Cancer proteomics applications

Application of 2D-DIGE in Cancer Proteomics Toward Personalized Medicine... 

Fig. 1. Work flow and purpose of cancer profeomics. Profeomic profiles of fumor fissues are infegrated wifh clinico-pafhological data to identify the proteomic aberrations governing cancer phenotypes. The goals of cancer proteomics are understanding of the molecular mechanisms of cancer diversity and developing applications of potential clinical benefit.

Kondo T, Hirohashi S. (2006) Application of highly sensitive fluorescent dyes (CyDye DICE Fluor saturation dyes) to laser micro-dissection and two-dimensional difference gel electrophoresis (2D-DICE) for cancer proteomics. NatProtoc 1,2940-56. 

Kondo T, Seike M, Mori Y, Fujii K, Yamada T, Hirohashi S. (2003) Application of sensitive fluorescent dyes in linkage of laser microdissection and two-dimensional gel electrophoresis as a cancer proteomic study tool. Proteomics 3, 1758-66. 

Wulfkuhle JD, Liotta LA, Petricoin EF. Proteomic applications for the early detection of cancer. Nat Rev Cancer 2003 3(4) 267-275. 

Petricoin, E. F., Ornstein, D. K., Sc Liotta, L. A (2004). Clinical proteomics Applications for prostate cancer biomarker discovery and detection. Urologic Oncology, 22, 322-328. 

E.C. (2002) Genomics and proteomics application of novel technology to early detection and prevention of cancer. Cancer Detect Prev 26, 249-55. 

Wulfkuhle, D. J., et al. Proteomic Applications for the Early Detection of Gancer. Nature Reviews 3 (2003) 267-275. Describes cancer detection with a great review of techniques used in proteomics. 

Thanks to proteomic applications in diagnosis of cancer, several research groups have identified proteomic patterns associated with ovarian, prostafic, colorectal, lung, and other cancers. While the sensitivity and specificity of these patterns are... 

Proteomic Mapping and Clustering of Multiple Samples—Application to Ovarian Cancer Cell Lines... 

Fig. 4. Application of bioinformatics tools to 2D-DIGE data analysis. Proteome data consisting of the normalized spot intensity values are exported from the image analysis software and their correlation with clinicopathological data examined. Using informatics tools including clustering algorithms and machine-learning methods, a novel cancer classification based on proteome data is established, and key proteomic features and proteins corresponding to biomarker candidates are identified.

Many proteomic studies have identified diverse proteins that may be involved in the pathogenic mechanism of disease and which may be disease markers. Most of the applications use expression proteomics to determine expression profiles of proteins in cells and tissues in normal or disease states. In the present study, we analyzed protein expression in cancer tissue samples and corresponding non-cancerous tissue samples to find proteins that might be involved in carcinogenesis or pathogenesis. 

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