Evidence map›Paper›PMID 42634462›Full record

ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2026

[Research Advances in the Pulmonary Carcinogenic Effects of 
Atmospheric Fine Particulate Matter Mediated by GPER].

Zhenhua Li, Dingbiao Li

Abstract readReviewEnglish Abstract
In one paragraph

Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2026. 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
–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

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

2 authors.

Zhenhua LiDepartment of Thoracic Surgery, Yan'an Hospital Affiliated to Kunming Medical University, Kunming 650051, China.
Dingbiao LiDepartment of Thoracic Surgery, Yan'an Hospital Affiliated to Kunming Medical University, Kunming 650051, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains the leading cause of cancer-related death worldwide. Fine particulate matter (PM2.5), classified as a group I human carcinogen, has been extensively linked to lung cancer development through epidemiological studies. The G protein-coupled estrogen receptor (GPER) can be activated and induce tumorigenesis by environmental pollutants, and it is significantly overexpressed in lung cancer tissues, highlighting its critical role in disease progression. Research indicates that environmental estrogen-like components and metal ions in PM2.5 can activate GPER, thereby regulating downstream signaling pathways such as mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) and phosphoinositide 3-kinase/protein kinase B (PI3K/AKT). This activation triggers inflammatory responses, oxidative stress, and ferroptosis, ultimately promoting tumor cell proliferation, apoptosis, migration, epithelial-mesenchymal transition, and remodeling of the tumor microenvironment. Collectively, these mechanisms drive the initiation and progression of lung cancer. In summary, GPER serves as a pivotal molecular hub connecting PM2.5 exposure to lung cancer pathogenesis, presenting a promising therapeutic target. Inhibitors targeting GPER may offer novel strategies for the prevention and treatment of lung cancer.
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Indexed as

Lung NeoplasmsParticulate MatterReceptors, EstrogenReceptors, G-Protein-CoupledAnimalsHumansSignal TransductionGPER1 protein, humanParticulate MatterReceptors, EstrogenReceptors, G-Protein-CoupledFine particulate matterG protein-coupled estrogen receptorLung neoplasmsRespiratory system

Identifiers

PMID42634462
PMCPMC13482757

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.