ArticleFrontiers in nutrition2025
A comprehensive analysis reveals the relationship between artificial sweeteners and prostate cancer.
Article in Frontiers in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Study on the Potential Molecular Mechanisms of Sodium Dehydroacetate (Na-DHA) Interfering With Bone Metabolism and Inducing Osteoporosis Based on Network Toxicology, Molecular Docking, and In Vitro Experimental Validation.Food science & nutrition · 2026Article
- Deciphering the role of per- and polyfluoroalkyl substances in prostate cancer: a multi-omics and computational toxicology approach.Frontiers in cell and developmental biology · 2026Article
- Multi omics network toxicology andFrontiers in cell and developmental biology · 2026Article
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Authors and funding
4 authors.
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Abstract
Background: Global consumption of artificial sweeteners (ASs) has risen substantially in recent years. However, their relationship with prostate cancer (PCa) remains poorly characterized. This study investigates the AS-PCa association to identify pivotal genes potentially bridging this relationship. Method: This study retrieved target genes associated with ASs and PCa from multiple public databases. Protein-protein interaction (PPI) network analysis and visualization were conducted on overlapping genes, followed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the underlying mechanisms. Subsequently, the optimal predictive model was selected from 101 machine-learning algorithm combinations and validated against 2 external datasets. Molecular docking analysis was then performed to examine the interactions between key genes and AS compounds. Finally, Results: We analyzed seven common ASs-aspartame, acesulfame-K, sucralose, NHDC, sodium cyclamate, neotame, and saccharin-identifying 261 overlapping targets associated with PCa. The GO and KEGG enrichment analyses revealed that these targets primarily regulate cell proliferation, inflammation, and cancer cell metabolism. Machine learning algorithm screening identified the Lasso-SuperPC hybrid model as demonstrating optimal predictive performance, with robust validation in two independent external datasets. Subsequent analysis identified two key regulatory genes: CD38 and MMP11. Molecular docking analysis further confirmed potential interactions between AS compounds and the core target MMP11. Finally, Conclusion: By integrating bioinformatics, machine learning, molecular docking, and
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