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Population Cohort-Validated PM_2.5-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure

Yu-Chung Wei¹, Wen-Chi Cheng², Pinpin Lin³

¹Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua City 500207, Taiwan.

|July 25, 2025

Summary

This summary is machine-generated.

Exposure to fine particulate matter (PM2.5) alters gene expression. This study identified five specific genes that increase with PM2.5 exposure, offering potential biomarkers for public health.

Keywords:

PM2.5 biomarker machine learning predictive model transcriptomic profiling

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Area of Science:

Environmental Health
Molecular Biology
Genomics

Background:

Particulate matter (PM2.5) exposure is linked to adverse health outcomes.
Gene expression modulation by PM2.5 is known, but population-based studies on specific gene sets are limited.

Purpose of the Study:

To identify PM2.5-responsive genes using transcriptomic profiling in a mouse model.
To validate these gene expression changes in human cell lines and a population-based cohort.
To develop predictive models for PM2.5 exposure using gene expression profiles.

Main Methods:

Unbiased transcriptomic profiling in a PM2.5-exposed mouse model.
Validation in human cell lines and two cohorts of older adults (≥ 65 years).
Logistic regression and decision tree algorithms for predictive model construction.

Main Results:

Five genes (FAM102B, PPP2R1B, OXR1, ITGAM, PRP38B) showed increased expression with PM2.5 exposure.
Expression patterns were consistent across mouse models, human cell lines, and human cohorts.
Predictive models accurately classified high and low PM2.5 exposure levels.

Conclusions:

Specific gene expression profiles can serve as reliable biomarkers for PM2.5 exposure.
These findings support the potential integration of gene biomarkers into public health surveillance.
Further research may elucidate PM2.5's impact on aging and disease through these genetic pathways.