Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 351

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Tumor Immune Microenvironment during Epithelial–Mesenchymal Transition

Taki, Mana, Abiko, Kaoru, Ukita, Masayo, Murakami, Ryusuke, Yamanoi, Koji, Yamaguchi, Ken, Hamanishi, Junzo, Baba, Tsukasa, Matsumura, Noriomi, Mandai, Masaki·2021·Clinical Cancer Research

Abstract

Epithelial–mesenchymal transition (EMT) has been shown to play a critical role in tumor development from initiation to metastasis. EMT could be regarded as a continuum, with intermediate hybrid epithelial and mesenchymal phenotypes having high plasticity. Classical EMT is characterized by the phenotype change of epithelial cells to cells with mesenchymal properties, but EMT is also associated with multiple other molecular processes, including tumor immune evasion. Some previous studies have shown that EMT is associated with the cell number of immunosuppressive cells, such as myeloid-derived suppressor cells, and the expression of immune checkpoints, such as programmed cell death-ligand 1, in several cancer types. At the molecular level, EMT transcriptional factors, including Snail, Zeb1, and Twist1, produce or attract immunosuppressive cells or promote the expression of immunosuppressive checkpoint molecules via chemokine production, leading to a tumor immunosuppressive microenvironment. In turn, immunosuppressive factors induce EMT in tumor cells. This feedback loop between EMT and immunosuppression promotes tumor progression. For therapy directly targeting EMT has been challenging, the elucidation of the interactive regulation of EMT and immunosuppression is desirable for developing new therapeutic approaches in cancer. The combination of immune checkpoint inhibitors and immunotherapy targeting immunosuppressive cells could be a promising therapy for EMT.

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 · 10.1158/1078-0432.ccr-20-4459

Cites

23 of 23 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 23 references

Cited by

Metadata

Authors
Taki, M., Abiko, K., Ukita, M., Murakami, R., Yamanoi, K., Yamaguchi, K., Hamanishi, J., Baba, T., Matsumura, N., & Mandai, M.
Venue
Clinical Cancer Research
Published
2021-04-07
Volume
27
Issue
17
Pages
4669-4679
ISSN
  • 1078-0432 (ISSN-L)
  • 1557-3265
OpenAlex
W3150492631
Cited by
351
References
23
FWCI
18.92
Type
journal article
Language
en
Open access
closed

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

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.