Toxicol Oncol. 2025;44(4):31–46. 10.1615/JEnvironPatholToxicolOncol.2025054888 . PMID: 41213061. Casella C, Dondi D, Vadivel D. Do microplastics (MPs) and nanoplastics (NPs) directly contribute to human carcinogenesis? Environ Pollut. 2026;388:127343. 10.1016/j.envpol.2025.127343 . Epub 2025 Nov 1. PMID: 41183647. Silva GCA, Fioretto MN, Lima MC, Scarano WR, Delella FK. Micro and nanoplastics in human carcinogenesis: Insights from in vitro studies. Toxicology. 2026;519:154331. 10.1016/j.tox.2025.154331 . Epub 2025 Nov 1. PMID: 41177257. Hasan GM, Mohammad T, Zaidi S, Shamsi A, Hassan MI. Molecular docking and dynamics in protein serine/threonine kinase drug discovery: advances, challenges, and future perspectives. Front Pharmacol. 2025;16:1696204. 10.3389/fphar.2025.1696204 . PMID: 41221048; PMCID: PMC12598042. Sheng Z, Beck P, Gabby M, Habte-Mariam S, Mitkos K. Molecular Basis of Oncogenic PI3K Proteins. Cancers (Basel). 2024;17(1):77. 10.3390/cancers17010077 . PMID: 39796708; PMCID: PMC11720314. Zhang H, Liu Y, Liu J, Chen J, Wang J, Hua H, Jiang Y. cAMP-PKA/EPAC signaling and cancer: the interplay in tumor microenvironment. J Hematol Oncol. 2024;17(1):5. 10.1186/s13045-024-01524-x . PMID: 38233872; PMCID: PMC10792844. Levantini E, Maroni G, Del Re M, Tenen DG. EGFR signaling pathway as therapeutic target in human cancers. Semin Cancer Biol. 2022;85:253–75. 10.1016/j.semcancer.2022.04.002 . Epub 2022 Apr 12. PMID: 35427766. O'Callaghan L, Olsen M, Tajouri L, Beaver D, Hudson C, Alghafri R, McKirdy S, Goldsworthy A. Plastic induced urinary tract disease and dysfunction: a scoping review. J Expo Sci Environ Epidemiol. 2025;35(5):770–84. 10.1038/s41370-024-00709-3 . Epub 2024 Aug 31. PMID: 39217203; PMCID: PMC12401723. Zeng S, Guo H. Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy. Sci Rep. 2025;15(1):19547. 10.1038/s41598-025-03285-0 . PMID: 40467639; PMCID: PMC12137637. Ohbayashi N, Murayama K, Kato-Murayama M, Kukimoto-Niino M, Uejima T, Matsuda T, Ohsawa N, Yokoyoma S, Nojima H, Shirouzu M. Structural Basis for the Inhibition of Cyclin G-Associated Kinase by Gefitinib. ChemistryOpen. 2018;7(9):721–7. 10.1002/open.201800177 . PMID: 30214852; PMCID: PMC6129943. Patel SH, Metcalf M, Bivalacqua TJ, Kates M. Plastic exposure and urological malignancies - an emerging field. Nat Rev Urol. 2020;17(12):653–654. 10.1038/s41585-020-00385-6 . PMID: 33028980. Zhang K, Chen D, Ma K, Wu X, Hao H, Jiang S. NAD(P)H:Quinone Oxidoreductase 1 (NQO1) as a Therapeutic and Diagnostic Target in Cancer. J Med Chem. 2018;61(16):6983–7003. 10.1021/acs.jmedchem.8b00124 . Epub 2018 May 7. PMID: 29712428. Boison D, Yegutkin GG. Adenosine Metabolism: Emerging Concepts for Cancer Therapy. Cancer Cell. 2019;36(6):582–96. 10.1016/j.ccell.2019.10.007 . PMID: 31821783; PMCID: PMC7224341. Zhulai G, Oleinik E, Shibaev M, Ignatev K. Adenosine-Metabolizing Enzymes, Adenosine Kinase and Adenosine Deaminase, in Cancer. Biomolecules. 2022;12(3):418. 10.3390/biom12030418 . PMID: 35327609; PMCID: PMC8946555. Ohta A, Gorelik E, Prasad SJ, Ronchese F, Lukashev D, Wong MK, Huang X, Caldwell S, Liu K, Smith P, Chen JF, Jackson EK, Apasov S, Abrams S, Sitkovsky M. A2A adenosine receptor protects tumors from antitumor T cells. Proc Natl Acad Sci U S A. 2006;103(35):13132–7. 10.1073/pnas.0605251103 . Epub 2006 Aug 17. PMID: 16916931; PMCID: PMC1559765. Boswell-Smith V, Spina D, Page CP. Phosphodiesterase inhibitors. Br J Pharmacol. 2006;147(1):S252–7. 10.1038/sj.bjp.0706495 . PMID: 16402111; PMCID: PMC1760738. Wang K, Xie CJ, Ding Z, Shan T, Zhong Z, Yuan FL, Wu JJ, Yuan ZD, Qian C, Yu L, Liu Y, Cheng J, Zhang QL, Liu W, Zhao Y, Huang J, Zhang WZ, Yin Q, Gao BY, Hou JQ, Wang JC, Mei J, Deng C. PDE5A + cancer-associated fibroblasts enhance immune suppression in gastric cancer. Gut 2025 Oct 20:gutjnl–2025. 10.1136/gutjnl-2025-335794 . Epub ahead of print. PMID: 41115748. Cheng C, Zhang Z, Wang J, Wang C, Liu T, Yang C, Wang G, Huang H, Li Y. CircPGM5 regulates Foxo3a phosphorylation via MiR-21-5p/MAPK10 axis to inhibit bladder cancer progression. Cell Signal. 2024;121:111297. Epub 2024 Jul 14. PMID: 39004326. Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. Prog Med Chem. 2021;60:273–343. 10.1016/bs.pmch.2021.01.004 . Epub 2021 May 27. PMID: 34147204. Han Y, Zhang Z, Wang Z, Li Y, Chen G, Yi C, Wang R, Yu D, He Y. Integrated network toxicology, machine learning, molecular docking and experimental validation to elucidate mechanism of polyethylene terephthalate microplastics inducing periodontitis. Environ Int. 2025;203:109784. Epub 2025 Sep 9. PMID: 40945017. Chen Y, Zhang Z, Ji K, Zhang Q, Qian L, Yang C. Role of microplastics in the tumor microenvironment (Review). Oncol Lett. 2025;29(4):193. 10.3892/ol.2025.14939 . PMID: 40041410; PMCID: PMC11877014. Zhao J, Zhang H, Shi L, Jia Y, Sheng H. Detection and quantification of microplastics in various types of human tumor tissues. Ecotoxicol Environ Saf. 2024;283:116818. 10.1016/j.ecoenv.2024.116818 . Epub 2024 Jul 30. PMID: 39083862. Zeng S, Guo H. Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy. Sci Rep. 2025;15(1):19547. 10.1038/s41598-025-03285-0 . PMID: 40467639; PMCID: PMC12137637. Han Y, Zhang Z, Wang Z, Li Y, Chen G, Yi C, Wang R, Yu D, He Y. Integrated network toxicology, machine learning, molecular docking and experimental validation to elucidate mechanism of polyethylene terephthalate microplastics inducing periodontitis. Environ Int. 2025;203:109784. Epub 2025 Sep 9. PMID: 40945017. Additional Declarations No competing interests reported. Supplementary Files FigS1.tif 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-8349736","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582683389,"identity":"82fe6f56-46a6-4b54-894d-fe317fc87a43","order_by":0,"name":"Nanjie Li","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nanjie","middleName":"","lastName":"Li","suffix":""},{"id":582683390,"identity":"09c8dde5-27ce-4175-b2ae-0247cd97520e","order_by":1,"name":"Lu Ding","email":"","orcid":"","institution":"Ningxia Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Ding","suffix":""},{"id":582683391,"identity":"f75347dd-3997-4883-9226-4d038cd42d78","order_by":2,"name":"Hui Dong","email":"","orcid":"","institution":"Ningxia Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Dong","suffix":""},{"id":582683394,"identity":"60ef321a-1729-41a5-a5cf-5c369d2c336b","order_by":3,"name":"Xusheng Zhang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xusheng","middleName":"","lastName":"Zhang","suffix":""},{"id":582683397,"identity":"3c15f93e-e6e4-4b04-af16-2331cdb977d9","order_by":4,"name":"Jiawei Wang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Wang","suffix":""},{"id":582683399,"identity":"c924e6fc-1bc2-42d9-b492-79fc56ebf18b","order_by":5,"name":"Huifeng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACefnnxz98qLBhZmxvIFKLYUNOGuOMM2nszD0HiLXmQIIZM2/bYX72GQlE6mBsOJD2mLeNWZp35uONNxhqbKIJamFnbDxuOOccm7Hk7LRiC4ZjabkNBG1pZkiQeFPGk2w4O8dMgrHhMGEtDMcYDCR42CTq9988Q6yWMwxmkjxtBsyMM3iI1GI4A+imGWcSmBl7gH5JIMYv8hLsBx98qPgPjMrDG298qLEhwmFIwEAigRTlEC2k6hgFo2AUjIKRAQAEfUBa06pCogAAAABJRU5ErkJggg==","orcid":"","institution":"Ningxia Medical University General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huifeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-12-13 03:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8349736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8349736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101752669,"identity":"6c917947-f8d1-49d5-aa2d-11e203546e8b","added_by":"auto","created_at":"2026-02-03 10:28:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193084,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DET-associated candidate targets in BLCA, PPI network and functional enrichment.\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram showing 86 intersecting genes between the predicted DET-related targets and BLCA-related genes.\u003c/p\u003e\n\u003cp\u003e(B) “DET–targets–BLCA” interaction network visualized in Cytoscape (DET and BLCA as phenotype nodes; genes as shared nodes).\u003c/p\u003e\n\u003cp\u003e(C) Bubble plots showing the top 10 enriched GO terms in BP, CC and MF categories.\u003c/p\u003e\n\u003cp\u003e(D) Top 10 significantly enriched KEGG pathways. Bubble size indicates gene count and color indicates statistical significance.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349736/v1/e830c38b8e9cea619eb2d8b0.jpg"},{"id":101630870,"identity":"9536ff65-e74e-44d2-912d-131b1e596325","added_by":"auto","created_at":"2026-02-02 05:26:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218820,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of key prognostic genes, construction of the risk score model, and clinical prediction evaluation.