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Unraveling the Carcinogenic Mechanisms of Food Contaminants: An Integrated in Silico Framework Combining Network
Bangsheng Chen1, Maomao Li2, Yi Gu3
1Emergency Medical Center, Ningbo Yinzhou No. 2 Hospital, Ningbo, Zhejiang, China.
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.
Insights
Dietary contaminants can cause cancer by affecting specific genes. This study identified key molecular targets like JUN, CDC42, and MAPK14, linking food contaminants to cancer development through molecular interactions.
Area of Science:
- Molecular Biology
- Cancer Research
- Environmental Health
Background:
- Food contamination is a global health concern with carcinogenic risks.
- Molecular mechanisms linking dietary contaminants to cancer are not well understood.
Purpose of the Study:
- Identify molecular targets mediating carcinogenic effects of nine prevalent dietary contaminants.
- Investigate the mechanistic links between these contaminants and cancer development.
Main Methods:
- Utilized online databases to identify contaminant-associated genes and pan-cancer targets.
- Performed protein-protein interaction (PPI), gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) analyses.
- Employed topological algorithms, machine learning, WGCNA, and molecular docking for hub gene identification and validation.
Main Results:
- Identified 69 pan-cancer-intersected target genes, revealing significant cancer-associated pathways.
- Pinpointed JUN (BRCA), CDC42 (COAD), and MAPK14 (PRAD) as critical hub genes.
- Validated differential expression of hub genes in The Cancer Genome Atlas (TCGA) data and confirmed contaminant-hub gene binding affinities.
Conclusions:
- JUN, CDC42, and MAPK14 are key molecular targets mediating the carcinogenic effects of dietary contaminants.
- Molecular docking simulations support direct interactions between contaminants and identified hub targets.
- Findings provide insights into the molecular pathways linking food contaminants to cancer development.
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