bioRxivCancer Biology
Mechanistic insights into zearalenone-accelerated colorectal cancer in mice using integrative multi-omics approaches
E. K. K. Lo, X. Wang, P.-K. Lee, H.-C. Wong, J. C.-Y. Lee, C. Gomez-Gallego, D. Zhao, H. El-Nezami, J. Li
Abstract
Zearalenone (ZEA), a secondary metabolite of Fusarium fungi found in cereal-based foods, promotes the growth of colon, breast, and prostate cancer cells in vitro. However, the lack of animal studies hinders a deeper mechanistic understanding of the cancer-promoting effects of ZEA. This study aimed to determine the effect of ZEA on colon cancer progression and its underlying mechanisms. Through integrative analyses of transcriptomics, metabolomics, metagenomics, and host phenotypes, we investigated the impact of a 4-week ZEA intervention on colorectal cancer in xenograft mice. Our results showed a twofold increase in tumor weight with the 4-week ZEA intervention. ZEA exposure significantly increased the mRNA and protein levels of BEST4, DGKB, and Ki67 and the phosphorylation levels of ERK1/2 and AKT. Serum metabolomic analysis revealed that the levels of amino acids, including histidine, arginine, citrulline, and glycine, decreased significantly in the ZEA group. Furthermore, ZEA lowered the alpha diversity of the gut microbiota and reduced the abundance of nine genera, including Tuzzerella and Rikenella. Further association analysis indicated that Tuzzerella was negatively associated with the expression of BEST4 and DGKB genes, serum uric acid levels, and tumor weight. Additionally, circulatory hippuric acid levels positively correlated with tumor weight and the expression of oncogenic genes, including ROBO3, JAK3, and BEST4. Altogether, our results indicated that ZEA promotes colon cancer progression by enhancing the BEST4/AKT/ERK1/2 pathway, lowering circulatory amino acid concentrations, altering gut microbiota composition, and suppressing short chain fatty acids production.
- organism: mouse
- assay: bulk RNA-seq
- assay: metabolomics
- assay: metagenomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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