Evidence map›Paper›PMID 10439834›Full record

ReviewMolecular pathology : MP1999

Liver enriched transcription factors and differentiation of hepatocellular carcinoma.

Y Hayashi, W Wang, T Ninomiya, H Nagano, K Ohta, H Itoh

Open access · bronzeAbstract readReview
In one paragraph

Review in Molecular pathology : MP, 1999. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

32 citing papers in PubMed, 95 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Y HayashiFirst Division of Pathology, Kobe University School of Medicine, Japan. hayashi@mailgate.kobe-u.ac.jp
W Wang
T Ninomiya
H Nagano
K Ohta
H Itoh
Kobe University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of a complex organism relies on the precise temporal and spacial expression of its genome in many different cell types. The unique phenotype of hepatocytes arises from the expression of genes in a liver specific fashion, which is controlled primarily at the level of mRNA synthesis. By analysing DNA sequences implicated in liver specific transcription, it has been possible to identify members of the nuclear proteins, such as the liver enriched transactivating factors, hepatic nuclear factor 1(HNF-1), HNF-3, HNF-4, HNF-6, CCAAT/enhancer binding protein (C/EBP), and D binding protein (DBP), which are key elements in the liver specific transcriptional regulation of genes. Each of these factors is characterised by DNA binding domains that bind to unique DNA sequences (cis-acting factors) in the promoter and enhancer regions of genes expressed in terminally differentiated hepatocytes (such as, albumin, alpha 1-antitrypsin, transthyretin, alpha-fetoprotein). The determination of the tissue distribution of these factors and analysis of their hierarchical relations has led to the hypothesis that the cooperation of liver enriched transcription factors with the ubiquitous transactivating factors is necessary, and possibly even sufficient, for the maintenance of liver specific gene transcription. With the increase in information about transcriptional regulation, it should be possible to evaluate fully the clinicopathological usefulness of transcription factors in the diagnosis and treatment of hepatocellular carcinoma.

Indexed as

Carcinoma, HepatocellularCell DifferentiationDNA-Binding ProteinsHumansLiver NeoplasmsTranscription FactorsTranscription, GeneticDNA-Binding ProteinsTranscription Factors

Identifiers

PMID10439834
PMCPMC395665
OpenAlexW2082013454

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.