Mass spectrometry-based expression profiling of clinical prostate cancer.
The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.
Wright
Michael E
ME
UC Davis Genome Center, Department of Pharmacology and Toxicology, University of California Davis School of Medicine, Davis, CA 95616, USA. mewright@ucdavis.edu
Han
David K
DK
Aebersold
Ruedi
R
D014408:Biomarkers, Tumor / Q000097:blood / Q000378:metabolism*; D006801:Humans; D008297:Male; D013058:Mass Spectrometry / Q000379:methods*; D009363:Neoplasm Proteins / Q000097:blood / Q000378:metabolism*; D011471:Prostatic Neoplasms / Q000097:blood / Q000378:metabolism* / Q000473:pathology; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020543:Proteome