Evidence map›Paper›PMID 41777063›Full record

ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2025

[p53-SOAT1 Axis: A Novel Target for Tumor Lipid Metabolism and Therapy].

Xi Yu, Yuting Xiao, Bowen Xu, Xinhui Li, Hongmei Wang, Dunqiang Ren

Abstract readReviewEnglish Abstract
In one paragraph

Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Xi YuDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Yuting XiaoDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Bowen XuDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Xinhui LiDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Hongmei WangDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Dunqiang RenDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer treatment is a significant challenge facing global medicine, with complex molecular mechanisms and drug resistance being key factors limiting treatment outcomes. Abnormal lipid metabolism is one of the important characteristics of tumors, providing metabolic support for the growth, proliferation, migration, and invasion of tumor cells. Tumor suppressor p53 protein and sterol O-acyltransferase 1 (SOAT1) play an important role in regulating cellular lipid metabolism and are closely related to the occurrence, development, and prognosis of various tumors. p53 protein regulates tumor lipid metabolism through multiple signaling pathways, while SOAT1, as a key enzyme in cholesterol esterification, is highly expressed in many tumors and accelerates tumor progression. Recent studies have shown that there may be a functional association between p53 protein and SOAT1, coordinating the regulation of lipid homeostasis in tumor cells. This article reviews the research progress on p53 protein and SOAT1 in tumor lipid metabolism, focusing on the potential mechanisms of action of the p53-SOAT1 axis in tumor development, and prospects its application prospects as a target for cancer treatment.
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Indexed as

Lipid MetabolismMolecular Targeted TherapyNeoplasmsSterol O-AcyltransferaseTumor Suppressor Protein p53AnimalsHumansSignal TransductionSterol O-Acyltransferasesterol O-acyltransferase 1Tumor Suppressor Protein p53Lipid metabolismp53SOAT1Tumor

Identifiers

PMID41777063
PMCPMC12972576

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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.