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Expressions of hepatic genes, especially IGF-binding protein-1, correlating with serum corticosterone in microarray analysis

  1. Author:
    Cheng, R. Y. S.
    Birely, L. A.
    Lum, N. L.
    Perella, C. M.
    Cherry, J. M.
    Bhat, N. K.
    Kasprzak, K. S.
    Powell, D. A.
    Alvord, W. G.
    Anderson, L. M.
  2. Author Address

    Cheng, RYS, NCI, Comparat Carcinogenesis Lab, Bldg 538, Frederick, MD 21701 USA NCI, Comparat Carcinogenesis Lab, Frederick, MD 21701 USA. SAIC Frederick Inc, Frederick, MD USA. Data Management Serv Inc, Frederick, MD USA.
    1. Year: 2004
  1. Journal: Journal of Molecular Endocrinology
    1. 32
    2. 1
    3. Pages: 257-278
  2. Type of Article: Article
  1. Abstract:

    Microarray technology was evaluated for usefulness in assessing relationships between serum corticosterone and hepatic gene expression. Nine pairs of female Swiss mice were chosen to provide a wide range of serum corticosterone ratios; cDNA microarray analysis (similar to8000 genes) was performed on their livers. A statistical method based on calculation of 99% confidence intervals discovered 32 genes which varied significantly among the livers. Five of these ratios correlated significantly with serum corticosterone ratio, including tyrosine aminotransferase, stress-induced protein, pleiotropic regulator I and insulin-like growth factor-binding protein-1; the latter has a potential role in cancer development. Secondly, linear regression of gene expression vs corticosterone ratios was screened for those with r greater than or equal to 0.8 (P < 0.01), yielding 141 genes, including some known to be corticosterone regulated and others of interest as possible glucocorticoid targets. Half of these significant correlations involved data sets where no microarray ratio exceeded &PLUSMN;1.5. These results showed that microarray may be used to survey tissues for changes in gene expression related to serum hormones, and that even small changes in expression can be of statistical significance in a study with adequate numbers of replicate samples

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