\u003c/p\u003e\n\u003cp\u003e(A) Univariate Cox regression results for the candidate common targets (forest plot or bar chart displaying significant genes).\u003c/p\u003e\n\u003cp\u003e(B) Trajectory plot of LASSO regression coefficients.\u003c/p\u003e\n\u003cp\u003e(C) Cross-validation curve to determine the optimal λ.\u003c/p\u003e\n\u003cp\u003e(D) Multivariate Cox regression screening identifying 6 key genes and construction of the risk score model.\u003c/p\u003e\n\u003cp\u003e(E) Kaplan–Meier overall survival curves grouped by median risk score.\u003c/p\u003e\n\u003cp\u003e(F) Time-dependent ROC curves evaluating 1-/3-/5-year survival prediction performance.\u003c/p\u003e\n\u003cp\u003e(G) Univariate/Multivariate Cox regression incorporating clinical variables (age, gender, TNM stage, etc.) validating the independent prognostic value of the risk score.\u003c/p\u003e\n\u003cp\u003e(H) Nomogram and calibration curves: Predicting 1-/3-/5-year survival probabilities based on the risk score (and clinical variables, if included) and assessing prediction consistency.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349736/v1/7f8c1faba33deb692535375a.jpg"},{"id":101630871,"identity":"9ad008f7-f73b-486f-9520-31819cad5e5c","added_by":"auto","created_at":"2026-02-02 05:26:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372274,"visible":true,"origin":"","legend":"\u003cp\u003eRisk stratification and correlation analysis of key genes with immune infiltration.\u003c/p\u003e\n\u003cp\u003e(A) Differences in the infiltration proportions of 22 immune cell types between the high-risk and low-risk groups (box plots/violin plots; significance indicated by asterisks).\u003c/p\u003e\n\u003cp\u003e(B–G) Lollipop plots showing the Spearman correlation between the expression of key genes (NQO1, AKR1B1, ADK, GAK, PDE5A, MAPK10) and the infiltration of 22 immune cell types; dot size indicates the absolute value of the correlation coefficient, and color indicates statistical significance (e.g., P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349736/v1/d4fde8780fc1717690b7c5dc.jpg"},{"id":101630872,"identity":"72e0d6ec-342a-4892-9879-e39dd7934c30","added_by":"auto","created_at":"2026-02-02 05:26:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":386874,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results of DET with key target proteins.\u003c/p\u003e\n\u003cp\u003e(A–F) Docking conformations of DET with ADK, GAK, MAPK10, NQO1, PDE5A, and AKR1B1, respectively. Each panel can sequentially display: protein surface and ligand position (left), enlarged view of the binding pocket with key amino acid residue interactions (middle), and a 2D interaction diagram (right). Corresponding binding energies (kcal/mol): ADK −6.1, GAK −6.3, MAPK10 −6.4, NQO1 −5.9, PDE5A −7.4, AKR1B1 −7.9.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349736/v1/3a04c8d00464b58946dad430.jpg"},{"id":104835301,"identity":"b105fa40-c36b-4692-8dfb-86de306a1558","added_by":"auto","created_at":"2026-03-17 17:43:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1980799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8349736/v1/67b1c560-f699-4e26-a383-c524aef681e2.pdf"},{"id":101630873,"identity":"5f97af39-c2ab-470b-a551-d6bedc2b0b29","added_by":"auto","created_at":"2026-02-02 05:26:34","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8282475,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8349736/v1/ceeb1de6c318bcc5ecdf86d1.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Network Toxicology and Molecular Docking Elucidate the Molecular Mechanisms and Immune Implications of Diethyl Terephthalate in Bladder Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe profound influence of human activities on the Earth system has led to the concept of the \u0026ldquo;Anthropocene\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In this context, plastics have been produced and used at an unprecedented scale because of their low weight, durability, low cost, and broad applicability, thereby becoming an increasingly important source of environmental exposure [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Common plastic polymers include polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), polycarbonate (PC), polyvinyl chloride (PVC), and polystyrene (PS) [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Across their production\u0026ndash;use\u0026ndash;disposal/recycling lifecycle, plastics can continuously release contaminants, including polymer fragments and chemical additives. Microplastics (MPs; \u0026lt;5 mm) have attracted particular attention due to their environmental persistence, widespread distribution, and potential for bioaccumulation [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have detected MPs in multiple human specimens, including placenta, lung, and liver tissues, as well as sputum, breast milk, feces, urine, and blood [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings indicate that MPs and plastic-associated chemicals can enter the body via ingestion, inhalation, or dermal contact and may accumulate over time. Consistent with this exposure potential, experimental evidence suggests that plastic-related exposures can elicit inflammation, oxidative stress, and immune dysregulation\u0026mdash;processes implicated in several hallmarks of tumorigenesis [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong potential health outcomes linked to plastic-related exposures, urological malignancies warrant close attention. Bladder cancer (BLCA) is a common urological cancer characterized by complex gene-regulatory programs and tumor microenvironment (TME) remodeling, including dysregulated signaling, metabolic reprogramming, and immune evasion [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although environmental exposures are recognized as important exogenous contributors to BLCA risk and prognosis, systematic molecular investigations connecting specific plastic-associated chemicals to BLCA remain limited [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiethyl terephthalate (DET), a terephthalate diester, can serve as a representative candidate chemical within PET-related systems for computational toxicology analyses. However, the molecular mechanisms potentially linking DET exposure to BLCA have not been comprehensively characterized, and candidate targets for downstream validation remain insufficiently defined. Recent advances in network toxicology and computational biology enable integrative frameworks that combine target prediction, transcriptomic profiling, statistical modeling, and molecular docking to interrogate chemical\u0026ndash;disease associations at a systems level [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, this study investigated DET using an integrated pipeline encompassing target prediction, intersection with disease genes, network and pathway analyses, prognostic modeling, immune infiltration assessment, and molecular docking. Specifically, we: (1) identified putative DET targets and intersected them with BLCA-related genes to define candidate shared targets; (2) characterized potential biological functions and signaling pathways using protein\u0026ndash;protein interaction (PPI) and enrichment analyses; (3) constructed and evaluated a prognostic risk model in the TCGA-BLCA cohort; (4) quantified associations between key genes and immune cell infiltration patterns; and (5) performed molecular docking to provide structural-level computational evidence for potential ligand\u0026ndash;target interactions. Collectively, this work aims to generate testable hypotheses regarding the DET-related target network in BLCA and to nominate candidates for subsequent mechanistic validation and translational investigation.