Imidacloprid contributes to bladder cancer progression: preliminary evidence based on network toxicology, machine learning and molecular docking

Background: Imidacloprid (IMI) has been widely used in agriculture and is increasingly infiltrating urban environments. This study aimed to investigate the role of IMI in the development of bladder Cancer (BCa) and its potential molecular mechanisms.

Methods: IMI-related genes were obtained from the CTD database. Differentially expressed IMI-related genes (DEIRGs) in BCa were identified through differential analysis of RNA-seq data from TCGA database, followed by KEGG and GO enrichment analyses to explore their biological functions. Ten machine learning algorithms and their combinations were applied to construct prognostic models based on DEIRGs in TCGA cohort, with external validation in two independent cohorts (GSE13507 and GSE31684). Multivariate COX regression analyses were further used to develop a DEIRG-based risk score model for patient risk stratification. Drug sensitivity analyses were performed using the pRRophetic R package. Molecular docking was conducted using the CB-Dock2 tool. Colony formation, wound healing, and transwell invasion assays were used to evaluate the biological behaviors of BCa cells. Real-Time quantitative Polymerase Chain Reaction (RT-qPCR) and western blot analyses were performed to evaluate the expression levels of key genes.

Results: A total of 138 DEIRGs were identified, which were enriched in pathways including Insulin resistance, chemical carcinogenesis, cAMP, FOXO, and AMPK signaling. Machine learning models based on these genes demonstrated robust prognostic performance across all three cohorts. Five key genes (SREBF1, PRELP, TGFBI, TNFAIP2 and TACR3) were further selected via multivariate COX regression, and patients in the high-risk group exhibited significantly worse prognosis than those in the low-risk group (all p < 0.05). A nomogram integrating the risk score and clinicopathological features enabled individualized survival prediction. The high-risk group was more sensitive to drugs such as Rizavasertib, Saracatinib, and Motesanib, whereas the low-risk group was more sensitive to drugs such as Rucaparib, Veliparib, and Axitinib. Molecular docking demonstrated strong binding affinity of IMI to these five target proteins. In vitro experiments further showed that IMI at occupational exposure concentrations (10 ng/mL) in urine significantly promoted the proliferation, migration, and invasion of BCa cells. RT-qPCR and western blot analyses confirmed that IMI exposure upregulated the expression of SREBF1, PERLP, and TGFBI, and downregulated the expression of TNFAIP2, while having no significant effect on TACR3 expression.

Conclusions: This study suggests that IMI may promote BCa development and sheds light on potential molecular mechanisms. Moreover, a DEIRG-based risk stratification model may facilitate personalized treatment decisions for BCa patients.

Supplementary information: The online version contains supplementary material available at 10.1186/s40360-025-01016-9.

Bladder cancer; Imidacloprid; Machine learning; Molecular docking; Network toxicology; Risk stratification.