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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using bioinformatic approaches to identify pathways targeted by human leukemogens.

Reuben Thomas1, Jimmy Phuong, Cliona M McHale

  • 1Genes and Environment Laboratory, School of Public Health, University of California, Berkeley, CA 94720, USA. reuben.thomas@berkeley.edu

International Journal of Environmental Research and Public Health
|August 2, 2012
PubMed
Summary

Bioinformatic analysis identified common pathways in chemical leukemogens but could not reliably distinguish them from non-leukemogenic carcinogens. Machine learning models showed a 76% chance of differentiating leukemogen and non-leukemogen pairs based on pathway data.

Keywords:
Comparative Toxicogenomics Databasecarcinogenclusteringleukemogenpathway

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Area of Science:

  • Toxicology
  • Bioinformatics
  • Computational Biology

Background:

  • Chemical carcinogens pose risks, including leukemia, but distinguishing leukemogens from other carcinogens remains challenging.
  • Understanding shared biological pathways targeted by leukemogens can inform risk assessment and prevention strategies.

Purpose of the Study:

  • To identify common biochemical pathways targeted by chemical leukemogens.
  • To determine if leukemogens can be distinguished from non-leukemogenic carcinogens using pathway enrichment analysis.

Main Methods:

  • Bioinformatic analysis of gene/protein targets from the Comparative Toxicogenomics Database (CTD).
  • Pathway enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
  • Clustering, support vector machines, and random forests for classification of carcinogens.

Main Results:

  • Leukemogens commonly targeted pathways including xenobiotic metabolism, glutathione metabolism, apoptosis, and MAPK signaling.
  • Unsupervised methods failed to distinguish leukemogens from non-leukemogenic carcinogens based on pathway data.
  • Two-class random forests achieved a 76% accuracy in distinguishing leukemogen and non-leukemogen pairs.

Conclusions:

  • While specific pathways are associated with leukemogenesis, pathway enrichment alone is insufficient for definitive classification.
  • Machine learning approaches show promise for differentiating leukemogens from non-leukemogenic carcinogens, warranting further investigation.