A comparison of proteomic profiles changes during 17beta-estradiol treatment in human prostate cancer PC-3 cell line.
ABSTRACT Human telomerase reverse transcriptase (hTERT) is overexpressed in prostate cancer. Estrogen plays a central role in the development of prostate cancer. hTERT activity has been shown to be increased after estrogen treatment. Although significant efforts have been made to understand the role of estrogen, the telomerase connection with estrogen is poorly understood. In this report, we describe a proteomics approach for investigating the global changes in protein expression in estrogen-treated human prostate cancer PC-3 cells. PC-3 cells were seeded in medium and then treated with estrogen; the protein extract from these cells was used for two-dimensional (2D) gel electrophoresis. The protein spots were subjected to comparative analysis by liquid chromatography/mass spectrometry (LC/MS). We observed that the expression of 17 proteins, including stress-induced phosphoprotein 1 and lamin-A/C was down-regulated, and that the expression of proteins such as subunit alpha of T-complex protein 1, tubulin alpha-1B, and other 13 proteins was up-regulated. These proteins may have been closely associated with estrogen-induced hTERT activity. The expression level of these proteins could be a useful parameter for evaluating the estrogen-induced hTERT activity in clinical specimens of human prostate cancer.
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ABSTRACT: The proteome represents the identity, expression levels, interacting partners, and posttranslational modifications of proteins expressed within any given cell. Proteomic studies aim to census the quantitative and qualitative factors regulating the biological relationships of proteins acting in concert as functional cellular networks. In the field of endocrinology, proteomics has been of considerable value in determining the function and mechanism of action of endocrine signaling molecules in the cell membrane, cytoplasm, and nucleus and for the discovery of proteins as candidates for clinical biomarkers. The volume of data that can be generated by proteomics methodologies, up to gigabytes of data within a few hours, brings with it its own logistical hurdles and presents significant challenges to realizing the full potential of these datasets. In this minireview, we describe selected current proteomics methodologies and their application in basic and translational endocrinology before focusing on mass spectrometry as a model for current progress and challenges in data analysis, management, sharing, and integration.Molecular Endocrinology 08/2012; 26(10):1660-74. DOI:10.1210/me.2012-1180 · 4.20 Impact Factor