Literature DB >> 11721637

Proteomic approaches to biomarker discovery in prostate and bladder cancers.

B L Adam1, A Vlahou, O J Semmes, G L Wright.   

Abstract

Proteomic technologies, including high resolution two-dimensional electrophoresis (2-DE), antibody/protein arrays, and advances in mass spectrometry (MS), are providing the tools needed to discover and identify disease associated biomarkers. Although application of these technologies to search for potential diagnostic/prognostic biomarkers associated with prostate and bladder cancer have been somewhat limited to date, proteins either overexpressed or underexpressed have been detected in both these urological cancers. Recent advances in mass spectrometry, especially platforms that permit rapid "fingerprint" profiling of multiple biomarkers, and tandem mass spectrometers for protein identification, will most assuredly enhance the discovery, identification, and characterization of potential cancer associated biomarkers. Furthermore, application of laser capture microdissection microscopes has provided a rapid and reproducible approach to procure pure populations of cells. This technology coupled to 2-DE and MS has significantly aided the elucidation of the differential expression profiles between disease, benign and normal prostate and bladder cell populations. Finally, development and application of learning algorithms and bioinformatics to the data generated by these proteomic technologies will be essential in determining the clinical potential of a protein biomarker. The purpose of this review is to provide the reader with an overview of the application of these technologies in the search and identification of potential diagnostic/prognostic biomarkers for prostate and bladder cancers.

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Year:  2001        PMID: 11721637     DOI: 10.1002/1615-9861(200110)1:10<1264::AID-PROT1264>3.0.CO;2-R

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  19 in total

Review 1.  Current perspectives in cancer proteomics.

Authors:  Miriam V Dwek; Sarah L Rawlings
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Review 2.  Clinical applications of proteomics: proteomic pattern diagnostics.

Authors:  Emanuel E Petricoin; Cloud P Paweletz; Lance A Liotta
Journal:  J Mammary Gland Biol Neoplasia       Date:  2002-10       Impact factor: 2.673

3.  Modern Tumor Marker Discovery in Urology: Surface Enhanced Laser Desorption and Ionization (SELDI).

Authors:  Matthew B Gretzer; Alan W Partin; Daniel W Chan; Robert W Veltri
Journal:  Rev Urol       Date:  2003

4.  SELDI-TOF MS profiling of serum for detection of laryngeal squamous cell carcinoma and the progression to lymph node metastasis.

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Journal:  J Cancer Res Clin Oncol       Date:  2008-01-17       Impact factor: 4.553

5.  New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples.

Authors:  Thomas Göbel; Sonja Vorderwülbecke; Katarzyna Hauck; Holger Fey; Dieter Häussinger; Andreas Erhardt
Journal:  World J Gastroenterol       Date:  2006-12-21       Impact factor: 5.742

6.  Comparative proteomic analysis of non-small-cell lung cancer and normal controls using serum label-free quantitative shotgun technology.

Authors:  Jun Pan; Hai-Quan Chen; Yi-Hua Sun; Jun-Hua Zhang; Xiao-Yang Luo
Journal:  Lung       Date:  2008-05-09       Impact factor: 2.584

7.  Towards proteome standards: the use of absolute quantitation in high-throughput biomarker discovery.

Authors:  Tzu-Chiao Chao; Nicole Hansmeier; Rolf U Halden
Journal:  J Proteomics       Date:  2010-04-22       Impact factor: 4.044

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Authors:  Ana P Barba-de la Rosa; Erika Briones-Cerecero; Ofelia Lugo-Melchor; Antonio De León-Rodríguez; Leticia Santos; Julio Castelo-Ruelas; Alejandra Valdivia; Patricia Piña; Alicia Chagolla-López; Daniel Hernandez-Cueto; Alejandra Mantilla; Minerva Lazos-Ochoa; Beatriz Gonzalez-Yebra; Mauricio Salcedo
Journal:  J Cancer Res Clin Oncol       Date:  2011-11-27       Impact factor: 4.553

9.  Ovarian cancer classification based on mass spectrometry analysis of sera.

Authors:  Baolin Wu; Tom Abbott; David Fishman; Walter McMurray; Gil Mor; Kathryn Stone; David Ward; Kenneth Williams; Hongyu Zhao
Journal:  Cancer Inform       Date:  2007-02-17

10.  A simulation-approximation approach to sample size planning for high-dimensional classification studies.

Authors:  Perry de Valpine; Hans-Marcus Bitter; Michael P S Brown; Jonathan Heller
Journal:  Biostatistics       Date:  2009-02-21       Impact factor: 5.899

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