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Acquisition of Potential Toxicity Targets and Disease-Related Genes\u003c/h2\u003e \u003cp\u003ePotential human targets of DET were predicted using SwissTargetPrediction by inputting the SMILES string (CCOC(=\u0026thinsp;O)C1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;C1)C(=\u0026thinsp;O)OCC). After mapping to gene symbols and removing duplicates, 115 unique genes were obtained. BLCA-related genes were retrieved from OMIM, TTD, and GeneCards using the keyword \u0026ldquo;bladder cancer\u0026rdquo;. All disease-associated genes were merged and deduplicated to generate a BLCA gene set. The intersection between predicted DET targets and the BLCA gene set was defined as the candidate shared target set for downstream analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Network construction\u003c/h2\u003e \u003cp\u003eA network was constructed to illustrate the potential linkages between DET exposure,\u003c/p\u003e \u003cp\u003etoxicological targets, and bladder cancer. The network was built using Cytoscape. Nodes\u003c/p\u003e \u003cp\u003erepresented DET, toxicological targets, and bladder cancer, while edges represented the\u003c/p\u003e \u003cp\u003erelationships between them\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 GO and KEGG Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the candidate shared target set. Multiple testing was controlled using the Benjamini\u0026ndash;Hochberg procedure, and an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification of Prognostic Genes and Construction of the Risk Score Model\u003c/h2\u003e \u003cp\u003eTranscriptomic profiles and clinical follow-up information (overall survival time and status) for TCGA-BLCA were obtained and preprocessed (sample matching, missing-value handling, and expression normalization). Using the candidate shared target set as the initial gene pool, univariate Cox proportional hazards regression was performed to screen prognostic genes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Significant candidates were then subjected to LASSO regression to reduce collinearity and overfitting, with the optimal penalty parameter (λ) selected by 10-fold cross-validation. Genes retained after LASSO were entered into a multivariable Cox model to derive regression coefficients (β). The risk score was calculated as:\u003c/p\u003e \u003cp\u003eRisk score\u0026thinsp;=\u0026thinsp;Σ(expression_i\u0026thinsp;\u0026times;\u0026thinsp;β_i).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Model Validation and Nomogram Evaluation\u003c/h2\u003e \u003cp\u003ePatients were stratified into high-risk and low-risk groups based on the median risk score. Kaplan\u0026ndash;Meier survival analysis with the log-rank test was used to evaluate the stratification capability. Time-dependent ROC analysis was employed to assess the model's discriminative ability for 1-, 3-, and 5-year survival outcomes, and AUC values were calculated. Univariate and multivariate Cox regressions incorporating clinical variables (e.g., age, gender, TNM stage) were performed to evaluate the independent prognostic value of the risk score. Based on this, a nomogram was constructed to predict individualized 1-, 3-, and 5-year survival probabilities, and calibration curves were used to evaluate prediction consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immune Cell Infiltration Analysis\u003c/h2\u003e \u003cp\u003eCIBERSORT was applied to deconvolute the BLCA transcriptomic data to estimate the relative infiltration proportions of 22 immune cell types; only samples with a CIBERSORT output P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were retained for subsequent analysis. If the analysis involved multi-cohort or multi-batch data, the ComBat method was used for batch effect correction; if only a single-source cohort was used, no batch correction was performed, as stated in the methods. Subsequently, Spearman correlation analysis was used to assess the association between the expression levels of key genes and immune cell infiltration proportions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular Docking\u003c/h2\u003e \u003cp\u003eThree-dimensional structures of key target proteins were obtained from the Protein Data Bank (PDB), and the DET molecular structure was obtained from PubChem. Molecular docking was performed using AutoDock Vina: receptor and ligand structures underwent preprocessing (e.g., removal of extraneous ligands/water molecules, addition of hydrogen atoms, assignment of charges, format conversion), a grid box was set to cover the potential binding pocket, representative binding conformations and predicted binding energies were output, and PyMOL was used to visualize the interaction patterns. To ensure reproducibility, key parameters such as PDB IDs, grid center coordinates and size, and exhaustiveness are summarized in the supplementary materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted in R 4.5.2 unless otherwise specified. All tests were two-sided. For continuous variables and correlation analyses, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. When multiple comparisons were performed, false discovery rate (FDR) correction was applied. Survival analyses were conducted using Cox regression and Kaplan\u0026ndash;Meier methods. Predictive performance was evaluated using time-dependent ROC analyses and calibration assessments.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification of PET Exposure and Bladder Cancer-Related Target Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the potential molecular link between PET exposure and BLCA, we performed a Venn diagram analysis comparing 115 DET-related targets with 15,969 BLCA-related differentially expressed genes (DEGs), identifying a total of 86 overlapping genes. These genes may serve as key molecular targets through which long-term PET exposure participates in the pathogenesis of BLCA. Furthermore, we used Cytoscape for visualization, constructing a \u0026quot;DET-Toxicity Targets-Bladder Cancer\u0026quot; interaction network. In this network, DET acts as a central node connected to multiple toxicological targets, which in turn establish associations with bladder cancer-related genes, revealing that DET may synergistically participate in bladder carcinogenesis through multi-target interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Functional Enrichment Analysis Reveals Significant Enrichment in Signal Transduction and Tumor-Related Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO and KEGG enrichment analyses were conducted for the 86 shared targets. GO terms were significantly enriched in biological processes such as \u003cem\u003ecellular response to chemical stress\u003c/em\u003e, \u003cem\u003epositive regulation of protein phosphorylation\u003c/em\u003e, and \u003cem\u003epeptidyl-serine phosphorylation\u003c/em\u003e, indicating that these targets are closely associated with phosphorylation control and signal transduction. KEGG analysis further demonstrated significant enrichment in tumor-associated pathways, including \u003cem\u003ethe cAMP signaling pathway\u003c/em\u003e and \u003cem\u003ethe ErbB signaling pathway\u003c/em\u003e. In addition, enrichment of \u0026ldquo;\u003cem\u003eChemical carcinogenesis - ROS\u003c/em\u003e\u0026rdquo;\u0026nbsp;suggested potential involvement of oxidative stress and chemical carcinogenesis-related processes. Collectively, these results provide systems-level support for an association between the DET-related target network and key signaling programs implicated in BLCA.\u003c/p\u003e\n\u003cp\u003eFigure 1. Identification of DET-associated candidate targets in BLCA, PPI network and functional enrichment.\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram showing 86 intersecting genes between the predicted DET-related targets and BLCA-related genes.\u003c/p\u003e\n\u003cp\u003e(B) \u0026ldquo;DET\u0026ndash;targets\u0026ndash;BLCA\u0026rdquo; interaction network visualized in Cytoscape (DET and BLCA as phenotype nodes; genes as shared nodes).\u003c/p\u003e\n\u003cp\u003e(C) Bubble plots showing the top 10 enriched GO terms in BP, CC and MF categories.\u003c/p\u003e\n\u003cp\u003e(D) Top 10 significantly enriched KEGG pathways. Bubble size indicates gene count and color indicates statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Screening of Key Prognostic Genes and Construction of the Risk Score Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression was applied to the 86 shared targets, identifying 22 genes significantly associated with overall survival (P \u0026lt; 0.05). These candidates were then subjected to LASSO regression for further feature reduction, and multivariate Cox regression ultimately retained six key prognostic genes (NQO1, AKR1B1, ADK, GAK, PDE5A, and MAPK10). A risk score model was established using these six genes, and patients were stratified into high- and low-risk groups according to the median risk score. Kaplan\u0026ndash;Meier analysis demonstrated significantly poorer overall survival in the high-risk group compared with the low-risk group (P \u0026lt; 0.001). Time-dependent ROC analysis suggested moderate predictive performance for 1-, 3-, and 5-year survival, with AUC values of 0.675, 0.658, and 0.651, respectively. A nomogram integrating the risk score with clinical characteristics further showed general concordance between predicted and observed survival probabilities based on calibration curves, indicating potential utility for individualized outcome estimation.