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Human Growth Hormone (GH) responsive genes in peripheral blood mononuclear cells (PBMC)

ABSTRACT: Biomarkers of responsiveness to treatment with recombinant human GH (rhGH) have only poorly been described. We aimed at identifying biomarkes of rhGH treatment in children. Our aim was to identify the genes in response to rhGH in human PBMC, that can be used as Biomarkes of growth prediction.

ORGANISM(S): Homo sapiens

PROVIDER: GSE27446 | GEO | 2012/12/01

SECONDARY ACCESSION(S): PRJNA137073

REPOSITORIES: GEO

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human Growth Hormone (GH) responsive genes in peripheral blood mononuclear cells (PBMC)

Project description:Biomarkers of responsiveness to treatment with recombinant human GH (rhGH) have only poorly been described. We aimed at identifying biomarkes of rhGH treatment in children. Our aim was to identify the genes in response to rhGH in human PBMC, that can be used as Biomarkes of growth prediction. Blood was obtained from pediatric patients before and 4 days after the growth hormone therapy. RNA was isolated from the PBMC and subsequently used to perform the whole genome cDNA microarrays. We used the type-1 microarray strategy (RNA isolated before the hormone treatment as a control, and RNA isolated after rhGH treatment). Microarrays represent PBMC RNA obtained from growth hormone treated patients (green channel = baseline, red channel = GH tretmanet for 4 days). Stanford cDNA microarrays were used. Only genes corresponding to spots with fluorescence signals of 1.5-fold over array background in both, the experimental channel (Cy5) and the reference channel (Cy3) and only genes with at least 80% interpretable data in the 9 microarray experiments were considered. Of note, the Cy5/Cy3 ratios on the arrays correspond directly to the fold up-or down-regulation of gene transcription following rhGH treatment. Subsequently, the Cy5/Cy3 ratios on arrays were log2 transformed for further analyses and the dataset was further reduced by considering only those genes with at least a 2-fold up- or down- regulation of transcription in least 3 of the 9 microarray experiments. Microarray experiments and genes were then organized by unsupervised hierarchical clustering using the Pearson correlation metric and average linkage clustering.

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