Quantitative glycoproteomic analysis of optimal cutting temperature-embedded frozen tissues identifying glycoproteins associated with aggressive prostate cancer.
Abstract
Prostate cancer is the most common malignancy in men in the United States, and one in seven men with prostate cancer dies of the disease. A major issue of prostate diagnosis is that there is no good method to reliably distinguish aggressive prostate cancer from nonaggressive prostate cancer. This leads to significant unnecessary suffering among prostate cancer patients and massive unnecessary health care expenditures. In this study, we aim to identify glycoproteins associated with aggressive prostate cancer using optimal cutting temperature (OCT)-embedded frozen tissues obtained from patients with known clinical outcome. To eliminate the interference of mass spectrometric analysis by the compounds in OCT and identify extracellular proteins that are likely to serve as biomarkers in body fluids, we employed glycoproteomic analysis using solid-phase extraction of glycopeptides, which allowed the immobilization of glycopeptides to solid support and removal of OCT from sample proteins before releasing the glycopeptides from the solid support for mass spectrometry analysis. Tumor tissues were cryostat microdissected from four cases of aggressive and four cases of nonaggressive prostate tumors, and glycopeptides were isolated and labeled with iTRAQ reagents before the samples were analyzed with LTQ Orbitrap Velos. From the aggressive prostate cancer tissues, we identified the overexpression of three glycoproteins involved in an extracellular matrix remodeling and further examined two glycoproteins, cathepsin L and periostin, using Western blot and immunohistochemistry analyses. This is the first proteomic study to identify proteins potentially associated with aggressive prostate cancer using OCT-embedded frozen tissues. Further study of these proteins will be needed to understand the roles of extracellular matrix proteins in cancer progression and their potential clinical utility in improving diagnosis of aggressive prostate cancer.
EDRN PI Authors
Medline Author List
- Bova GS
- Tian Y
- Zhang H