Integrated transcriptomics and network toxicology suggests core targets and potential mechanisms of aspartame-related breast carcinogenesis
Year: 2026
Type: article
Abstract: Aspartame (APM) is a globally prevalent low-calorie artificial sweetener. Although its long-term health risks have garnered increasing attention, the potential molecular targets and pathways linking APM exposure to breast cancer remain largely unexplored. In this study, we integrated network toxicology and transcriptomics with multiple computational approaches, including machine learning, molecular docking, and clinical validation, to predictively prioritize candidate targets and pathways potentially involved in APM‑related breast carcinogenesis. We acquired 226 APM targets and 1973 breast cancer genes, from which 46 overlapping potential targets were extracted. Functional enrichment analyses revealed that these targets were mainly enriched in biological processes related to cell proliferation and migration, as well as multiple cancer‑associated signaling pathways, including transcriptional misregulation in cancer, TNF, and IL-17. Using a multi-algorithm machine learning model, we further prioritized 5 core targets with favorable diagnostic performance and stable predictive ability. SHAP analysis was performed on the core targets, and virtual gene knockout was conducted for the gene with the most significant contribution. Finally, TCGA datasets further confirmed the differential expression of these core targets across breast cancer subtypes and normal tissues. Collectively, these computational findings provide a prioritized list of molecular candidates and a hypothesis‑generating basis for future experimental and mechanistic studies on APM and breast cancer.
Source: Scientific Reports
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7223180218