Evidence map›Paper›PMID 30975895›Full record

ReviewThe Journal of experimental medicine2019

Context-dependent EMT programs in cancer metastasis.

Nicole M Aiello, Yibin Kang

Open access · bronzeAbstract readReview
In one paragraph

Review in The Journal of experimental medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 347 papers.

0numbers the graph read from it
0cells of the map it votes in
347citing papers in PubMed
35.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

347 citing papers in PubMed, 590 citations in OpenAlex.

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  18. EMT and cancer stem cells: Drivers of therapy resistance and promising therapeutic targets.Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy · 2025
    Review
  19. Article
  20. Article

287 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Nicole M AielloDepartment of Molecular Biology, Princeton University, Princeton, NJ.
Yibin KangDepartment of Molecular Biology, Princeton University, Princeton, NJ ykang@princeton.edu.ORCID 0000-0002-1626-6730
Princeton University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epithelial-mesenchymal transition (EMT) is a developmental process whereby stationary, adherent cells acquire the ability to migrate. EMT is critical for dramatic cellular movements during embryogenesis; however, tumor cells can reactivate EMT programs, which increases their aggressiveness. In addition to motility, EMT is associated with enhanced stem cell properties and drug resistance; thus it can drive metastasis, tumor recurrence, and therapy resistance in the context of cancer. However, the precise requirements for EMT in metastasis have not been fully delineated, with different tumor types relying on discrete EMT effectors. Most tumor cells do not undergo a full EMT, but rather adopt some qualities of mesenchymal cells and maintain some epithelial characteristics. Emerging evidence suggests that partial EMT can drive distinct migratory properties and enhance the epithelial-mesenchymal plasticity of cancer cells as well as cell fate plasticity. This review discusses the diverse regulatory mechanisms and functional consequences of EMT, with an emphasis on the importance of partial EMT.

Indexed as

Epithelial-Mesenchymal TransitionNeoplasm MetastasisAnimalsBiomarkers, TumorCell MovementCell PlasticityEpithelial CellsGene Expression Regulation, NeoplasticHumansMesenchymal Stem CellsMiceNeoplasmsBiomarkers, Tumor

Identifiers

PMID30975895
PMCPMC6504222
OpenAlexW2940057770

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.