Biomarkers proteomics-based

Biomarker Discovery in Renal Cell Carcinoma Applying Proteome-Based Studies in Combination with Serology... 

Advantages of Diflerent Proteome-Based Technolc ies for the Identification of Biomarkers... 

Advantages of Different Proteome-Based Technologies for the Identification of Biomarkers 227... 

Seliger, B. and Kellner, R. (2002) Design of proteome-based studies in combination with serology for the identification of biomarkers and novel targets. Proteomics 2, 1641-1651. 

Mayr, M., Zhang, J., Greene, A. S., Gutterman, D. D., Perloff, J. K, Ping, P. (2006). Proteomic based development of biomarkers in cardiovascular disease Mechanistic, clinical, and therapeutic insights. Molecular Cf Cellular Proteomics MCP. 

Kovacs A, Sperling E, Lazar J, Balogh A, Kadas J, Szekrenyes A, et al. Fractionation of the human plasma proteome for monoclonal antibody proteomics-based biomarker discovery. Electrophoresis 2011 32(15) 1916—25. 

The availability of genome information for a particular pathogen allows the adaptation of bottom-up [36,37] or top-down [38 0] proteomics methodologies for microorganism identification by MS [41 5] (Fig. 3). These proteomics-based approaches are based on the initial identification of one or more individual protein biomarkers (from, e.g., their corresponding tryptic peptides and/or tandem mass spectra). By inference, the microorganism from which these proteins originate is then identified. For instance, rapid in situ (on a MALDI sample slide) proteolysis of... 

Global proteomic profiling by MS is gaining significant attention as a tool for discovering disease biomarkers. Two basic approaches have been explored. With the first, MS analysis is performed with a material from a specific disease condition and the mass spectra are compared to those of normal individuals or related disease conditions. SELDI-TOF MS gained popularity in this area because of its simplicity and the requirement for only small amounts of samples.38"13 In MS-pattem based disease categorization, the mass spectral patterns are considered reflective of the proteins present in samples from distinct clinical conditions. 

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.

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