\u003c/p\u003e\n\u003cp\u003eFigure 2 Screening of key prognostic genes, construction of the risk score model, and clinical prediction evaluation.\u003c/p\u003e\n\u003cp\u003e(A) Univariate Cox regression results for the candidate common targets (forest plot or bar chart displaying significant genes).\u003c/p\u003e\n\u003cp\u003e(B) Trajectory plot of LASSO regression coefficients.\u003c/p\u003e\n\u003cp\u003e(C) Cross-validation curve to determine the optimal \u0026lambda;.\u003c/p\u003e\n\u003cp\u003e(D) Multivariate Cox regression screening identifying 6 key genes and construction of the risk score model.\u003c/p\u003e\n\u003cp\u003e(E) Kaplan\u0026ndash;Meier overall survival curves grouped by median risk score.\u003c/p\u003e\n\u003cp\u003e(F) Time-dependent ROC curves evaluating 1-/3-/5-year survival prediction performance.\u003c/p\u003e\n\u003cp\u003e(G) Univariate/Multivariate Cox regression incorporating clinical variables (age, gender, TNM stage, etc.) validating the independent prognostic value of the risk score.\u003c/p\u003e\n\u003cp\u003e(H) Nomogram and calibration curves: Predicting 1-/3-/5-year survival probabilities based on the risk score (and clinical variables, if included) and assessing prediction consistency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Correlation Between Key Gene Expression and Immune Infiltration Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCIBERSORT was used to estimate the relative fractions of 22 immune cell types, followed by correlation analyses. Overall, the six prognostic genes exhibited significant relationships with multiple immune cell subsets. Specifically, NQO1 showed positive correlations with resting mast cells and resting dendritic cells, but negative correlations with CD8⁺\u0026nbsp;T cell infiltration and activated mast cells. AKR1B1 was positively associated with M2 macrophages and negatively associated with M1 macrophages. ADK displayed a positive correlation with activated dendritic cells, whereas GAK correlated positively with neutrophil infiltration. PDE5A was positively correlated with resting memory CD4⁺\u0026nbsp;T cells and resting mast cells but negatively correlated with resting NK cells, activated memory CD4⁺\u0026nbsp;T cells, and CD8⁺\u0026nbsp;T cell infiltration. MAPK10 was positively related to activated dendritic cells, monocytes, mast cells, and resting memory CD4⁺\u0026nbsp;T cells, while showing negative associations with CD8⁺\u0026nbsp;T cell infiltration, resting NK cells, activated mast cells, and activated memory CD4⁺\u0026nbsp;T cells. Collectively, these findings suggest that the DET-associated prognostic gene network may be linked to the immune infiltration landscape in BLCA, thereby offering candidate directions for mechanistic investigation of immune microenvironment regulation.\u003c/p\u003e\n\u003cp\u003eFigure 3 Risk stratification and correlation analysis of key genes with immune infiltration.\u003c/p\u003e\n\u003cp\u003e(A) Differences in the infiltration proportions of 22 immune cell types between the high-risk and low-risk groups (box plots/violin plots; significance indicated by asterisks).\u003c/p\u003e\n\u003cp\u003e(B\u0026ndash;G) Lollipop plots showing the Spearman correlation between the expression of key genes (NQO1, AKR1B1, ADK, GAK, PDE5A, MAPK10) and the infiltration of 22 immune cell types; dot size indicates the absolute value of the correlation coefficient, and color indicates statistical significance (e.g., P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Molecular Docking Results of DET with Key Target Proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess potential ligand\u0026ndash;protein interactions at the structural level, DET was docked to the six key target proteins. Docking results indicated that DET adopted feasible conformations within predicted binding pockets for all targets, with binding energies below\u0026nbsp;\u0026minus;5 kcal/mol. Among the targets, AKR1B1 exhibited the strongest predicted affinity (\u0026minus;7.9 kcal/mol), followed by NQO1 (\u0026minus;5.9), GAK (\u0026minus;6.3), ADK (\u0026minus;6.1), PDE5A (\u0026minus;7.4), and MAPK10 (\u0026minus;6.4). These results support the possibility of DET binding to these proteins; nonetheless, docking constitutes computational evidence only, and biological relevance requires further confirmation through molecular dynamics simulations and in vitro binding and functional assays.\u003c/p\u003e\n\u003cp\u003eFigure 4 Molecular docking results of DET with key target proteins.\u003c/p\u003e\n\u003cp\u003e(A\u0026ndash;F) Docking conformations of DET with ADK, GAK, MAPK10, NQO1, PDE5A, and AKR1B1, respectively. Each panel can sequentially display: protein surface and ligand position (left), enlarged view of the binding pocket with key amino acid residue interactions (middle), and a 2D interaction diagram (right). Corresponding binding energies (kcal/mol): ADK\u0026nbsp;\u0026minus;6.1, GAK\u0026nbsp;\u0026minus;6.3, MAPK10\u0026nbsp;\u0026minus;6.4, NQO1\u0026nbsp;\u0026minus;5.9, PDE5A\u0026nbsp;\u0026minus;7.4, AKR1B1\u0026nbsp;\u0026minus;7.9.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe potential link between microplastic (MP) exposure and tumorigenesis has attracted increasing scientific interest. MPs are thought to promote tumor progression through mechanisms such as oxidative stress, inflammation, and endocrine disruption. Previous studies indicate that MPs can induce hepatotoxicity and carcinogenesis, contribute to endocrine dysregulation in cancers such as those of the breast and cervix, and correlate with neurotoxicity and brain malignancies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Accordingly, contemporary research increasingly integrates epidemiological evidence with in vitro and in vivo findings to delineate molecular pathways that may connect MPs to cancer outcomes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Mechanistically, MPs may activate signaling cascades that enhance cell proliferation and tumor-specific invasion, thereby fostering a tumor-promoting microenvironment. Moreover, MP-associated carcinogenesis may involve biomarkers indicative of both plastic exposure and tumor progression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Despite these advances, the mechanistic relationship between diethyl terephthalate (DET) and bladder cancer remains poorly characterized. Therefore, the present study employed a network toxicology framework to investigate molecular associations between DET exposure and the development, progression, and prognosis of bladder cancer.\u003c/p\u003e \u003cp\u003eEnrichment analyses of DET-related toxicological targets differentially expressed in BLCA revealed several pathways central to BLCA biology. Notably, significant enrichment was observed for serine/threonine kinases, phosphatidylinositol kinases, the cAMP signaling pathway, and the ErbB signaling pathway. Serine/threonine kinases propagate signals through phosphorylation of downstream substrates, regulating diverse cellular processes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Phosphatidylinositol kinases modulate phosphatidylinositol metabolism to generate second messengers, thereby governing key cellular functions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Dysregulated cAMP signaling has been implicated in metabolic reprogramming in cancer cells, potentially promoting proliferation and invasion [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Aberrant ErbB signaling contributes to multiple malignancies and is highly relevant to oncogenic processes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Consistent with these observations, MPs have been reported to exert cytotoxic effects in urinary tract-related cell lines by increasing inflammatory responses, reducing viability, and perturbing pathways such as mitogen-activated protein kinase (MAPK) signaling [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although direct evidence linking DET to bladder cancer remains limited, its polymer PET has been shown to disrupt key molecular and cellular pathways in diabetic nephropathy, impairing the regulation of apoptosis, immune responses, and cellular homeostasis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Together, these findings support the hypothesis that DET-associated chemical exposure may contribute to BLCA initiation and progression by modulating growth-related signaling and amplifying proliferative programs.