Evidence map›Paper›PMID 22572828›Full record

ReviewGut microbes

Microbes-induced EMT at the crossroad of inflammation and cancer.

Paul Hofman, Valérie Vouret-Craviari

Open access · hybridAbstract readReview
In one paragraph

Review in Gut microbes. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 2 pooled it
3.1field-weighted citation impact, top 8% 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

51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 94 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Time-course transcriptomics reveals the impact ofFrontiers in microbiology · 2025
    Article
  6. Understanding myofibroblast origin in the fibrotic lung.Chinese medical journal pulmonary and critical care medicine · 2024
    Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. Review
  15. Article
  16. Review
  17. Review
  18. Review
  19. IsFood and waterborne parasitology · 2022
    Article
  20. PersistentMicroorganisms · 2021
    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 2 institutions in 1 country.

Paul HofmanInstitution for Research on Cancer and Aging, Nice, France
Valérie Vouret-Craviari
Hôpital Pasteur · FRUniversité Côte d'Azur · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is noteworthy that bacterial or viral infections, and the resulting chronic inflammation, have been shown to predispose individuals to certain types of cancer. Remarkably, these microbes upregulated some transcription factors involved in the regulation of the epithelial to mesenchymal transition, referred herein as EMT. EMT is a cellular process that consists in the conversion of epithelial cell phenotype to a mesenchymal phenotype. Under physiological conditions EMT is clearly important for embryogenesis, organ development, wound repair and tissue remodeling. However, EMT may also be activated under pathologic conditions, more particularly in carcinogenesis and metastatic progression. In this review, we make a parallel between microbes- and growth factors- induced transcription factors. A unifying EMT model then emerges that may help in understanding the development of microbial pathogenesis and in defining new potential future therapeutic strategy in treating diseases linked to infections.

Indexed as

AnimalsBacteriaEpitheliumGene Expression RegulationHumansInflammationMesodermMetaplasiaModels, BiologicalNeoplasms

Identifiers

PMID22572828
PMCPMC3427211
OpenAlexW2078900549

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.