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Carcinogenesis resistance in the longest‐lived rodent, the naked mole‐rat

Yuki Yamamura, Yoshimi Kawamura, Kaori Oka, Kyoko Miura

Cancer Science · 2022 · ▲ 19 citations

Abstract

Certain mammalian species are resistant to cancer, and a better understanding of how this cancer resistance arises could provide valuable insights for basic cancer research. Recent technological innovations in molecular biology have allowed the study of cancer-resistant mammals, despite the fact that they are not the classical model animals, which are easily studied using genetic approaches. Naked mole-rats (NMRs; Heterocephalus glaber) are the longest-lived rodent, with a maximum lifespan of more than 37 years, and almost never show spontaneous carcinogenesis. NMRs are currently attracting much attention from aging and cancer researchers, and published studies on NMR have continued to increase over the past decade. Cancer development occurs via multiple steps and involves many biological processes. Recent research on the NMR as a model for cancer resistance suggests that they possess various unique carcinogenesis-resistance mechanisms, including efficient DNA repair pathways, cell-autonomous resistance to transformation, and dampened inflammatory response. Here, we summarize the molecular mechanisms of carcinogenesis resistance in NMR, which have been uncovered over the past two decades, and discuss future perspectives.

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Provenance

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OpenAlex
DOI
10.1111/cas.15570
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2026-10-13 MST

Cite this

APA
Yamamura, Y., Kawamura, Y., Oka, K., &amp; Miura, K. (2022). Carcinogenesis resistance in the longest‐lived rodent, the naked mole‐rat. <em>Cancer Science</em>. https://doi.org/10.1111/cas.15570
Vancouver
Yamamura Y, Kawamura Y, Oka K, Miura K. Carcinogenesis resistance in the longest‐lived rodent, the naked mole‐rat. Cancer Science. 2022. doi:10.1111/cas.15570.
BibTeX
@article{yuki2022Carcin, title = {Carcinogenesis resistance in the longest‐lived rodent, the naked mole‐rat}, author = {Yuki Yamamura and Yoshimi Kawamura and Kaori Oka and Kyoko Miura}, journal = {Cancer Science}, year = {2022}, doi = {10.1111/cas.15570}, }

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