\u003c/p\u003e \u003cp\u003eTo further explore DET\u0026ndash;BLCA molecular connections, we identified six hub genes: GAK, NQO1, AKR1B1, ADK, PDE5A, and MAPK10. GAK is a cyclin-dependent serine/threonine protein kinase implicated in cell cycle progression and cancer development [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. NQO1 exhibits context-dependent (\u0026ldquo;Janus\u0026rdquo;) functions, is upregulated in multiple solid tumors, and higher expression is associated with poorer prognosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. AKR1B1-mediated endogenous fructose metabolism can promote proliferation and migration through regulation of cyclins and the RhoA\u0026ndash;ROCK2 axis, thereby facilitating tumorigenesis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Tumor cell survival and expansion are closely linked to adenosine metabolism [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As a regulator of adenosine levels, ADK may promote oncogenic progression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and influence immune responses via overactivation of the Gs protein-coupled A2A receptor on immune cells [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. PDE5A hydrolyzes cyclic guanosine monophosphate (cGMP), a key intracellular second messenger [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In gastric cancer, PDE5A-positive cancer-associated fibroblasts (CAFs) have been reported to promote epithelial\u0026ndash;mesenchymal transition by remodeling the extracellular matrix and to activate PI3K/AKT/mTOR signaling, inducing CXCL12 release; CXCL12 subsequently binds CXCR4, recruits CD8⁺ exhausted T cells (TEX LAG3⁺ T cells), and contributes to an immunosuppressive TME [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. MAPK10, a member of the MAPK pathway, has been reported to function as a tumor suppressor when activated during bladder cancer progression [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Molecular docking is widely used to predict binding modes and affinity, supporting target identification in drug discovery and materials research [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Prior docking studies have reported high-affinity binding between DET and core targets [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In our docking analyses, DET exhibited feasible binding conformations at the active sites of the six hub proteins, with favorable predicted affinities. These results suggest that DET may influence BLCA through these targets. Notably, the six hub genes also demonstrated relatively high AUC values, supporting their potential diagnostic relevance and clinical applicability.\u003c/p\u003e \u003cp\u003eMPs can shape the tumor microenvironment (TME), thereby promoting tumor progression [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In pancreatic cancer, MP-infiltrated tumor immune microenvironment (TIME) has been associated with decreased CD8⁺ T cells, natural killer cells, and dendritic cells, alongside increased neutrophil infiltration [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], underscoring a role for MPs in shaping immune contexture. Consistent with this, our immune infiltration analyses suggest that DET exposure may remodel the BLCA immune microenvironment by modulating immune cell recruitment and activation. Specifically, NQO1 and MAPK10 may be involved in dendritic cell, mast cell, and CD8⁺ T cell responses; MAPK10 may negatively regulate neutrophil abundance; PDE5A may modulate CD4⁺ T cells, CD8⁺ T cells, mast cells, and natural killer cells; ADK may promote dendritic cell activation; and AKR1B1 appears primarily associated with M1/M2 macrophage polarization and may facilitate immune escape. Although direct evidence linking DET to the tumor immune microenvironment remains limited, exposure to its polymer PET in diabetic nephropathy has been associated with reduced CD8⁺ T cells, monocytes, and neutrophils, and increased Tregs and M2 macrophages [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In periodontitis, PET has been reported to affect immune-related pathways including C-type lectin receptor signaling, VEGF receptor signaling, and TNF signaling [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Collectively, these observations suggest that DET-related exposures may be closely linked to immune microenvironment regulation, potentially representing a mechanistic axis contributing to bladder cancer progression.\u003c/p\u003e \u003cp\u003eIt is important to note that molecular docking does not imply stable in vivo binding or confirm pathogenic effects. Actual biological outcomes may depend on DET exposure levels, metabolic transformation, tissue distribution, target accessibility, and co-exposure to other environmental factors. To strengthen the evidentiary chain, future work should prioritize: (1) exposure assessment, including measurement of DET or its metabolites (e.g., serum/urine biomarkers) in population samples and correlation with clinical outcomes and molecular phenotypes; (2) mechanistic validation via controlled-dose exposure experiments in bladder epithelial/tumor and immune cell systems to assess oxidative stress, inflammatory signaling, and immune-regulatory pathways; and (3) causal inference and external validation using independent cohorts and multi-omics integration (transcriptomics, proteomics, metabolomics, methylation) to evaluate the causal plausibility and translational potential of DET-related networks in BLCA progression.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, TCGA-BLCA is a retrospective tumor cohort lacking individual-level DET exposure measurements or systematic environmental/lifestyle covariates, which constrains causal inference and increases susceptibility to confounding and co-exposure effects. Second, the prognostic model and immune infiltration analyses were developed primarily within the same public cohort; external validation is necessary to assess generalizability. Third, CIBERSORT deconvolution relies on reference immune signatures and may be influenced by tumor purity, batch effects, and sample heterogeneity; future studies should employ multiple algorithms and/or experimental validation (e.g., flow cytometry, immunohistochemistry, spatial omics) for triangulation. Finally, docking provides static, structure-based predictions; molecular dynamics simulations and in vitro binding assays (e.g., SPR, MST) are recommended to validate binding stability and functional relevance.\u003c/p\u003e \u003cp\u003eOverall, this study proposes and evaluates an integrated analytical framework for interrogating systemic \u0026ldquo;environmental chemical\u0026ndash;tumor\u0026rdquo; associations. It identifies candidate shared targets between DET and BLCA, delineates key enriched signaling pathways, constructs a prognostic gene signature, and links these genes to immune microenvironment features. These findings provide actionable directions for mechanistic studies, functional validation, and biomarker development.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eUsing an integrated strategy combining network toxicology and bioinformatics, this study systematically identified overlap between predicted DET targets and the BLCA-related gene network, characterized the corresponding PPI network and functional enrichment patterns, and constructed a prognostic risk model comprising NQO1, AKR1B1, ADK, GAK, PDE5A, and MAPK10. Associations between key genes and the immune infiltration landscape were further delineated, and molecular docking provided computational support for potential DET binding to hub proteins. Given that the analyses relied primarily on public cohorts and in silico predictions, future work should integrate exposure measurements, experimental models, and external cohort validation to more rigorously assess causal plausibility and translational potential.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor’s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Nanjie Li,Jiawei Wang\u003c/p\u003e\n\u003cp\u003eData curation: Nanjie Li,Jiawei Wang\u003c/p\u003e\n\u003cp\u003eFormal analysis: Nanjie Li, Xusheng Zhang\u003c/p\u003e\n\u003cp\u003eMethodology: Nanjie Li,Jiawei Wang\u003c/p\u003e\n\u003cp\u003eProject administration: Hui Dong,Huifeng Wang\u003c/p\u003e\n\u003cp\u003eSoftware: Nanjie Li\u003c/p\u003e\n\u003cp\u003eSupervision: Lu Ding, Hui Dong\u003c/p\u003e\n\u003cp\u003eWriting – original draft: Nanjie Li，Xusheng Zhang\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: Lu Ding,Hui Dong,HuiFeng Wang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Ningxia Key R\u0026amp;D Program (2023BEG03033).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the https://zenodo.org/records/18195465.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTrischler H. The Anthropocene: A Challenge for the History of Science, Technology, and the Environment. NTM. 2016;24(3):309\u0026ndash;335. English. