Evidence map›Paper›PMID 41307603›Full record

ArticleDiscover oncology2025

Exploring the association between air pollutants and non-small cell lung cancer using network toxicology and machine learning.

Qiang Zhao, Zhiqiang Zhao, Kunpeng Du

Abstract read
In one paragraph

Article in Discover oncology, 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.

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2 · The registry

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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

3 authors.

Qiang ZhaoCenter for Precision Cancer Medicine and Translational Research, Tianjin Cancer Hospital Airport Hospital, Tianjin, 300308, China.
Zhiqiang ZhaoHebei Yizhou Cancer Hospital, 301 Guarantee Base, Matou Town, Zhuozhou City, Baoding City, 072750, Hebei Province, China. medsoft@139.com.
Kunpeng DuDepartment of Radiation Oncology, Zhujiang Hospital of Southern Medical University, Guangzhou, 510282, Guangdong Province, China. dkp321098@smu.edu.cn.

Funding

Baoding Science and Technology Plan Project: Research 2441ZF122Science and Technology Talent Cultivation Project from Tianjin Municipal Health Commission RC20189
6 · The paper itself

Abstract

backgroundNon-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide. The urgent need to understand its risk factors and develop effective treatment strategies drives ongoing research in this field. Among various environmental factors, air pollutants have emerged as potential risk factors. Therefore, in-depth exploration is necessary to elucidate their impact on NSCLC pathogenesis.

methodsThis study employs a multifaceted approach combining transcriptomic data analysis, machine learning, and molecular docking simulations to assess the association between air pollutants-carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), benzo[a]anthracene (BaA), benzo[a]pyrene (BaP), and 3-methylcholanthrene (3-MC)-and NSCLC. We identified a total of 30 gene targets associated with air pollutants in NSCLC. These findings highlight significant molecular alterations. Pathway enrichment analysis was then performed to identify crucial pathways implicated in tumorigenesis. Particular emphasis was placed on the cell cycle and p53 signaling pathways.

resultsUsing machine learning, seven core genes (CKS1B, GAPDH, TYMS, AURKA, CCNE1, PARP1, and MGLL) were identified as promising diagnostic markers, achieving an area under the curve (AUC) value exceeding 0.95 during validation. Additionally, molecular docking revealed strong binding interactions between these core genes and selected air pollutants, with molecular dynamics simulations confirming the stability of these interactions.

conclusionsOur findings suggest a significant association between air pollutants and the development of NSCLC and propose potential biomarkers for enhanced diagnostic accuracy, alongside potential therapeutic targets. Future research should prioritize the clinical validation of these findings and the investigation of targeted therapies that consider environmental risk factors, thereby enhancing NSCLC management strategies and patient outcomes.

Indexed as

Air pollutantMachine learningMolecular dockingNetwork toxicologyNon-small cell lung cancer

Identifiers

PMID41307603
PMCPMC12748423

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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.