Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 317

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

p53 and Tumor Suppression: It Takes a Network

Open access
Boutelle, Anthony M., Attardi, Laura D.·2021·Trends in Cell Biology

Abstract

The TP53 tumor suppressor is the most frequently mutated gene in human cancer. p53 suppresses tumorigenesis by transcriptionally regulating a network of target genes that play roles in various cellular processes. Though originally characterized as a critical regulator for responses to acute DNA damage - activation of apoptosis and cell cycle arrest – recent studies have highlighted new pathways and transcriptional targets downstream of p53 regulating genomic integrity, metabolism, redox biology, stemness, and non-cell autonomous signaling in tumor suppression. Here, we summarize our current understanding of p53-mediated tumor suppression, situating recent findings from mouse models and unbiased screens in the context of previous studies and arguing for the importance of the pleiotropic effects of the p53 transcriptional network in inhibiting cancer.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.tcb.2020.12.011

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 14 references

Cited by

See all 317

Metadata

Authors
Boutelle, A. M., & Attardi, L. D.
Venue
Trends in Cell Biology
Publisher
Elsevier BV
Published
2021-04
Volume
31
Issue
4
Pages
298-310
ISSN
0962-8924 (ISSN-L)
OpenAlex
W3128544040
Cited by
317
References
14
FWCI
8.55
Type
journal article
Language
en
Open access
bronze

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 ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: p53 and Tumor Suppression: It Takes a Network. https://app.cochat.ai/papers/10.1016/j.tcb.2020.12.011
The paper
Boutelle, A. M., & Attardi, L. D. (2021). p53 and Tumor Suppression: It Takes a Network. Trends in Cell Biology. https://doi.org/10.1016/j.tcb.2020.12.011

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.