Evidence map›Paper›PMID 41208994›Full record

ArticleFrontiers in immunology2025

Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.

Xingchao Liu, Yinghui Zhang, Yangke He, Liang Liang

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 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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.

Xingchao Liu *Organ Transplantation Center, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yinghui Zhang *Department of Gastroenterology and Hepatology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yangke HeDepartment of Oncology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Liang LiangDepartment of Oncology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Liver hepatocellular carcinoma (LIHC) is a leading cause of cancer-related mortality, with an immunosuppressive tumor microenvironment (TME) contributing to therapeutic resistance. Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood. Methods: We integrated bulk RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database (GSE39791) alongside scRNA-seq data from GSE149614 and GSE290925. Neutrophils were annotated based on specific marker genes (FCGR3B, CSF3R) and classified into three metabolic states: high arginine state (HAS), intermediate arginine state (DTAS), and low arginine state (LAS) using arginine metabolism-related gene sets. Differentiation trajectories were reconstructed via CytoTRACE and monocle2. Intercellular communication was analyzed using CellChat, while machine learning, incorporating seven different algorithms, was applied to identify key regulatory genes. Results: scRNA-seq analysis revealed three distinct neutrophil subgroups: high (HAS), intermediate (DTAS), and low (LAS) arginine metabolism states. The proportion of LAS neutrophils was significantly enriched in tumor tissues compared to normal tissues (p < 0.001). Trajectory analysis indicated that LAS neutrophils exhibited a less differentiated state. From this landscape, ATP11B and PADI4 were identified as key genes, with PADI4 expression being approximately 3-fold higher in HAS compared to LAS neutrophils. Functional studies demonstrated that silencing PADI4 in LIHC cell lines inhibited cell proliferation by approximately 50% at 96 hours, increased apoptosis by 2-fold, and reduced cell invasion by 50%. Conclusions: Arginine metabolism shapes neutrophil polarization in the LIHC TME. Targeting metabolic pathways may provide new therapeutic strategies to modulate the immune landscape and improve patient outcomes.

Indexed as

ArginineCarcinoma, HepatocellularLiver NeoplasmsNeutrophilsCell Line, TumorGene Expression Regulation, NeoplasticHumansTumor MicroenvironmentArgininearginine metabolismhepatocellular carcinomaneutrophil heterogeneityPADI4single-cell transcriptomicstumor microenvironment

Identifiers

PMID41208994
PMCPMC12589071

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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.