Transcriptomics and metabonomics study on the effect of exercise combined with curcumin supplementation on breast cancer in mice

Curcumin and exercise have been reported to show good anti-tumour effects. However, relevant research on the combined effects of physical exercise and curcumin supplementation on cancer and the underlying mechanisms is still lacking. The current study aimed to construct an anti-breast tumour mouse model using the combined effects of curcumin treatment and swimming exercise. Transcriptomic and metabolomic techniques were used to screen for differentially expressed genes and metabolites, evaluate the anticancer effects, and analyse the molecular regulatory mechanisms related to metabolism. Observation of the mouse phenotypes, including tumour appearance, in-vivo tumour imaging, and HE staining results of pathological sections, suggested a more obvious inhibitory effect of the combination of curcumin administration and exercise intervention on breast cancer than that of a single treatment. The combination treatment group had a total of 445 differentially expressed (154 upregulated and 291 downregulated) genes. Functional enrichment analysis showed the calcium signalling pathway, Wnt signalling pathway, PI3K Akt signalling pathway, and IL-17 signalling pathway to significantly participate in the anti-breast cancer process of curcumin-exercise combination treatment. Results of the intergroup differential metabolite analysis showed that the combined effect of curcumin and exercise involves two unique pathways, namely the amino sugar and nucleotide sugar metabolism, which includes chitosan, d-glucosamine 6-phosphate, l-fucose, and N-acetyl beta-mannosamine, and the amino acid biosynthesis, which includes dl-isoleucine, dl-tyrosine, and homocysteine. Collectively, the top-ranked genes and metabolites with the highest degree of associations were further revealed by O2PLS analysis. Overall, the study helped reveal the mechanism of action of curcumin-exercise combination treatment on breast cancer at multi-omics level.