Evidence map›Paper›PMID 37131406›Full record

ArticleMolecular therapy. Nucleic acids2023

Landscape and significance of human super enhancer-driven core transcription regulatory circuitry.

Chenchen Feng, Chao Song, Yong Jiang, Jun Zhao, Jian Zhang, Yuezhu Wang, Mingxue Yin, Jiang Zhu, Bo Ai, Qiuyu Wang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular therapy. Nucleic acids, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. The role of super-enhancer-driven lncRNAs in cancer.Computational and structural biotechnology journal · 2025
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Super-enhancer omics in stem cell.Molecular cancer · 2024
    Review
  12. Article
  13. Article
  14. Review
  15. Molecular therapy. Nucleic acids · 2023
    Article
  16. Article
  17. 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

15 authors.

Chenchen FengSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Chao SongThe First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Yong JiangMedical Research Institute, Wuhan University, Wuhan 430000, China.
Jun ZhaoSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Jian ZhangSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Yuezhu WangSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Mingxue YinThe First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Jiang ZhuSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Bo AiSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Qiuyu WangThe First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Fengcui QianThe First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Yuexin ZhangThe First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Desi ShangThe First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Jiaqi LiuThe First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Chunquan LiSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A core transcription regulatory circuitry (CRC) is an interconnected self-regulatory circuitry that is formed by a group of core transcription factors (TFs). These core TFs collectively regulate gene expression by binding not only to their own super enhancers (SEs) but also to the SEs of one another. For most human tissue/cell types, a global view of CRCs and core TFs has not been generated. Here, we identified numerous CRCs using two identification methods and detailed the landscape of the CRCs driven by SEs in large cell/tissue samples. The comprehensive biological analyses, including sequence conservation, CRC activity and genome binding affinity were conducted for common TFs, moderate TFs, and specific TFs, which exhibit different biological features. The local module located from the common CRC network highlighted the essential functions and prognostic performance. The tissue-specific CRC network was highly related to cell identity. Core TFs in tissue-specific CRC networks exhibited disease markers, and had regulatory potential for cancer immunotherapy. Moreover, a user-friendly resource named CRCdb (http://www.licpathway.net/crcdb/index.html) was developed, which contained the detailed information of CRCs and core TFs used in this study, as well as other interesting results, such as the most representative CRC, frequency of TFs, and indegree/outdegree of TFs.

Indexed as

core transcription regulatory circuitryMT: Bioinformaticsnetwork analysisplatformsuper enhancertranscription factor

Identifiers

PMID37131406
PMCPMC10149290

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.