Impact of diabetes on promoting the growth of breast cancer
Open accessAbstract
Background: Type II diabetes mellitus (DM2) is a significant risk factor for cancers, including breast cancer. However, a proper diabetic breast cancer mouse model is not well‐established for treatment strategy design. Additionally, the precise diabetic signaling pathways that regulate cancer growth remain unresolved. In the present study, we established a suitable mouse model and demonstrated the pathogenic role of diabetes on breast cancer progression. Methods: We successfully generated a transgenic mouse model of human epidermal growth factor receptor 2 positive (Her2 + or ERBB2) breast cancer with DM2 by crossing leptin receptor mutant ( Lepr db/+ ) mice with MMTV‐ ErbB2 /neu) mice. The mouse models were administrated with antidiabetic drugs to assess the impacts of controlling DM2 in affecting tumor growth. Magnetic resonance spectroscopic imaging was employed to analyze the tumor metabolism. Results: Treatment with metformin/rosiglitazone in MMTV‐ ErbB2 / Lepr db/db mouse model reduced serum insulin levels, prolonged overall survival, decreased cumulative tumor incidence, and inhibited tumor progression. Anti‐insulin resistance medications also inhibited glycolytic metabolism in tumors in vivo as indicated by the reduced metabolic flux of hyperpolarized 13 C pyruvate‐to‐lactate reaction. The tumor cells from MMTV‐ ErbB2 / Lepr db/db transgenic mice treated with metformin had reprogrammed metabolism by reducing levels of both oxygen consumption and lactate production. Metformin decreased the expression of Myc and pyruvate kinase isozyme 2 (PKM2), leading to metabolism reprogramming. Moreover, metformin attenuated the mTOR/AKT signaling pathway and altered adipokine profiles. Conclusions: MMTV‐ ErbB2 / Lepr db/db mouse model was able to recapitulate diabetic HER2 + human breast cancer. Additionally, our results defined the signaling pathways deregulated in HER2 + breast cancer under diabetic condition, which can be intervened by anti‐insulin resistance therapy.
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- Authors
- Chou, P., Choi, H. H., Huang, Y., Fuentes‐Mattei, E., Velazquez‐Torres, G., Zhang, F., Phan, L., Lee, J., Shi, Y., Bankson, J. A., Wu, Y., Wang, H., Zhao, R., Yeung, S. J., & Lee, M.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- the University of Texas MD Anderson Cancer Center
- Sun Yat-sen University Cancer Center (CN)
- Corresponding author
- Zhao, Ruiying
- Yeung, Sai‐Ching Jim
- Lee, Mong‐Hong
- ORCID
- Venue
- Cancer Communications
- Published
- 2021-02-20
- Volume
- 41
- Issue
- 5
- Pages
- 414-431
- ISSN
- 1000-467X (ISSN-L)
- 2523-3548
- PMC
- PMC8118590
- OpenAlex
- W3129958206
- Cited by
- 35
- References
- 54
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 5 · 2023: 9 · 2022: 7 · 2021: 1
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- 癌症：英文版 (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fidelity Foundation (2020YFA0803300)
- National Natural Science Foundation of China (81702749, 81630072, 81773098, 81803568, 8160242)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Lipids, and Metabolism
- Keywords
- type 2 diabetes, breast cancer, HER2-positive breast cancer, metformin, rosiglitazone, insulin resistance, tumor metabolic reprogramming, glycolysis, lactic acid production, c-Myc, PKM2, Akt/mTOR signaling, adipokines, diabetes treatment
- MeSH terms
- Animals, Breast Neoplasms, Diabetes Mellitus, Type 2, Disease Models, Animal, Female, Humans, Mice, Transgenic, Signal Transduction, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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