Evidence map›Paper›PMID 40845073›Full record

ArticleJournal of cellular and molecular medicine2025

Investigating Liquid-Liquid Phase Separation in Lung Adenocarcinoma to Improve Prognostic Accuracy and Treatment Efficacy.

Zipei Song, Yuting Li, Pengpeng Zhang, Ke Wei, Miaolin Zhu, Yuheng Wang, Zhihua Li, Liang Chen, Jianan Zheng

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Zipei SongDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID 0009-0001-3388-7432
Yuting LiDepartment of Oncology, the First Medical Center, Chinese PLA General Hospital/Medical School of Chinese PLA, Beijing, China.
Pengpeng ZhangDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.ORCID 0000-0003-1488-265X
Ke WeiDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Miaolin ZhuDepartment of Oncology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, China.
Yuheng WangDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zhihua LiDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Liang ChenDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jianan ZhengDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Funding

National Natural Science Foundation of China 81972175
6 · The paper itself

Abstract

Liquid-Liquid Phase Separation (LLPS) refers to the separation of biomacromolecules into separate liquid phases within the cells, plays a critical role in lung cancer pathogenesis. Using machine learning, we developed an LLPS-associated signature (LLPSAS) based on 79 key genes. The LLPSAS demonstrated superior prognostic performance compared to 140 existing lung adenocarcinoma (LUAD) prognostic models. Patients stratified by LLPSAS risk scores revealed significantly lower overall survival in the high-risk group. Comparative analysis between the high-risk and low-risk groups showed distinct pathway enrichment, genomic alterations, tumour immune microenvironment (TIME) profiles, immunotherapy responses and drug sensitivities. The low-risk group exhibited an inflamed TIME, suggesting potentially better immunotherapy response. Furthermore, potential effective small molecule drugs were identified for high-risk patients. Finally, immunohistochemistry confirmed upregulation of LLPS-associated proteins (PLK1, HMMR, PRC1) in LUAD tissues, and immunofluorescence validated their LLPS occurrence. Conclusively, the LLPSAS provides a valuable tool for LUAD prognosis and treatment optimisation.

Indexed as

Adenocarcinoma of LungLung NeoplasmsBiomarkers, TumorCell Cycle ProteinsFemaleGene Expression Regulation, NeoplasticHumansMachine LearningMalePhase SeparationPolo-Like Kinase 1PrognosisTreatment OutcomeTumor MicroenvironmentBiomarkers, TumorCell Cycle ProteinsPolo-Like Kinase 1immunotherapy responseliquid–liquid phase separationlung adenocarcinoma (LUAD)machine learningprognosissingle‐cell RNA‐seq

Identifiers

PMID40845073
PMCPMC12372984

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.