Cross experimental analysis of microarray gene expression data from volatile organic compounds treated targets

Molecular & Cellular Toxicology

Byung-Moo Lee¹  Won Cheol Yim¹  Kyoungmi Min¹  Youngeun Kwon¹  Deokho Jung¹ 

[1] Dongguk University-Seoul$$

关键词: DNA microarray;   

DOI  :  10.1007/s13273-011-0029-6

学科分类：分子生物学，细胞生物学和基因

来源: Korean Society of Toxicogenomics and Toxicoproteomics

【 摘 要 】

DNA microarrays have revolutionized environmental research by enabling the discovery of genomic markers that reflect the toxic effect of various chemicals and by providing information on the underlying mechanisms. Microarray-based toxicogenomics approaches have become a popular tool to investigate potential risks of exposure to various environmental contaminants at the DNA level. Especially, the analysis of microarray data that are generated under various experimental conditions is critically important for validation of biomarkers and, thus, diagnosis and treatment of environmental targets. Presently, we identified commonly regulated genes whose expression level varied upon exposure to volatile organic compounds (VOCs) by performing cross-experimental analysis of public gene expression datasets using the RankProd algorithm. VOCs are chemical contaminants that often exhibit long-term adverse effects upon chronic exposure. Since VOCs are often used in household items and residential buildings, it is important to understand their effect on human health in a more systematic way. This cross-experiment resulted in a valid set of commonly regulated genes. The functional analysis of these differentially expressed genes (DEGs) generated several significantly over-represented Gene Ontology terms and identified metabolic pathways tightly-associated with cancer development. The functional analysis of identified up-regulated genes (RPL27, RPS6, RPS11, RPS27A, AURKA, FNTA, HSP90AB1) revealed concordance with genes related to various respiratory symptoms such as non-small cell lung cancer. The selected commonly regulated diseaserelated genes were also compared with the DEGs identified in previous analysis performed individually for validation of biomarkers.

【 授权许可】

Unknown