Evidence map›Paper›PMID 40211955›Full record

ReviewCell proliferation2025

Membraneless Organelles and Phase Separation in Tumours: Mechanisms and Prospects.

Hao Yang, Zhong Chu, Shuwen Han, Yuefen Pan

Abstract readReview
In one paragraph

Review in Cell proliferation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Spatial protein redistribution: wandering but not lost.Cellular and molecular life sciences : CMLS · 2025
    Review
  3. 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

4 authors.

Hao YangDepartment of Oncology, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, Zhejiang Province, China.ORCID https://orcid.org/0000-0003-4615-8033
Zhong ChuDepartment of Oncology, Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Shuwen HanDepartment of Oncology, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, Zhejiang Province, China.ORCID https://orcid.org/0000-0001-6180-9565
Yuefen PanDepartment of Oncology, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, Zhejiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Membraneless organelles (MLOs) are a type of subcellular compartment structure discovered in eukaryotes in recent years. They are mainly formed through the liquid-liquid phase separation (LLPS) and aggregation of macromolecular substances such as proteins or nucleic acids in cells. When cells are stimulated, they initiate a series of stress responses including gene transcription, RNA metabolism, translation, protein modification and signal transduction to maintain homeostasis. The dysregulation of these cellular processes is a key event in the occurrence and development of cancer. This article provides an overview of the structure and function of membraneless organelles, as well as the mechanisms of phase separation, to summarise the latest research progress on phase separation in tumours. It focuses on the role and molecular mechanism of LLPS in the development of tumours, with the aim of providing new theoretical references for developing drug action targets and innovative treatment strategies.

Indexed as

NeoplasmsOrganellesAnimalsHumansPhase SeparationSignal TransductioncancermechanismMembraneless organellesphase separationtherapy

Identifiers

PMID40211955
PMCPMC12336459

What OpenQuestion holds

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LicenceCC BY
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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.