ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutrition and Metabolism
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1646623
This article is part of the Research TopicThe Impact of Sweeteners in Metabolism and Gut Microbiota-Brain AxisView all articles
A comprehensive analysis reveals the relationship between artificial sweeteners and prostate cancer
Provisionally accepted
- 1First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
- 2Guizhou Provincial People's Hospital, Guiyang, China
- 3Affiliated Hospital of Guizhou Medical University, Guiyang, China
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Abstract Background:Global consumption of artificial sweeteners (AS) 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 AS and PCa from multiple public databases. Protein-protein interaction (PPI) network analysis and visualization were conducted on overlapping genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore underlying mechanisms. Subsequently, the optimal predictive model was selected from 101 machine-learning algorithm combinations and validated against two external datasets. Molecular docking analysis was then performed to examine interactions between key genes and AS compounds. Finally, in vitro cellular assays were conducted to validate the specific effects of AS on PCa. Result:We analyzed seven common AS – aspartame, acesulfame-K, sucralose, NHDC, sodium cyclamate, neotame, and saccharin – identifying 261 overlapping targets associated with PCa. GO and KEGG enrichment analyses revealed 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, in vitro cellular assays demonstrated that NHDC suppresses MMP11 expression in PCa cells and exhibits anti-PCa pharmacological effects. Conclusion:By integrating bioinformatics, machine learning, molecular docking, and in vitro cellular assays, this study demonstrates that AS inhibits PCa progression through multiple molecular targets and signaling pathways. Collectively, these findings provide important insights for safety assessment of food additives and cancer risk
Keywords: Artificial sweetener, prostate cancer, robot learning, molecular docking, Matrix Metalloprotein 11
Received: 16 Jun 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Zhang, Che, Gao and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wei Li, Affiliated Hospital of Guizhou Medical University, Guiyang, China
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