Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 402

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Silencing of CXCR4 Blocks Breast Cancer Metastasis

Open access
Liang, Zhongxing, Yoon, Younghyoun, Votaw, John, Goodman, Mark M., Williams, Larry, Shim, Hyunsuk·2005·Cancer Research

Abstract

RNA interference technology, silencing targeted genes in mammalian cells, has become a powerful tool for studying gene function. For the first time in cancer research, we show that direct injection of a pool of naked small interfering RNA (siRNA) duplexes can prevent tumorigenesis in an animal model, suggesting a novel preventive and therapeutic strategy for cancer management. As a model system, we used siRNA duplexes of CXCR4 to block breast cancer metastasis. Here, we show that blocking CXCR4 expression at the mRNA level by a combination of two siRNAs impairs invasion of breast cancer cells in Matrigel invasion assay and inhibits breast cancer metastasis in an animal model. Targeting more than one site of the target gene may be important to overcome the functional redundancy of other variants of a single gene, especially in in vivo experiments. Moreover, our studies confirm the necessity of CXCR4 in breast cancer metastasis.

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 · 10.1158/0008-5472.967.65.3

Cites

1 of 1 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

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 1 references

Cited by

See all 402

Metadata

Authors
Liang, Z., Yoon, Y., Votaw, J., Goodman, M. M., Williams, L., & Shim, H.
Venue
Cancer Research
Published
2005-02-01
Volume
65
Issue
3
Pages
967-971
ISSN
  • 0008-5472 (ISSN-L)
  • 1538-7445
OpenAlex
W1809740262
Cited by
402
References
1
FWCI
14.84
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: Silencing of CXCR4 Blocks Breast Cancer Metastasis. https://app.cochat.ai/papers/10.1158/0008-5472.967.65.3
The paper
Liang, Z., Yoon, Y., Votaw, J., Goodman, M. M., Williams, L., & Shim, H. (2005). Silencing of CXCR4 Blocks Breast Cancer Metastasis. Cancer Research. https://doi.org/10.1158/0008-5472.967.65.3

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.