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00048-016-0146-3\u003c/span\u003e\u003cspan address=\"10.1007/s00048-016-0146-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 27567637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRangel-Buitrago N, Neal W, Williams A. The Plasticene: Time and rocks. Mar Pollut Bull. 2022;185(Pt B):114358. doi: 10.1016/j.marpolbul.2022.114358. Epub 2022 Nov 16. PMID: 36401945.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePesonen M, V\u0026auml;h\u0026auml;kangas K. Contribution of common plastic-related endocrine disruptors to epithelial-mesenchymal transition (EMT) and tumor progression. Chemosphere. 2022;309(Pt 1):136560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chemosphere.2022.136560\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2022.136560\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Sep 21. PMID: 36152835.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams AT, Rangel-Buitrago N. The past, present, and future of plastic pollution. Mar Pollut Bull. 2022;176:113429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.marpolbul.2022.113429\u003c/span\u003e\u003cspan address=\"10.1016/j.marpolbul.2022.113429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Feb 24. PMID: 35217417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun MY, Guo JY, Wang XY, Chang X. Sources and distributions of microplastics and the hazards to plants, animals and human health:A review. Ying Yong Sheng Tai Xue Bao. 2024;35(8):2301\u0026ndash;2312. English. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13287/j.1001-9332.202408.028\u003c/span\u003e\u003cspan address=\"10.13287/j.1001-9332.202408.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 39419815.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSangkham S, Faikhaw O, Munkong N, Sakunkoo P, Arunlertaree C, Chavali M, Mousazadeh M, Tiwari A. A review on microplastics and nanoplastics in the environment: Their occurrence, exposure routes, toxic studies, and potential effects on human health. Mar Pollut Bull. 2022;181:113832. Epub 2022 Jun 15. PMID: 35716489.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarcel\u0026oacute; D, Pic\u0026oacute; Y, Alfarhan AH, Microplastics. Detection in human samples, cell line studies, and health impacts. Environ Toxicol Pharmacol. 2023;101:104204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.etap.2023.104204\u003c/span\u003e\u003cspan address=\"10.1016/j.etap.2023.104204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2023 Jun 28. PMID: 37391049.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrzygoda-Kuś P, Kosiorowska KE, Urbanek AK, Mirończuk AM. Current Approaches to Microplastics Detection and Plastic Biodegradation. Molecules. 2025;30(11):2462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/molecules30112462\u003c/span\u003e\u003cspan address=\"10.3390/molecules30112462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 40509349; PMCID: PMC12156231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly 9WSL. FJ. Plastic and Human Health: A Micro Issue? Environ Sci Technol. 2017;51(12):6634\u0026ndash;6647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.est.7b00423\u003c/span\u003e\u003cspan address=\"10.1021/acs.est.7b00423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2017 Jun 7. PMID: 28531345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNingrum PT, Keman S, Sulistyorini L, Sudiana IK, Hidayat A, Negoro AHS, Junaidi H, Kustin K. A systematic review of the effects of airborne microplastic contamination on human lungs. Afr J Reprod Health. 2024;28(10s):430\u0026ndash;448. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.29063/ajrh2024/v28i10s.45\u003c/span\u003e\u003cspan address=\"10.29063/ajrh2024/v28i10s.45\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 39641437.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026auml;uerlein PS, Hofman-Caris RCHM, Pieke EN, Ter Laak TL. Fate of microplastics in the drinking water production. Water Res. 2022;221:118790. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.watres.2022.118790\u003c/span\u003e\u003cspan address=\"10.1016/j.watres.2022.118790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Jun 24. PMID: 35780766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Yang Y, Sun Y, Wei Z, Wang D, Chen S, Yang F, Wang J, Kang X. Assessing the toxicological impact of PET-MPs exposure on IVDD: Insights from network toxicology and molecular docking. J Environ Manage. 2025;373:123830. Epub 2024 Dec 29. PMID: 39736229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrata JC, da Costa JP, Lopes I, Duarte AC, Rocha-Santos T. Environmental exposure to microplastics: An overview on possible human health effects. Sci Total Environ. 2020;702:134455. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.scitotenv.2019.134455\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2019.134455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2019 Oct 4. PMID: 31733547.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYee MS, Hii LW, Looi CK, Lim WM, Wong SF, Kok YY, Tan BK, Wong CY, Leong CO. Impact of Microplastics and Nanoplastics on Human Health. Nanomaterials (Basel). 2021;11(2):496. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nano11020496\u003c/span\u003e\u003cspan address=\"10.3390/nano11020496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 33669327; PMCID: PMC7920297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Q, He Y, Cheng R, Li Q, Qian Z, Lin X. Recent advances in toxicological research and potential health impact of microplastics and nanoplastics in vivo. Environ Sci Pollut Res Int. 2022;29(27):40415\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11356-022-19745-3\u003c/span\u003e\u003cspan address=\"10.1007/s11356-022-19745-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Mar 26. PMID: 35347608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalafaia G, Barcel\u0026oacute; D. Microplastics in human samples: Recent advances, hot-spots, and analytical challenges. Trends Anal Chem. 2023;161:117016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.trac.2023.117016\u003c/span\u003e\u003cspan address=\"10.1016/j.trac.2023.117016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKutralam-Muniasamy G, Shruti VC, P\u0026eacute;rez-Guevara F, Roy PD. Microplastic diagnostics in humans: The 3Ps Progress, problems, and prospects. Sci Total Environ. 2023;856(Pt 2):159164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.scitotenv.2022.159164\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.159164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Oct 3. PMID: 36195147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuller R, Landrigan PJ, Balakrishnan K, Bathan G, Bose-O'Reilly S, Brauer M, Caravanos J, Chiles T, Cohen A, Corra L, Cropper M, Ferraro G, Hanna J, Hanrahan D, Hu H, Hunter D, Janata G, Kupka R, Lanphear B, Lichtveld M, Martin K, Mustapha A, Sanchez-Triana E, Sandilya K, Schaefli L, Shaw J, Seddon J, Suk W, T\u0026eacute;llez-Rojo MM, Yan C. Pollution and health: a progress update. Lancet Planet Health. 2022;6(6):e535-e547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2542-5196(22)00090-0\u003c/span\u003e\u003cspan address=\"10.1016/S2542-5196(22)00090-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 May 18. Erratum in: Lancet Planet Health. 2022;6(7):e553. doi: 10.1016/S2542-5196(22)00145-0. PMID: 35594895; PMCID: PMC11995256.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbrahimi P, Abbasi S, Pashaei R, Bogusz A, Oleszczuk P. Investigating impact of physicochemical properties of microplastics on human health: A short bibliometric analysis and review. Chemosphere. 2022;289:133146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chemosphere.2021.133146\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2021.133146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Dec 3. PMID: 34871607.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiccolai E, Colzi I, Amedei A. Adverse Effects of Micro- and Nanoplastics on Humans and the Environment. Int J Mol Sci. 2023;24(21):15822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms242115822\u003c/span\u003e\u003cspan address=\"10.3390/ijms242115822\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37958802; PMCID: PMC10647433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YL, Lee YH, Chiu IJ, Lin YF, Chiu HW. Potent Impact of Plastic Nanomaterials and Micromaterials on the Food Chain and Human Health. Int J Mol Sci. 2020;21(5):1727. