Evidence map›Paper›PMID 41017970›Full record

ArticleJournal of inflammation research2025

Identifying Chemokine System-Related Phenotype to Predict Immune Feature in Pan-Cancer and Prognostic Signature for Lung Adenocarcinoma.

Tianming Zhao, Xu Wu, Shiqi Guo, Jun Nie, Shitao Fang, Liangchao Wang, Xiaojuan Li, Tingting Nie, Kecheng Yao, Xinge Du and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of inflammation research, 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

13 authors.

Tianming Zhao *Department of Respiratory and Critical Care Medicine, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, People's Republic of China.
Xu Wu *Department of Respiratory and Critical Care Medicine, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, People's Republic of China.
Shiqi Guo *Department of Cardiothoracic Surgery, The First College of Clinical Medical of Science, Yichang Central People's Hospital, China Three Gorges University, Yichang, People's Republic of China.
Jun NieDepartment of Cardiothoracic Surgery, The First College of Clinical Medical of Science, Yichang Central People's Hospital, China Three Gorges University, Yichang, People's Republic of China.
Shitao FangDepartment of Cardiothoracic Surgery, The First College of Clinical Medical of Science, Yichang Central People's Hospital, China Three Gorges University, Yichang, People's Republic of China.
Liangchao WangDepartment of Pulmonary and Critical Care Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Xiaojuan LiDepartment of Respiratory and Critical Care Medicine, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, People's Republic of China.
Tingting NieDepartment of Respiratory and Critical Care Medicine, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, People's Republic of China.
Kecheng YaoDepartment of Geriatrics, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, People's Republic of China.ORCID 0000-0001-8977-4165
Xinge DuDepartment of Respiratory and Critical Care Medicine, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, People's Republic of China.
Yingnan WangDepartment of Respiratory and Critical Care Medicine, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, People's Republic of China.
Yurong YuanYichang Emergency Medical Center, Yichang, People's Republic of China.
Jixiang NiDepartment of Pulmonary and Critical Care Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.ORCID 0000-0001-7111-1427

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The chemokine system modulates tumor cell characteristics and influences immune cell function. This research investigates the roles of chemokines and their receptors (CaCRs) across multiple cancers and establishes a reliable CaCRs-based prognostic model for lung adenocarcinoma (LUAD). Methods: Gene expression data were sourced from the UCSC-Xena platform and the GEO database. The chemokine score was calculated using the ssGSEA algorithm. A CaCRs-based prognostic signature was constructed and validated for LUAD. Expression levels of signature genes in lung cancer tissues were verified. Results: Dysregulation of CaCRs expression was observed in multiple cancers. The chemokine score has shown prognostic features in various tumors. In the LUAD cohort, a seven-gene signature of CaCRs (CCR2, CCR4, CCR6, XCR1, CCL20, CXCL17, and XCL2) was constructed as a prognostic model, identifying a poorer prognosis for high-risk groups. mRNA levels of CCR2, CCR4, CCR6, and XCR1 were significantly reduced in lung cancer tissues compared to adjacent normal tissues, while CCL20 was markedly overexpressed in tumor tissues. Furthermore, CCL20 promoted A549 cell proliferation via the MAPK pathway, with JNK inhibitors effectively blocking CCL20-induced proliferation. Conclusion: This study highlights the substantial role of CaCRs in immunity and prognosis. The identified seven-gene signature of CaCRs provides a new prognostic tool for LUAD.

Indexed as

chemokinechemokine receptorLUADpan-cancerprognosis

Identifiers

PMID41017970
PMCPMC12475510

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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.