Evidence map›Paper›PMID 42318335›Full record

ArticleFrontiers in pharmacology2026

A mechanistic study of bisphenol a exposure on asthma through integrating clinical research, network toxicology, machine learning, and multi-omics.

Ge Gao, Yuqiu Hao, Lin Zhang, Wei Li, Peng Gao, Yuxi Jia

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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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

6 authors.

Ge GaoDepartment of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Jilin University, Changchun, China.
Yuqiu HaoDepartment of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Jilin University, Changchun, China.
Lin ZhangDepartment of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Jilin University, Changchun, China.
Wei LiDepartment of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Jilin University, Changchun, China.
Peng GaoDepartment of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Jilin University, Changchun, China.
Yuxi JiaDepartment of the Second Hospital of Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: BPA exposure is known to be correlated with asthma incidence. This study aimed to investigate the molecular mechanisms underlying bisphenol A (BPA)-induced asthma. Methods: In this study, network toxicology, machine learning, and bioinformatics approaches were integrated with differential expression analysis across multiple datasets. Clinical validation was performed using serum BPA measurement, lung function tests, and quantitative PCR in peripheral blood mononuclear cells. Results: BPA was significantly elevated in the serum of patients with asthma, showing negative correlations with lung function parameters, such as forced expiratory volume in one second (FEV1), the ratio of FEV1 to forced vital capacity (FEV1/FVC), maximal expiratory flow at 75% of FVC (MEF75), and maximal expiratory flow at 25% of FVC (MEF25), while displaying positive correlations with fractional exhaled nitric oxide, eosinophil count, and eosinophil percentage (p < 0.05). We identified 116 potential target genes associated with BPA-induced asthma, with significant enrichment in the JAK-STAT and Chemokine signaling pathways. Using machine learning and Cytoscape analysis, we further narrowed these to 10 core regulatory genes. BPA exhibits strong binding affinity for five target proteins (CCR3, IL2RB, IL2RA, CXCR4, and CCR7). Quantitative PCR in human peripheral blood mononuclear cells confirmed significant upregulation of the expression of corresponding genes (p < 0.05), with partial gene upregulation correlated with serum BPA levels. Discussion: BPA exposure affects lung function and systemic immune-inflammatory responses in asthma, potentially modulating asthma pathogenesis by targeting specific genes and the JAK-STAT signaling pathway. These findings provide preliminary evidence for further exploration of the molecular mechanisms and potential therapeutic targets in BPA-induced asthma.

Indexed as

Asthmabisphenol Aenvironmental healthmachine learningnetwork toxicology

Identifiers

PMID42318335
PMCPMC13272424

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