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms21051727\u003c/span\u003e\u003cspan address=\"10.3390/ijms21051727\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32138322; PMCID: PMC7084205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerman Federal Institute for Risk Assessment (BfR) Department of Food Safety, Unit Effect-Based Analytics and Toxicogenomics Unit and Nanotoxicology Junior Research Group, Berlin, Germany, Shopova S, Sieg H, Braeuning A. Risk assessment and toxicological research on micro- and nanoplastics after oral exposure via food products. EFSA J. 2020;18((Suppl. S1)):e181102. doi: 10.2903/j.efsa.2020.e181102. [DOI] [PMC free article] [PubMed] [Google Scholar].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Zhao J, Xing B. Environmental source, fate, and toxicity of microplastics. J Hazard Mater. 2021;407:124357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhazmat.2020.124357\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2020.124357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Oct 22. PMID: 33158648.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuccarello P, Ferrante M, Cristaldi A, Copat C, Grasso A, Sangregorio D, Fiore M, Oliveri Conti G. Exposure to microplastics (\u0026lt;\u0026thinsp;10 \u0026micro;m) associated to plastic bottles mineral water consumption: The first quantitative study. Water Res. 2019;157:365\u0026ndash;71. Epub 2019 Mar 29. PMID: 30974285.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Lin L, Pang L, Pu K, Fu J, Shen Y, Zhang W, Xu H, Niu Y. Application and development trends of network toxicology in the safety assessment of traditional Chinese medicine. J Ethnopharmacol. 2025;343:119480. doi: 10.1016/j.jep.2025.119480. Epub 2025 Feb 11. PMID: 39947372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Zhu X, Gu W, Li X, Niu T, Mao P, Yuan H. Elucidating the mechanism of phthalates induced osteoporosis through network toxicology and molecular docking. Ecotoxicol Environ Saf. 2025;291:117820. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecoenv.2025.117820\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2025.117820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2025 Jan 30. PMID: 39884015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNivedita J, Megha P, Daisy PS, Sivachandran R, Ramprasath A, Rajkumar M, Anitha TS. Microplastics and Cancer: A Comprehensive Review of Their Impact on Tumor Progression and Mechanisms of Carcinogenesis. J Environ Pathol Toxicol Oncol. 2025;44(4):31\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1615/JEnvironPatholToxicolOncol.2025054888\u003c/span\u003e\u003cspan address=\"10.1615/JEnvironPatholToxicolOncol.2025054888\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 41213061.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasella C, Dondi D, Vadivel D. Do microplastics (MPs) and nanoplastics (NPs) directly contribute to human carcinogenesis? Environ Pollut. 2026;388:127343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.envpol.2025.127343\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2025.127343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2025 Nov 1. PMID: 41183647.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva GCA, Fioretto MN, Lima MC, Scarano WR, Delella FK. Micro and nanoplastics in human carcinogenesis: Insights from in vitro studies. Toxicology. 2026;519:154331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tox.2025.154331\u003c/span\u003e\u003cspan address=\"10.1016/j.tox.2025.154331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2025 Nov 1. PMID: 41177257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan GM, Mohammad T, Zaidi S, Shamsi A, Hassan MI. Molecular docking and dynamics in protein serine/threonine kinase drug discovery: advances, challenges, and future perspectives. Front Pharmacol. 2025;16:1696204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphar.2025.1696204\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2025.1696204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 41221048; PMCID: PMC12598042.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng Z, Beck P, Gabby M, Habte-Mariam S, Mitkos K. Molecular Basis of Oncogenic PI3K Proteins. Cancers (Basel). 2024;17(1):77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers17010077\u003c/span\u003e\u003cspan address=\"10.3390/cancers17010077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 39796708; PMCID: PMC11720314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Liu Y, Liu J, Chen J, Wang J, Hua H, Jiang Y. cAMP-PKA/EPAC signaling and cancer: the interplay in tumor microenvironment. J Hematol Oncol. 2024;17(1):5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13045-024-01524-x\u003c/span\u003e\u003cspan address=\"10.1186/s13045-024-01524-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 38233872; PMCID: PMC10792844.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevantini E, Maroni G, Del Re M, Tenen DG. EGFR signaling pathway as therapeutic target in human cancers. Semin Cancer Biol. 2022;85:253\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.semcancer.2022.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.semcancer.2022.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Apr 12. PMID: 35427766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Callaghan L, Olsen M, Tajouri L, Beaver D, Hudson C, Alghafri R, McKirdy S, Goldsworthy A. Plastic induced urinary tract disease and dysfunction: a scoping review. J Expo Sci Environ Epidemiol. 2025;35(5):770\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41370-024-00709-3\u003c/span\u003e\u003cspan address=\"10.1038/s41370-024-00709-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Aug 31. PMID: 39217203; PMCID: PMC12401723.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng S, Guo H. Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy. Sci Rep. 2025;15(1):19547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-03285-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-03285-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 40467639; PMCID: PMC12137637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhbayashi N, Murayama K, Kato-Murayama M, Kukimoto-Niino M, Uejima T, Matsuda T, Ohsawa N, Yokoyoma S, Nojima H, Shirouzu M. Structural Basis for the Inhibition of Cyclin G-Associated Kinase by Gefitinib. ChemistryOpen. 2018;7(9):721\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/open.201800177\u003c/span\u003e\u003cspan address=\"10.1002/open.201800177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 30214852; PMCID: PMC6129943.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel SH, Metcalf M, Bivalacqua TJ, Kates M. Plastic exposure and urological malignancies - an emerging field. Nat Rev Urol. 2020;17(12):653\u0026ndash;654. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41585-020-00385-6\u003c/span\u003e\u003cspan address=\"10.1038/s41585-020-00385-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 33028980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang K, Chen D, Ma K, Wu X, Hao H, Jiang S. NAD(P)H:Quinone Oxidoreductase 1 (NQO1) as a Therapeutic and Diagnostic Target in Cancer. J Med Chem. 2018;61(16):6983\u0026ndash;7003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jmedchem.8b00124\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.8b00124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2018 May 7. PMID: 29712428.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoison D, Yegutkin GG. Adenosine Metabolism: Emerging Concepts for Cancer Therapy. Cancer Cell. 2019;36(6):582\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccell.2019.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2019.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31821783; PMCID: PMC7224341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhulai G, Oleinik E, Shibaev M, Ignatev K. Adenosine-Metabolizing Enzymes, Adenosine Kinase and Adenosine Deaminase, in Cancer. Biomolecules. 2022;12(3):418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biom12030418\u003c/span\u003e\u003cspan address=\"10.3390/biom12030418\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35327609; PMCID: PMC8946555.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhta A, Gorelik E, Prasad SJ, Ronchese F, Lukashev D, Wong MK, Huang X, Caldwell S, Liu K, Smith P, Chen JF, Jackson EK, Apasov S, Abrams S, Sitkovsky M. A2A adenosine receptor protects tumors from antitumor T cells. Proc Natl Acad Sci U S A. 2006;103(35):13132\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0605251103\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0605251103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2006 Aug 17. PMID: 16916931; PMCID: PMC1559765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoswell-Smith V, Spina D, Page CP. Phosphodiesterase inhibitors. Br J Pharmacol. 