Deciphering Food-Disease Relationships Through Molecular Networks in Breast Cancer

Abstract

Abstract Purpose: Despite advances in food and disease databases, a lack of integrated resources remains that systematically elucidate molecular-level food-disease associations. This study aims to introduce a comprehensive framework for mapping associations between foods, their metabolites, molecular targets, and diseases, using breast cancer as a case study. Methods: We integrated data from FooDB, HMDB, and DisGeNET to extract information on foods, food-derived metabolites, protein/gene targets, and disease associations. Data cleaning and mapping were performed in Excel 2019. Networks illustrating food-metabolite-protein/gene-disease interactions were constructed and visualized using Cytoscape 3.7.2. Functional enrichment and topological analyses (degree and betweenness centrality) identified key nodes. Additionally, a systematic literature review was conducted in PubMed to synthesize evidence from systematic reviews and meta-analyses on dietary factors and breast cancer risk. Results: The integrated network included nearly 1,000 foods, 61,500 ingredients, 250,000 metabolites, and 15,000 disease variants. The breast cancer network comprised 15,966 nodes and 321,144 edges. APOE emerged as the central gene, connected to 13,628 metabolites, while major food groups also featured as key nodes. Literature synthesis confirmed that high intake of fruits and vegetables is protective against breast cancer, while alcohol, fats, and processed meats increase risk, aligning with network findings. Conclusion: This study proposes a molecular framework that links foods to diseases through metabolites and pathways, enabling testable hypotheses and informing precision nutrition. It urges improved data integration, control of confounders, and experimental validation to support personalized dietary recommendations. Keywords food-disease associations, molecular nutrition, relation extraction, precision nutrition, network analysis

Keywords: Food-disease associations, Molecular nutrition, Relation extraction, Precision nutrition, Network analysis