Evidence map›Paper›PMID 39962573›Full record

ArticleBMC pharmacology & toxicology2025

Identification of benzo(a)pyrene-related toxicological targets and their role in chronic obstructive pulmonary disease pathogenesis: a comprehensive bioinformatics and machine learning approach.

Jiehua Deng, Lixia Wei, Yongyu Chen, Xiaofeng Li, Hui Zhang, Xuan Wei, Xin Feng, Xue Qiu, Bin Liang, Jianquan Zhang

Abstract read
In one paragraph

Article in BMC pharmacology & toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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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

10 authors.

Jiehua DengDepartment of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Zhong Lu, Shenzhen City, Guangdong Province, 518033, China.
Lixia WeiDepartment of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Zhong Lu, Shenzhen City, Guangdong Province, 518033, China. weilx004011@aliyun.com.
Yongyu ChenDepartment of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
Xiaofeng LiDepartment of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
Hui ZhangDepartment of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
Xuan WeiDepartment of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
Xin FengGastroenterology and Respiratory Internal Medicine Department, The Afliated Tumor Hospital of Guangxi Medical University, Nanning, 530021, China.
Xue QiuDepartment of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
Bin LiangDepartment of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
Jianquan ZhangDepartment of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Zhong Lu, Shenzhen City, Guangdong Province, 518033, China. jqzhang2002@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic obstructive pulmonary disease (COPD) pathogenesis is influenced by environmental factors, including Benzo(a)pyrene (BaP) exposure. This study aims to identify BaP-related toxicological targets and elucidate their roles in COPD development.

methodsA comprehensive bioinformatics approach was employed, including the retrieval of BaP-related targets from the Comparative Toxicogenomics Database (CTD) and Super-PRED database, identification of differentially expressed genes (DEGs) from the GSE76925 dataset, and protein-protein interaction (PPI) network analysis. Functional enrichment and immune infiltration analyses were conducted using GO, KEGG, and ssGSEA algorithms. Feature genes related to BaP exposure were identified using SVM-RFE, Lasso, and RF machine learning methods. A nomogram was constructed and validated for COPD risk prediction. Molecular docking was performed to evaluate the binding affinity of BaP with proteins encoded by the feature genes.

resultsWe identified 72 differentially expressed BaP-related toxicological targets in COPD. Functional enrichment analysis highlighted pathways related to oxidative stress and inflammation. Immune infiltration analysis revealed significant increases in B cells, DC, iDC, macrophages, T cells, T helper cells, Tcm, and TFH in COPD patients compared to controls. Correlation analysis showed strong links between oxidative stress, inflammation pathway scores, and the infiltration of immune cells, including aDC, macrophages, T cells, Th1 cells, and Th2 cells. Seven feature genes (ACE, APOE, CDK1, CTNNB1, GATA6, IRF1, SLC1A3) were identified across machine learning methods. A nomogram based on these genes showed high diagnostic accuracy and clinical utility. Molecular docking revealed the highest binding affinity of BaP with CDK1, suggestive of its pivotal role in BaP-induced COPD pathogenesis.

conclusionsThe study elucidates the molecular mechanisms of BaP-induced COPD, specifically highlighting the role of oxidative stress and inflammation pathways in promoting immune cell infiltration. The identified feature genes may serve as potential biomarkers and therapeutic targets. Additionally, the constructed nomogram demonstrates high accuracy in predicting COPD risk, providing a valuable tool for clinical application in BaP-exposed individuals.

Indexed as

Benzo(a)pyreneMachine LearningPulmonary Disease, Chronic ObstructiveComputational BiologyHumansMolecular Docking SimulationNomogramsOxidative StressProtein Interaction MapsBenzo(a)pyreneBenzo(a)pyreneBioinformaticsChronic obstructive pulmonary diseaseImmune scoreKEGGPolycyclic aromatic hydrocarbons

Identifiers

PMID39962573
PMCPMC11834632

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LicenceCC BY-NC-ND
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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.