{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:20:36Z","timestamp":1789395636118,"version":"build-2803163510"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acetyl tributyl citrate (ATBC) is an alternative plasticizer to Di(2-ethylhexyl) phthalate (DEHP) and bisphenol A (BPA) that may contribute to the development of various cancers. The cytotoxic activity and pro\u2010tumorigenic role of ATBC in prostate cancer (PCa) progression remains poorly understood, highlighting the need for a systematic evaluation of its molecular toxicity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study identified potential targets of ATBC in PCa by integrating multiple online databases, including GEO, ChEMBL, STITCH, and Swiss Target Prediction. Protein-protein interaction (PPI) analysis was performed on the intersecting targets, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, and immune microenvironment analyses. Subsequent these crucial targets were identified through the application of machine learning algorithms, specifically the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). The expression of these targets was validated using external datasets from The Cancer Genome Atlas (TCGA) and\n                      <jats:italic>in vitro<\/jats:italic>\n                      experiments. Molecular docking analysis was used to explore the interactions between the key genes and ATBC. Finally, virtual knockout and pathway enrichment analysis simulations on single-cell RNA-seq data were conducted to investigate the biological functions of these genes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Through the utilization of multiple online databases, we identified 45 potential targets associated with ATBC exposure-related PCa. Further refinement using STRING and Cytoscape software elucidated the PPI network. GO and KEGG pathway analyses revealed that these targets, associated with ATBC and PCa, were predominantly enriched in several signal pathways. Subsequent analysis employing machine learning algorithms identified three core targets: EZH2, DNMT1, and PRMT5, which influence the immune infiltration of PCa. Additionally, molecular docking analysis and\n                      <jats:italic>in vitro<\/jats:italic>\n                      experiments further confirmed these potential therapeutic targets. Virtual knockdown of DNMT1, EZH2, and PRMT5 reveals their essential role in preserving collagen-rich ECM integrity and cell\u2013matrix adhesion to maintain prostatic stromal homeostasis, whose disruption mediates ATBC-induced cytotoxicity and potential pro-tumorigenic effects.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Overall, the findings provide new insights into linking the effects of ATBC to PCa, and they provide a preventive and therapeutic strategy for PCa that exposure to the environments with excessive ATBC levels.<\/jats:p>\n                  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