Evidence map›Paper›PMID 28967874›Full record

ReviewLaboratory investigation; a journal of technical methods and pathology2018

Metastasis suppressors: functional pathways.

Imran Khan, Patricia S Steeg

Open access · bronzeAbstract readReview
In one paragraph

Review in Laboratory investigation; a journal of technical methods and pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 6% 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

37 citing papers in PubMed, 1 synthesis or guideline pooled it, 122 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Molecular principles underlying aggressive cancers.Signal transduction and targeted therapy · 2025
    Review
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  18. Is ThereCancers · 2021
    Article
  19. Article
  20. Article
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.

Imran KhanWomen's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Patricia S SteegWomen's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Center for Cancer Research · US

Funding

Molecular Biology of the Metastatic PhenotypeZIASC000892 · NCI · DIVISION OF CLINICAL SCIENCES - NCI · PI STEEG, PATRICIA SCHRIVER · 2009 to 2023
$8.4M
Molecular OncologyZ01BC010641 · NCI · BASIC SCIENCES · PI STEEG, PATRICIA SCHRIVER · 2005 to 2007
$220k
Intramural NIH HHS Z01 BC010641
6 · The paper itself

Abstract

Metastasis is a complex process and a major contributor of death in cancer patients. Metastasis suppressor genes are identified by their ability to inhibit metastasis at a secondary site without affecting the growth of primary tumor. In this review, we have conducted a survey of the metastasis suppressor literature to identify common downstream pathways. The metastasis suppressor genes mechanistically target MAPK, G-protein-coupled receptor, cell adhesion, cytoskeletal, transcriptional regulatory, and metastasis susceptibility pathways. The majority of the metastasis suppressor genes are functionally multifactorial, inhibiting metastasis at multiple points in the cascade, and many operate in a context-dependent fashion. A greater understanding of common pathways/molecules targeted by metastasis suppressor could improve metastasis treatment strategies.

Indexed as

AnimalsCell MovementGene Expression Regulation, NeoplasticHumansModels, GeneticNeoplasm MetastasisNeoplasmsSignal TransductionTumor Suppressor ProteinsTumor Suppressor Proteins

Identifiers

PMID28967874
PMCPMC6545599
OpenAlexW2762631488

What OpenQuestion holds

Textmetadata
LicenceTDM
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.