Quantitative Glycoproteomics and Aggressive Prostate Cancer

Serum prostate-specific antigen blood testing, the standard screening assay for prostate cancer, does not distinguish between aggressive and indolent prostate cancer. In the absence of reliable screening methods for aggressive prostate cancer, researchers sought to investigate glycoprotein markers specific to aggressive prostate cancer.1

Using a solid-phase extraction of N-linked glycopeptide (SPEG) method, the researchers performed mass spectrometry (MS) analysis on glycoproteins in prostate specimens. The SPEG method enables isolation of peptides with N-linked glycosylation sites and facilitates proteomic analysis of frozen tissues by removing optimal cutting temperature medium.2

In the current study, using label-free LC-MS/MS glycoproteomic analysis, the researchers identified a total of 350 unique N-linked glycopeptides — 17 associated with aggressive prostate cancer. They analyzed formerly N-linked glycopeptides from non-aggressive and aggressive prostate tumors, using a linear ion trap mass spectrometer (LTQ, Thermo Scientific, Waltham, MA) after separation with a 15 cm × 75 μm C18 column (5 μm particles with 100 Å pore size). The 350 unique N-linked glycopeptides containing consensus N-linked glycosylation motif represent 242 unique glycoproteins. The researchers then identified eleven upregulated glycoproteins and six downregulated glycoproteins in aggressive prostate cancer, five of which differed with statistical significance between aggressive and non-aggressive tumors.

Using enzyme linked immunosorbent (ELISA) assays, the investigators then evaluated four proteins from the proteomic results — cartilage oligomeric matrix protein (COMP), periostin, membrane primary amine oxidase (VAP-1) and cathepsin L. COMP plays a role in prostate tumorigenesis. It is expressed by prostate fibroblasts with highly increased mRNA expression in grade 3 reactive stroma,3 and during transdifferentiation of fibroblasts.4

The findings confirmed the proteomic analysis. Both COMP expression and periostin expression were significantly higher in aggressive prostate tumors. The average COMP expression was approximately seven-fold in aggressive tumor tissue compared to normal prostate tissue, and almost two-fold when compared with non-aggressive tumor tissue. The average periostin expression was almost 254-fold in aggressive tumors compared to normal prostate tissue, and about 2.6-fold compared to non-aggressive tumor tissue. The investigators also observed a 2.5-fold decrease in the average VAP-1 expression in aggressive tumors compared to normal prostate tissue, and a 1.4-fold decrease compared to non-aggressive tumors. The expression of VAP-1 was the lowest in prostate metastases, significantly decreased from aggressive tumor tissue by nearly 6.5-fold. A high throughput antibody-independent technique — such as specific reaction monitoring — may be better than ELISA to expedite marker evaluation of large numbers of candidate proteins.5

“This study provides a workflow for biomarker discovery, prioritization and evaluation of aggressive prostate cancer markers using tissue specimens,” the researchers claim, noting that their data “suggest [an] increase in COMP and periostin and decrease in VAP-1 expression in the prostate may be associated with aggressive prostate cancer.”

 References

  1. Chen. J., et al. (2013, May 29) “Identification, prioritization and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody-based assays on tissue specimens,” Proteomics, doi: 10.1002/pmic.201200541.
  2. Zhang, H., et al. (2005) “High throughput quantitative analysis of  serum proteins using glycopeptide capture and liquid chromatography mass spectrometry,” Molecular and Cellular Proteomics, 4 (pp. 144–55), doi: 10.1074/mcp.M400090-MCP200.
  3. Dakhova, O., et al. (2009) “Global gene expression analysis of reactive stroma in prostate cancer,” Clinical Cancer Research, 15 (pp. 3979–89), doi: 10.1158/1078-0432.CCR-08-1899.
  4. Untergasser, G., et al. (2005) “Profiling molecular targets of TGF-beta1 in prostate fibroblast-to-myofibroblast transdifferentiation,” Mechanisms of Ageing and Development, 126 (pp. 59–69), http://dx.doi.org/10.1016/j.mad.2004.09.023.
  5. Prakash, A., et al. (2010) “Platform for establishing  interlaboratory reproducibility of selected reaction monitoring-based mass spectrometry peptide assays,” Journal of Proteome Research,  9 (pp. 6678–88), doi: 10.1021/pr100821m.

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