ArticleJournal of food science2025
Unraveling the Carcinogenic Mechanisms of Food Contaminants: An Integrated in Silico Framework Combining Network Toxicology, Machine Learning, and Molecular Docking.
Article in Journal of food science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Investigating the Shared Mechanisms of Endocrine-Disrupting Chemicals in Urogenital Tumors.Biology · 2026Article
- Unraveling the Carcinogenic Mechanisms of Food Contaminants: An Integrated in Silico Framework Combining Network Toxicology, Machine Learning, and Molecular Docking.Journal of food science · 2025Article
- Food contaminants: mechanisms of toxicity, computational assessment, and mitigation.Frontiers in toxicology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Food contamination poses a significant global health threat with carcinogenic potential, though the molecular pathways connecting contaminants to cancer remain poorly understood. This study sought to identify key molecular targets mediating the carcinogenic effects of nine prevalent dietary contaminants: glyphosate, perfluorooctane sulfonate, nitrosamines, pentabromodiphenyl ethers, methylmercury, dioxins, acrylamide, pyrrolizidine alkaloids, and aflatoxin. Using multiple online databases, we identified target genes associated with these contaminants and pan-cancer, then conducted protein-protein interaction (PPI) analysis and visualization on intersecting genes. Subsequent gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) functional enrichment analyses were performed to uncover potential mechanisms, with a focus on breast (BRCA), prostate (PRAD), and colon (COAD) carcinomas due to their significant pathway associations. Hub genes were prioritized through an integrative strategy combining topological algorithms in cytoscape (Centiscape, MCODE, and cytohubba's MCC), machine learning validation, and weighted gene co-expression network analysis (WGCNA). Molecular docking simulations were conducted to examine interactions between contaminants and hub genes. The study identified 69 pan-cancer-intersected targets, with enrichment analyses revealing significant cancer-associated pathways. Hub gene prioritization pinpointed JUN in BRCA, CDC42 in COAD, and MAPK14 in PRAD as critical regulatory targets. Validation using The Cancer Genome Atlas (TCGA) data confirmed statistically significant differential expression patterns (p < 0.05) for these targets across respective malignancies. Gene set enrichment analysis (GSEA) outlined pathway activation profiles consistent with tumor progression mechanisms. Molecular docking simulations demonstrated strong binding affinities (binding energy ≤ -5.0 kcal/mol) between contaminants and structural domains of the identified hub targets, suggesting potential mechanistic links between these food contaminants and cancer development.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.