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Preliminary evidence of TP53 variants in spontaneous tree shrew tumors is provided and natural subspecies variation is revealed and reinforces the tree shrew's relevance as a model for TP53-related cancer research, although further functional studies are needed.
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The tree shrew (Tupaia belangeri) is an emerging animal model for human diseases, yet the status of the critical tumor suppressor TP53 in its subspecies and spontaneous tumors is unknown. We sequenced the entire TP53 coding region from two subspecies, T. b. yaoshanensis and T. b. chinensis, and analyzed two spontaneous sarcomas from T. b. yaoshanensis using histopathology, immunohistochemistry, and cDNA sequencing with clonal validation. TP53 showed 99.5% nucleotide identity between subspecies, with a single fixed non-synonymous substitution (D42G) in T. b. yaoshanensis predicted to be functionally neutral. All residues corresponding to human cancer hotspots were strictly conserved, and structural modeling confirmed high similarity to human p53 (RMSD = 0.6 Å). Critically, we identified TP53 sequence variants in both spontaneous tumors: a missense variant (Ser175Gly) in the DNA-binding domain of a liposarcoma and another (Met344Thr) in the tetramerization domain of a myxofibrosarcoma. Computational prediction suggested both missense variants are tolerated (SIFT scores 0.61 and 0.77), and their functional significance remains to be determined. This study provides preliminary evidence of TP53 variants in spontaneous tree shrew tumors and reveals natural subspecies variation. The high conservation of p53 reinforces the tree shrew's relevance as a model for TP53-related cancer research, although further functional studies are needed.
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@article{Cao2026Comparative,
title = {Comparative analysis of TP53 gene in Tupaia belangeri subspecies ( Tupaia belangeri yaoshanensis vs. Tupaia belangeri chinensis ) and identification of mutations in spontaneous tumor cases},
author = {Yingying Cao and Haibo Tang and Liang Liang and Jing Leng and Weijian Huang},
journal = {Animal Models and Experimental Medicine},
year = {2026},
doi = {10.1002/ame2.70260},
url = {https://doi.org/10.1002/ame2.70260}
}
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