Evidence map›Paper›PMID 40375987›Full record

ArticleFrontiers in immunology2025

Integrative single-cell RNA sequencing and bulk RNA sequencing reveals the characteristics of glutathione metabolism and protective role of GSTA4 gene in pancreatic cancer.

Xinya Jia, Qiang Zhang, Zhe Wang, Jianliang Cao, Anran Song, Chao Lan, Yuepeng Hu

Abstract read
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Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xinya Jia *Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Qiang Zhang *Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zhe Wang *Department of Urology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.
Jianliang CaoDepartment of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Anran SongDepartment of Physical Diagnosis, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Chao LanDepartment of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yuepeng HuDepartment of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Recent studies have increasingly reported abnormal glutathione (GSH) metabolism within the tumor microenvironment across various solid tumors. However, the specific mechanisms underlying aberrant GSH metabolism in pancreatic cancer (PC) remain unclear. This study aims to investigate the prognostic significance of GSH metabolism-related genes in PC and to identify key molecular targets, thereby providing novel perspectives for targeted PC therapy. Methods: The GSH metabolism gene set was retrieved from the KEGG database. Utilizing single-cell transcriptomic data from the GSE205049 dataset, this study analyzed the variation in GSH metabolic signaling intensity across distinct cell types within the tumor microenvironment of PC. Additionally, transcriptomic data from multiple repositories, including TCGA, ICGC, and GEO, comprising a total of 930 patients with PC, were integrated to construct a prognostic molecular classifier related to GSH metabolism. Furthermore, the role of the key gene GSTA4 in PC was experimentally validated through a series of Results: Significant differences in GSH metabolic signaling intensity were observed across various cell types in both normal pancreatic and PC tissues. A prognostic signature comprising six GSH metabolism-related genes (GSTA5, PGD, IDH2, GSTA4, GPX2, and GPX3) was established, wherein a high-risk score was associated with a poorer patient prognosis. Notably, GSTA4 expression was significantly reduced in PC tissues, and higher GSTA4 levels were linked to a favorable prognosis. Conclusion: The GSH metabolism-associated prognostic signature developed in this study effectively identifies high-risk patients with PC. As a prognostic protective factor, GSTA4 exhibits downregulated expression in PC tissues and suppresses tumor proliferation and migration, highlighting its potential as a therapeutic target.

Indexed as

GlutathioneGlutathione TransferasePancreatic NeoplasmsBiomarkers, TumorCell Line, TumorCell ProliferationFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMalePrognosisSequence Analysis, RNASingle-Cell AnalysisTumor MicroenvironmentBiomarkers, TumorGlutathioneGlutathione Transferasebulk RNA sequencingGSH metabolismGSTA4 genepancreatic cancersingle-cell RNA sequencing

Identifiers

PMID40375987
PMCPMC12078301

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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.