Laboratory Animal and Comparative Medicine ›› 2024, Vol. 44 ›› Issue (1): 52-61.DOI: 10.12300/j.issn.1674-5817.2023.094

• Animal Models of Human Diseases • Previous Articles     Next Articles

Metabolomics Analysis of Tupaia belangeri Breast Tumor Model

Xi FANG1, Qingqing AO2, Chunhong LI1, Yiqiang OUYANG1, Songchao GUO1, Bing HU1()   

  1. 1.Laboratory Animal Center, Guangxi Medical University, Nanning 530021, China
    2.Guilin People's Hospital, Guilin 541002, China
  • Received:2023-06-30 Revised:2023-12-12 Online:2024-02-25 Published:2024-03-07
  • Contact: Bing HU

Abstract:

Objective To explore the metabolic changes during the development of Tupaia belangeri breast tumors, to investigate the close relationship between the changes of serum metabolic substances and the occurrence and progression of tumors, and to screen for biomarkers reflecting the progression of breast tumors. Methods Breast tumors in Tupaia belangeri were induced by orally administering 7,12-dimethylbenzoanthracene (DMBA) three times, with a 15-day interval between each administration, along with a high-fat and high-sugar diet. The DMBA-induced breast cancer group and the DMBA-inducedwithout breast cancer group were compared with the control group. Untargeted determination of serum metabolites was performed using gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) in DMBA-induced Tupaia belangeri with breast cancer, DMBA-induced without breast cancer and the control group. Multidimensional statistical analysis including unsupervised principal component analysis (PCA), and orthogonal partial least squares analysis (OPLS-DA) were conducted. Furthermore, t-test was used for intergroup differential comparison. Differential metabolites were screened under VIP>1 and P<0.05 conditions, and significantly changing differential metabolites were identified using the HMDB online database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was utilized to enrich metabolic-related gene regulatory pathways. Results The incidence of breast tumors was 40% in DMBA-induced Tupaia belangeri. Compared with the control group, 30 metabolic differential products were detected in the serum of the group with breast cancer, with 18 down-regulated and 12 up-regulated (VIP>1, P<0.05). KEGG pathway analysis revealed significant changes in four metabolic pathways: glutamate metabolism, glyceride metabolism, citric acid cycle, and alanine metabolism. Compared with the group without breast cancer, 18 metabolic differential products were detected, with 7 down-regulated and 11 up-regulated (VIP>1, P<0.05). KEGG pathway analysis revealed significant changes in the citric acid cycle and glutamate metabolism. Compared with the control group, 31 metabolic differential products were detected in the serum of the groups without breast cancer, with 14 down-regulated and 17 up-regulated (VIP>1, P<0.05). KEGG pathway analysis revealed significant changes in three metabolic pathways: glutamate metabolism, glyceride metabolism, and citric acid cycle. Conclusion Metabolomics analysis can reveal the characteristics of changes in metabolites in the serum of breast tumors. The results suggest that glutamate metabolism, glyceride metabolism, citric acid cycle, and alanine metabolism pathways are associated with the occurrence and development of DMBA-induced breast tumors in Tupaia belangeri. It provides a foundation for further research into the biological mechanism of breast cancer.

Key words: Tupaia belangeri, Breast tumors, Metabolomics, 7,12-Dimethylbenzoanthracene, High fat and sugar

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