Evidence map›Paper›PMID 29754476›Full record

ReviewMolecules and cells2018

Targeting Super-Enhancers for Disease Treatment and Diagnosis.

Ha Youn Shin

Abstract readReview
In one paragraph

Review in Molecules and cells, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

75 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
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  6. Article
  7. Article
  8. Introducing the EpGEpigenomes · 2025
    Article
  9. Article
  10. Article
  11. Super-enhancer profiling reveals ThPOK/ZBTB7B, a CD4Cellular and molecular life sciences : CMLS · 2025
    Article
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  13. Review
  14. Article
  15. Article
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  20. Review

15 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

1 author.

Ha Youn ShinDepartment of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The transcriptional regulation of genes determines the fate of animal cell differentiation and subsequent organ development. With the recent progress in genome-wide technologies, the genomic landscapes of enhancers have been broadly explored in mammalian genomes, which led to the discovery of novel specific subsets of enhancers, termed super-enhancers. Super-enhancers are large clusters of enhancers covering the long region of regulatory DNA and are densely occupied by transcription factors, active histone marks, and co-activators. Accumulating evidence points to the critical role that super-enhancers play in cell type-specific development and differentiation, as well as in the development of various diseases. Here, I provide a comprehensive description of the optimal approach for identifying functional units of super-enhancers and their unique chromatin features in normal development and in diseases, including cancers. I also review the recent updated knowledge on novel approaches of targeting super-enhancers for the treatment of specific diseases, such as small-molecule inhibitors and potential gene therapy. This review will provide perspectives on using super-enhancers as biomarkers to develop novel disease diagnostic tools and establish new directions in clinical therapeutic strategies.

Indexed as

AnimalsCell DifferentiationDiseaseEnhancer Elements, GeneticHumanscell identityclinical therapeuticsdisease diagnosisdiseasessuper-enhancer

Identifiers

PMID29754476
PMCPMC6030247

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.