Skip Navigation

University of California-Berkeley: Dataset Details, ID=GSE119533

Superfund Research Program

Toxic Substances in the Environment

Center Director: Martyn T. Smith
Grant Number: P42ES004705
Funding Period: 1987-2027
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

Program Links

Connect with the Grant Recipients

Visit the grantee's eNewsletter page Visit the grantee's Twitter page Visit the grantee's Facebook page View the grantee's Factsheet(491KB)

Title: nCounter analysis of mRNAs associated with occupational benzene exposure using a Nanostring custom assay

Accession Number: GSE119533

Link to Dataset:

Repository: Gene Expression Omnibus (GEO)

Data Type(s): Gene Expression

Experiment Type(s): Expression profiling by array

Organism(s): Homo sapiens

Summary: Previously, using microarrays and mRNA-Sequencing (mRNA-Seq) we found that occupational exposure to a range of benzene levels perturbed gene expression in peripheral blood mononuclear cells. In the current study, we sought to identify gene expression biomarkers predictive of benzene exposure below 1 part per million (ppm), the occupational standard in the U.S. First, we used the nCounter platform to validate altered expression of 30 genes in 33 unexposed controls and 57 subjects exposed to benzene (<1 to 5 ppm). Second, we used SuperLearner (SL) to identify a minimal number of genes for which altered expression could predict <1 ppm benzene exposure, in 44 subjects with a mean air benzene level of 0.55 0.248 ppm (minimum 0.203ppm). nCounter and microarray expression levels were highly correlated (coefficients >0.7, p<0.05) for 26 microarray-selected genes. nCounter and mRNA-Seq levels were poorly correlated for 4 mRNA-Seq-selected genes. Using negative binomial regression with adjustment for covariates and multiple testing, we confirmed differential expression of 23 microarray-selected genes in the entire benzene-exposed group, and 27 genes in the <1 ppm-exposed subgroup, compared with the control group. Using SL, we identified 3 pairs of genes that could predict <1 ppm benzene exposure with cross-validated AUC estimates >0.9 (p<0.0001) and were not predictive of other exposures (nickel, arsenic, smoking, stress). The predictive gene pairs are PRG2/CLEC5A, NFKBI/CLEC5A, and ACSL1/CLEC5A. They play roles in innate immunity and inflammatory responses. Using nCounter and SL, we validated the altered expression of multiple mRNAs by benzene and identified gene pairs predictive of exposure to benzene at levels below the US occupational standard of 1ppm.

Publication(s) associated with this dataset:
  • Schiffman C, McHale CM, Hubbard AE, Zhang L, Thomas R, Vermeulen R, Li G, Shen M, Rappaport SM, Yin S, Lan Q, Smith MT, Rothman N. 2018. Identification of gene expression predictors of occupational benzene exposure. PLoS One 13(10):e0205427. doi:10.1371/journal.pone.0205427 PMID:30300410
to Top