Next Article in Journal
Suicide among War Veterans
Next Article in Special Issue
DNA Damage and Repair in Human Cancer: Molecular Mechanisms and Contribution to Therapy-Related Leukemias
Previous Article in Journal
Physical Activity Associated with Public Transport Use—A Review and Modelling of Potential Benefits
Previous Article in Special Issue
Secondary Leukemia Associated with the Anti-Cancer Agent, Etoposide, a Topoisomerase II Inhibitor
Open AccessArticle

Using Bioinformatic Approaches to Identify Pathways Targeted by Human Leukemogens

Genes and Environment Laboratory, School of Public Health, University of California, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2012, 9(7), 2479-2503; https://doi.org/10.3390/ijerph9072479
Received: 18 May 2012 / Revised: 25 June 2012 / Accepted: 26 June 2012 / Published: 12 July 2012
(This article belongs to the Special Issue Leukemia Arising from Chemical Exposures and Chemotherapeutic Drugs)
We have applied bioinformatic approaches to identify pathways common to chemical leukemogens and to determine whether leukemogens could be distinguished from non-leukemogenic carcinogens. From all known and probable carcinogens classified by IARC and NTP, we identified 35 carcinogens that were associated with leukemia risk in human studies and 16 non-leukemogenic carcinogens. Using data on gene/protein targets available in the Comparative Toxicogenomics Database (CTD) for 29 of the leukemogens and 11 of the non-leukemogenic carcinogens, we analyzed for enrichment of all 250 human biochemical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The top pathways targeted by the leukemogens included metabolism of xenobiotics by cytochrome P450, glutathione metabolism, neurotrophin signaling pathway, apoptosis, MAPK signaling, Toll-like receptor signaling and various cancer pathways. The 29 leukemogens formed 18 distinct clusters comprising 1 to 3 chemicals that did not correlate with known mechanism of action or with structural similarity as determined by 2D Tanimoto coefficients in the PubChem database. Unsupervised clustering and one-class support vector machines, based on the pathway data, were unable to distinguish the 29 leukemogens from 11 non-leukemogenic known and probable IARC carcinogens. However, using two-class random forests to estimate leukemogen and non-leukemogen patterns, we estimated a 76% chance of distinguishing a random leukemogen/non-leukemogen pair from each other. View Full-Text
Keywords: leukemogen; pathway; Comparative Toxicogenomics Database; carcinogen; clustering leukemogen; pathway; Comparative Toxicogenomics Database; carcinogen; clustering
Show Figures

Graphical abstract

MDPI and ACS Style

Thomas, R.; Phuong, J.; McHale, C.M.; Zhang, L. Using Bioinformatic Approaches to Identify Pathways Targeted by Human Leukemogens. Int. J. Environ. Res. Public Health 2012, 9, 2479-2503.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop