Nontargeted Identification of Organic Components in Fine Particulate Matter Related to Lung Tumor Metastasis Based on an Adverse Outcome Pathway Strategy.
Shaoyang JiYuqiong GuoJinjian DingWenjun HongZhipeng YanZhihong CaiHuifeng YueXinghua QiuNan SangPublished in: Environmental science & technology (2024)
Emerging studies implicate fine particulate matter (PM 2.5 ) and its organic components (OCs) as urgent hazard factors for lung cancer progression in nonsmokers. Establishing the adverse outcome pathway (AOP)-directed nontargeted identification method, this study aimed to explore whether PM 2.5 exposure in coal-burning areas promoted lung tumor metastasis and how we identify its effective OCs to support traceability and control of regional PM 2.5 pollution. First, we used a nude mouse model of lung cancer for PM 2.5 exposure and found that the exposure significantly promoted the hematogenous metastases of A549-Luc cells in lung tissues and the adverse outcomes (AOs), with key events (KEs) including the changed expression of epithelial-mesenchymal transition (EMT) markers, such as suppression of E-cad and increased expression of Fib. Subsequently, using AOs and KEs as adverse outcome directors, we identified a total of 35 candidate chemicals based on the in vitro model and nontargeted analysis. Among them, tributyl phosphate (C 12 H 27 O 4 P), 2-bromotetradecane (C 14 H 29 Br), and methyl decanoate (C 11 H 22 O 2 ) made greater contributions to the AOs. Finally, we clarified the interactions between these OCs and EMT-activating transcription factors (EMT-ATFs) as the molecular initiation event (MIE) to support the feasibility of the above identification strategy. The present study updates a new framework for identifying tumor metastasis-promoting OCs in PM 2.5 and provides solid data for screening out chemicals that need priority control in polluted areas posing higher lung cancer risk.
Keyphrases
- particulate matter
- air pollution
- epithelial mesenchymal transition
- poor prognosis
- mouse model
- signaling pathway
- transforming growth factor
- transcription factor
- coronary artery disease
- induced apoptosis
- high resolution mass spectrometry
- heavy metals
- binding protein
- machine learning
- bioinformatics analysis
- big data
- long non coding rna
- artificial intelligence
- liquid chromatography
- cell death
- high resolution
- human health