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Genomic and algorithm-based predictive risk assessment models for benzene exposure.

Minyun Jiang1,2, Na Cai1,2, Juan Hu3

  • 1School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.

Frontiers in Public Health
|February 6, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies key genes like PTX3 as crucial for detecting occupational benzene exposure. Machine learning models effectively predict benzene-induced damage, aiding in risk assessment.

Keywords:
benzene exposurebenzene-induced damagebioinformaticsmachine learningoccupational healthrisk assessment

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Area of Science:

  • Bioinformatics
  • Genomics
  • Machine Learning

Background:

  • Occupational benzene exposure poses significant health risks.
  • Identifying reliable biomarkers for benzene toxicity is crucial for early detection and management.

Purpose of the Study:

  • To pinpoint key risk genes associated with occupational benzene exposure.
  • To develop genomic and algorithm-based predictive models for risk assessment.

Main Methods:

  • Utilized bioinformatics and machine learning on microarray data (GSE9569, GSE21862).
  • Identified differentially expressed genes (DEGs) and analyzed affected pathways.
  • Applied machine learning for key gene identification and validated findings in human and mouse models.

Main Results:

  • Identified 40 DEGs, primarily in cytokine signaling, lipopolysaccharide response, and chemokine pathways.
  • NFKB1 and PTX3 were validated as significantly altered by benzene exposure.
  • PTX3 emerged as a potential diagnostic biomarker for benzene-induced damage.

Conclusions:

  • Developed predictive risk assessment models informed by oxidative stress markers.
  • These models successfully differentiated between benzene-exposed individuals and controls.