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Cited by 338

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Resveratrol suppresses cell transformation and induces apoptosis through a p53-dependent pathway

Huang, C.·1999·Carcinogenesis

Abstract

Resveratrol, a plant constituent enriched in the skin of grapes, is one of the most promising agents for the prevention of cancer. However, the mechanism of the anti-carcinogenic activity of resveratrol is not well understood. Here we offer a possible explanation of its anti-cancer effect. Resveratrol suppresses tumor promoter-induced cell transformation and markedly induces apoptosis, transactivation of p53 activity and expression of p53 protein in the same cell line and at the same dosage. Also, resveratrol-induced apoptosis occurs only in cells expressing wild-type p53 (p53+/+), but not in p53-deficient (p53-/-) cells, while there is no difference in apoptosis induction between normal lymphoblasts and sphingomyelinase-deficient cell lines. These results demonstrate for the first time that resveratrol induces apoptosis through activation of p53 activity, suggesting that its anti-tumor activity may occur through the induction of apoptosis.

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Crossref · 10.1093/carcin/20.2.237

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Metadata

Authors
Huang, C.
Venue
Carcinogenesis
Published
1999-02-01
Volume
20
Issue
2
Pages
237-242
ISSN
  • 0143-3334 (ISSN-L)
  • 1460-2180
OpenAlex
W2166185936
Cited by
338
References
4
FWCI
18.78
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Resveratrol suppresses cell transformation and induces apoptosis through a p53-dependent pathway. https://app.cochat.ai/papers/10.1093/carcin/20.2.237
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Huang, C. (1999). Resveratrol suppresses cell transformation and induces apoptosis through a p53-dependent pathway. Carcinogenesis. https://doi.org/10.1093/carcin/20.2.237

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.