Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction
Transcriptomic profiling has shown that exposure to PM<sub>2.5</sub>, 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<sub>2.5<...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2305-6304/13/7/562 |
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| author | Yu-Chung Wei Wen-Chi Cheng Pinpin Lin Zhi-Yao Zhang Chi-Hsien Chen Chih-Da Wu Yue Leon Guo Hung-Jung Wang |
| author_facet | Yu-Chung Wei Wen-Chi Cheng Pinpin Lin Zhi-Yao Zhang Chi-Hsien Chen Chih-Da Wu Yue Leon Guo Hung-Jung Wang |
| author_sort | Yu-Chung Wei |
| collection | DOAJ |
| description | Transcriptomic profiling has shown that exposure to PM<sub>2.5</sub>, 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<sub>2.5</sub> 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<sub>2.5</sub> and to identify PM<sub>2.5</sub>-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<sub>2.5</sub>. Logistic regression and decision tree algorithms were then utilized to construct predictive models for PM<sub>2.5</sub> exposure based on these gene expression profiles. Our results indicated that the expression of five genes (<i>FAM102B</i>, <i>PPP2R1B</i>, <i>OXR1</i>, <i>ITGAM</i>, and <i>PRP38B)</i> increased with PM<sub>2.5</sub> exposure in both cell-based assay and population-based cohort studies. Furthermore, the predictive models demonstrated high accuracy in classifying high-and-low PM<sub>2.5</sub> exposure, potentially supporting the integration of gene biomarkers into public health practices. |
| format | Article |
| id | doaj-art-ca22c1b22bff4670a4abfea21df7f491 |
| institution | Kabale University |
| issn | 2305-6304 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj-art-ca22c1b22bff4670a4abfea21df7f4912025-08-20T03:56:49ZengMDPI AGToxics2305-63042025-06-0113756210.3390/toxics13070562Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure PredictionYu-Chung Wei0Wen-Chi Cheng1Pinpin Lin2Zhi-Yao Zhang3Chi-Hsien Chen4Chih-Da Wu5Yue Leon Guo6Hung-Jung Wang7Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua City 500207, TaiwanInstitute of Medical Sciences, Tzu Chi University, Hualien City 970374, TaiwanNational Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan 35041, TaiwanInstitute of Biomedical Sciences and Engineering, Tzu Chi University, Hualien City 970374, TaiwanDepartment of Environmental and Occupational Medicine, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei 100225, TaiwanNational Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan 35041, TaiwanNational Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan 35041, TaiwanInstitute of Medical Sciences, Tzu Chi University, Hualien City 970374, TaiwanTranscriptomic profiling has shown that exposure to PM<sub>2.5</sub>, 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<sub>2.5</sub> 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<sub>2.5</sub> and to identify PM<sub>2.5</sub>-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<sub>2.5</sub>. Logistic regression and decision tree algorithms were then utilized to construct predictive models for PM<sub>2.5</sub> exposure based on these gene expression profiles. Our results indicated that the expression of five genes (<i>FAM102B</i>, <i>PPP2R1B</i>, <i>OXR1</i>, <i>ITGAM</i>, and <i>PRP38B)</i> increased with PM<sub>2.5</sub> exposure in both cell-based assay and population-based cohort studies. Furthermore, the predictive models demonstrated high accuracy in classifying high-and-low PM<sub>2.5</sub> exposure, potentially supporting the integration of gene biomarkers into public health practices.https://www.mdpi.com/2305-6304/13/7/562PM<sub>2.5</sub>transcriptomic profilingbiomarkermachine learningpredictive model |
| spellingShingle | Yu-Chung Wei Wen-Chi Cheng Pinpin Lin Zhi-Yao Zhang Chi-Hsien Chen Chih-Da Wu Yue Leon Guo Hung-Jung Wang Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction Toxics PM<sub>2.5</sub> transcriptomic profiling biomarker machine learning predictive model |
| title | Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction |
| title_full | Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction |
| title_fullStr | Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction |
| title_full_unstemmed | Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction |
| title_short | Population Cohort-Validated PM<sub>2.5</sub>-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction |
| title_sort | population cohort validated pm sub 2 5 sub induced gene signatures a machine learning approach to individual exposure prediction |
| topic | PM<sub>2.5</sub> transcriptomic profiling biomarker machine learning predictive model |
| url | https://www.mdpi.com/2305-6304/13/7/562 |
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