Evidence map›Paper›PMID 23428636›Full record

ReviewMolecular oncology2013

Mouse models for liver cancer.

Latifa Bakiri, Erwin F Wagner

Open access · greenAbstract readReview
In one paragraph

Review in Molecular oncology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed
6.5field-weighted citation impact, top 3% 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

93 citing papers in PubMed, 172 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Liver cancer development driven by the AP-1/c-Jun~Fra-2 dimer through c-Myc.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  12. Orthotopic Model of Hepatocellular Carcinoma in Mice.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

33 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.

Latifa BakiriGenes, Development and Disease Group, F-BBVA Cancer Cell Biology Programme, Spanish National Cancer Research Centre, Melchor Fernandez Almagro 3, 28029 Madrid, Spain. lbakiri@cnio.es
Erwin F Wagner
Spanish National Cancer Research Centre · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC), the most common form of primary liver cancer is the third leading cause of cancer-related cell death in human and the fifth in women worldwide. The incidence of HCC is increasing despite progress in identifying risk factors, understanding disease etiology and developing anti-viral strategies. Therapeutic options are limited and survival after diagnosis is poor. Therefore, better preventive, diagnostic and therapeutic tools are urgently needed, in particular given the increased contribution from systemic metabolic disease to HCC incidence worldwide. In the last three decades, technological advances have facilitated the generation of genetically engineered mouse models (GEMMs) to mimic the alterations frequently observed in human cancers or to conduct intervention studies and assess the relevance of candidate gene networks in tumor establishment, progression and maintenance. Because these studies allow molecular and cellular manipulations impossible to perform in patients, GEMMs have improved our understanding of this complex disease and represent a source of great potential for mechanism-based therapy development. In this review, we provide an overview of the current state of HCC modeling in the mouse, highlighting successes, current challenges and future opportunities.

Indexed as

Disease Models, AnimalAnimalsCell Transformation, NeoplasticGene TargetingGenomicsHumansLiver NeoplasmsMiceTumor Microenvironment

Identifiers

PMID23428636
PMCPMC5528415
OpenAlexW1999815561

What OpenQuestion holds

Textmetadata
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.