Wei Yu-Chung, Cheng Wen-Chi, Lin Pinpin, Zhang Zhi-Yao, Chen Chi-Hsien, Wu Chih-Da, Guo Yue Leon, Wang Hung-Jung
Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua City 500207, Taiwan.
Institute of Medical Sciences, Tzu Chi University, Hualien City 970374, Taiwan.
Toxics. 2025 Jun 30;13(7):562. doi: 10.3390/toxics13070562.
Transcriptomic profiling has shown that exposure to PM, a common air pollutant, can modulate gene expression, which has been linked to negative health effects and diseases. However, there are few population-based cohort studies on the association between PM exposure and specific gene set expression. In this study, we used an unbiased transcriptomic profiling approach to examine gene expression in a mouse model exposed to PM and to identify PM-responsive genes. The gene expressions were further validated in both the human cell lines and a population-based cohort study. Two cohorts of healthy older adults (aged ≥ 65 years) were recruited from regions characterized by differing levels of PM. Logistic regression and decision tree algorithms were then utilized to construct predictive models for PM exposure based on these gene expression profiles. Our results indicated that the expression of five genes (, , , , and increased with PM exposure in both cell-based assay and population-based cohort studies. Furthermore, the predictive models demonstrated high accuracy in classifying high-and-low PM exposure, potentially supporting the integration of gene biomarkers into public health practices.
转录组分析表明，暴露于常见空气污染物细颗粒物（PM）可调节基因表达，这与负面健康影响和疾病有关。然而，关于PM暴露与特定基因集表达之间关联的基于人群的队列研究很少。在本研究中，我们使用无偏转录组分析方法来检查暴露于PM的小鼠模型中的基因表达，并鉴定对PM有反应的基因。这些基因表达在人类细胞系和基于人群的队列研究中均得到进一步验证。从PM水平不同的地区招募了两组健康的老年人（年龄≥65岁）。然后利用逻辑回归和决策树算法，基于这些基因表达谱构建PM暴露的预测模型。我们的结果表明，在基于细胞的检测和基于人群的队列研究中，五个基因（、、、和）的表达均随PM暴露增加。此外，预测模型在区分高PM暴露和低PM暴露方面表现出高准确性，这可能支持将基因生物标志物整合到公共卫生实践中。