2006;147(1):S252\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/sj.bjp.0706495\u003c/span\u003e\u003cspan address=\"10.1038/sj.bjp.0706495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 16402111; PMCID: PMC1760738.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Xie CJ, Ding Z, Shan T, Zhong Z, Yuan FL, Wu JJ, Yuan ZD, Qian C, Yu L, Liu Y, Cheng J, Zhang QL, Liu W, Zhao Y, Huang J, Zhang WZ, Yin Q, Gao BY, Hou JQ, Wang JC, Mei J, Deng C. PDE5A\u0026thinsp;+\u0026thinsp;cancer-associated fibroblasts enhance immune suppression in gastric cancer. Gut 2025 Oct 20:gutjnl\u0026ndash;2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2025-335794\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2025-335794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub ahead of print. PMID: 41115748.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng C, Zhang Z, Wang J, Wang C, Liu T, Yang C, Wang G, Huang H, Li Y. CircPGM5 regulates Foxo3a phosphorylation via MiR-21-5p/MAPK10 axis to inhibit bladder cancer progression. Cell Signal. 2024;121:111297. Epub 2024 Jul 14. PMID: 39004326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. Prog Med Chem. 2021;60:273\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/bs.pmch.2021.01.004\u003c/span\u003e\u003cspan address=\"10.1016/bs.pmch.2021.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 May 27. PMID: 34147204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Zhang Z, Wang Z, Li Y, Chen G, Yi C, Wang R, Yu D, He Y. Integrated network toxicology, machine learning, molecular docking and experimental validation to elucidate mechanism of polyethylene terephthalate microplastics inducing periodontitis. Environ Int. 2025;203:109784. Epub 2025 Sep 9. PMID: 40945017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Zhang Z, Ji K, Zhang Q, Qian L, Yang C. Role of microplastics in the tumor microenvironment (Review). Oncol Lett. 2025;29(4):193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ol.2025.14939\u003c/span\u003e\u003cspan address=\"10.3892/ol.2025.14939\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 40041410; PMCID: PMC11877014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Zhang H, Shi L, Jia Y, Sheng H. Detection and quantification of microplastics in various types of human tumor tissues. Ecotoxicol Environ Saf. 2024;283:116818. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecoenv.2024.116818\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2024.116818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Jul 30. PMID: 39083862.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng S, Guo H. Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy. Sci Rep. 2025;15(1):19547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-03285-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-03285-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 40467639; PMCID: PMC12137637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Zhang Z, Wang Z, Li Y, Chen G, Yi C, Wang R, Yu D, He Y. Integrated network toxicology, machine learning, molecular docking and experimental validation to elucidate mechanism of polyethylene terephthalate microplastics inducing periodontitis. Environ Int. 2025;203:109784. Epub 2025 Sep 9. PMID: 40945017.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diethyl terephthalate (DET), Bladder cancer (BLCA), Network toxicology, Prognostic model, Immune infiltration, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-8349736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8349736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eDiethyl terephthalate (DET) is a diester small molecule that may originate as a by-product or a degradation/migration component from polyethylene terephthalate (PET)-related materials during their lifecycle processes such as synthesis, processing, use, and recycling. However, the potential molecular links between DET exposure and the development and progression of bladder cancer (BLCA) remain inadequately elucidated. This study aims to integrate network toxicology and bioinformatics strategies to identify potential DET targets and their intersection with BLCA-related gene networks, further construct a prognostic model to screen key genes, assess correlations with the immune microenvironment, and provide computational evidence at the structural level through molecular docking, thereby offering candidate clues for subsequent mechanistic research and experimental validation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePotential DET targets were predicted by inputting its SMILES (CCOC(=\u0026thinsp;O)C1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;C1)C(=\u0026thinsp;O)OCC) into the SwissTargetPrediction database; BLCA-related gene sets were retrieved from OMIM, TTD, and GeneCards databases and intersected with DET targets to obtain candidate common targets, which were then visualized using Cytoscape; GO and KEGG enrichment analyses were performed with significance determined by a Benjamini\u0026ndash;Hochberg corrected FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Using expression and follow-up data from the TCGA-BLCA cohort, univariate Cox, LASSO, and multivariate Cox regressions were employed to screen prognostic key genes and construct a risk score model. Model performance was evaluated using Kaplan\u0026ndash;Meier survival analysis and time-dependent ROC analysis, and a nomogram along with calibration curves were built by integrating clinical variables to assess predictive consistency. CIBERSORT was used to estimate the infiltration proportions of 22 immune cell types and analyze the correlation between key gene expression and immune infiltration. Finally, receptor/ligand structures were obtained from PDB and PubChem, and molecular docking was performed using AutoDock Vina with PyMOL for visualizing interaction patterns.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 115 potential DET targets were predicted; intersection with BLCA-related gene sets yielded 86 candidate common targets. Enrichment analysis showed that common targets were significantly enriched in molecular function terms such as serine/threonine kinase activity and phosphatidylinositol kinase activity, and in tumor-related signaling pathways such as cAMP and ErbB pathways. Enrichment in terms like \"Chemical carcinogenesis - ROS\" suggested potential associations with oxidative stress and chemical carcinogenesis processes. Prognostic modeling identified six key genes (NQO1, AKR1B1, ADK, GAK, PDE5A, MAPK10). The constructed risk score model enabled high/low-risk stratification (Kaplan\u0026ndash;Meier: P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and demonstrated moderate discriminative ability for 1-, 3-, and 5-year survival (AUC\u0026thinsp;=\u0026thinsp;0.675, 0.658, 0.651). Immune infiltration analysis indicated significant correlations between the expression of key genes and the infiltration proportions of CD8⁺ T cells, dendritic cells, mast cells, NK cells, and macrophage polarization-related subsets. Molecular docking showed that DET could achieve binding conformations with binding energies lower than \u0026minus;\u0026thinsp;5 kcal/mol for all six key target proteins, with the lowest binding energy observed for AKR1B1 (\u0026minus;\u0026thinsp;7.9 kcal/mol).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study proposes, at a systems level, a potential association between the DET-related target network and key BLCA signaling regulation, prognostic risk stratification, and immune microenvironment characteristics, providing a candidate framework of key genes and pathways. Given that the analysis is primarily based on public cohorts and computational predictions, further assessment of causal likelihood and translational potential requires integration with population exposure measurements, in vitro/in vivo functional experiments, and external validation using independent cohorts.\u003c/p\u003e","manuscriptTitle":"Integrative Network Toxicology and Molecular Docking Elucidate the Molecular Mechanisms and Immune Implications of Diethyl Terephthalate in Bladder Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 05:26:29","doi":"10.21203/rs.3.rs-8349736/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":"e3138b14-f8ba-4554-bf28-1913d1c5517c","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T15:12:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 05:26:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8349736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8349736","identity":"rs-8349736","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}