Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute's anticancer screen.
We have analyzed the level of gene coregulation, using gene expression patterns measured across the National Cancer Institute's 60 tumor cell panels (NCI(60)), in the context of predefined pathways or functional categories annotated by KEGG (Kyoto Encyclopedia of Genes and Genomes), BioCarta, and GO (Gene Ontology). Statistical methods were used to evaluate the level of gene expression coherence (coordinated expression) by comparing intra- and interpathway gene-gene correlations. Our results show that gene expression in pathways, or groups of functionally related genes, has a significantly higher level of coherence than that of a randomly selected set of genes. Transcriptional-level gene regulation appears to be on a "need to be" basis, such that pathways comprising genes encoding closely interacting proteins and pathways responsible for vital cellular processes or processes that are related to growth or proliferation, specifically in cancer cells, such as those engaged in genetic information processing, cell cycle, energy metabolism, and nucleotide metabolism, tend to be more modular (lower degree of gene sharing) and to have genes significantly more coherently expressed than most signaling and regular metabolic pathways. Hierarchical clustering of pathways based on their differential gene expression in the NCI(60) further revealed interesting interpathway communications or interactions indicative of a higher level of pathway regulation. The knowledge of the nature of gene expression regulation and biological pathways can be applied to understanding the mechanism by which small drug molecules interfere with biological systems.
Huang
Ruili
R
Laboratory of Computational Technologies, Developmental Therapeutics Program, Screening Technologies Branch, National Cancer Institute at Frederick, National Institutes of Health, Frederick, MD 21702, USA.
Wallqvist
Anders
A
Covell
David G
DG
D000465:Algorithms; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D030561:Databases, Nucleic Acid; D004354:Drug Screening Assays, Antitumor; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects / Q000235:genetics; D006801:Humans; D009316:National Institutes of Health (U.S.); D009369:Neoplasms / Q000235:genetics / Q000473:pathology / Q000517:prevention & control; D015398:Signal Transduction / Q000235:genetics*; D014481:United States