Evidence map›Paper›PMID 38933287›Full record

ReviewFundamental research2023

Noncoding RNAs in tumorigenesis and tumor therapy.

Pingping Zhu, Benyu Liu, Zusen Fan

Abstract readReview
In one paragraph

Review in Fundamental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. RNA biology and therapeutics.Fundamental research · 2023
    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

3 authors.

Pingping ZhuCAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Benyu LiuCAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Zusen FanCAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumorigenesis is a complicated process in which numerous modulators are involved in different ways. Previous studies have focused primarily on tumor-associated protein-coding genes such as oncogenes and tumor suppressor genes, as well as their associated oncogenic pathways. However, noncoding RNAs (ncRNAs), rising stars in diverse physiological and pathological processes, have recently emerged as additional modulators in tumorigenesis. In this review, we focus on two typical kinds of ncRNAs: long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs). We describe the molecular patterns of ncRNAs and focus on the roles of ncRNAs in cancer stem cells (CSCs), tumor cells, and tumor environmental cells. CSCs are a small subset of tumor cells and are generally considered to be cells that initiate tumorigenesis, and dozens of ncRNAs have been defined as critical modulators in CSC maintenance and oncogenesis. Moreover, ncRNAs are widely involved in oncogenetic processes, including sustaining proliferation, resisting cell death, genome instability, metabolic disorders, immune escape and metastasis. We also discuss the potential applications of ncRNAs in tumor diagnosis and therapy. The progress in ncRNA research greatly improves our understanding of ncRNAs in oncogenesis and provides new potential targets for future tumor therapy.

Indexed as

Cancer stem cellsCircRNALncRNANicheTumorigenesis

Identifiers

PMID38933287
PMCPMC11197782

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Registered trials

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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.