Evidence map›Paper›PMID 37174715›Full record

ReviewCells2023

The miR-183 Cluster: Biogenesis, Functions, and Cell Communication via Exosomes in Cancer.

Shuhui Li, Wei Meng, Ziyi Guo, Min Liu, Yanyun He, Yanli Li, Zhongliang Ma

Abstract readReview
In one paragraph

Review in Cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
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  10. Review
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

7 authors.

Shuhui LiLab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai 200444, China.
Wei MengLab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai 200444, China.
Ziyi GuoLab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai 200444, China.
Min LiuLab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai 200444, China.
Yanyun HeExperimental Center of Life Sciences, Shanghai University, Shanghai 200444, China.
Yanli LiLab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai 200444, China.
Zhongliang MaLab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai 200444, China.ORCID 0000-0002-4429-5213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is one of the leading causes of human death. MicroRNAs have been found to be closely associated with cancer. The miR-183 cluster, comprising miR-183, miR-96, and miR-182, is transcribed as a polycistronic miRNA cluster. Importantly, in most cases, these clusters promote cancer development through different pathways. Exosomes, as extracellular vesicles, play an important role in cellular communication and the regulation of the tissue microenvironment. Interestingly, the miR-183 cluster can be detected in exosomes and plays a functional regulatory role in tumor development. Here, the biogenesis and functions of the miR-183 cluster in highly prevalent cancers and their relationship with other non-coding RNAs are summarized. In addition, the miR-183 cluster in exosomes has also been discussed. Finally, we discuss the miR-183 cluster as a promising target for cancer therapy. This review is expected to provide a new direction for cancer treatment.

Indexed as

ExosomesMicroRNAsNeoplasmsCell CommunicationHumansTumor MicroenvironmentMicroRNAsMIRN183 microRNA, humancancerexosomemiR-183 clusternoncoding RNAs

Identifiers

PMID37174715
PMCPMC10177187

What OpenQuestion holds

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LicenceCC BY
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.