Proposition: The completion of the genomic sequences of a number of prokaryotic and eukaryotic species has catalyzed new research approaches to study the structure, function, and control of biological processes. They are characterized by the systematic and, in many cases, quantitative analysis of all the molecules of a particular type expressed by a cell or tissue. The systematic analysis of proteins has been termed “proteomics”. Cancer proteomics encompasses the identification and quantitative analysis of differentially expressed proteins relative to healthy tissue counterparts at different stages of disease, from preneoplasia to neoplasia. Proteomic technologies can also be used to identify markers for cancer diagnosis, to monitor disease progression, and to identify therapeutic targets. The main proteomic technology platform, two-dimensional gel electrophoresis (2DE), is used to separate complex protein mixtures allowing individual protein spots on the gel to be identified by mass spectrometry. This approach holds promise for identifying disease markers (tumor markers) that could be important in early detection and diagnosis, and in monitoring efficacy of treatment, and eventually leading the way to the design of novel treatments. Experimental Design: In the current study, the protein expression maps (PEMs) of 43 brain cancer tissues and seven nontumour brain tissues derived from epilyptic biopsies were examined using 2D Difference Gel Electroforesis (2D-DIGE). In 2D-DIGE hundreds of proteins are labeled simultaneously with multifluorescence dyes and separated based on their isoelectric point and molecular weight. The gel images were acquired using Typhoon imager, matched with DeCyder platform, and then statistically evaluated. An Unsupervised hierarchical cluster analysis was performed using EPCLUST (Expression Profile data CLUSTering) program. A simple transposed Pearson correlation was used to visualise the clustering. Unsupervised learning enables pattern discovery by organising data into clusters, using recursive partitioning methods. Results and Conclusion: Simple bioinformatic clustering demonstrates that the data from these gel spot maps can be used to distinguish normal (epilepsy) from neoplastic tumor tissues. Our results demonstrate that a set pf protein markers has identified that can differentiate between nontumor brain, grade II, grade III, and grade IV glial tumors. A series of proteins were identified in this preliminary analysis which include known generic tumour markers such as 78 kDa glucose-regulated protein, septin-7, vimentin, annexin I, and several carbonic anhydrase isoforms. The next step will be to undertake an analysis of blood serum samples to try and identify highly expressed markers (identified in the initial studies) in the blood of patients. This may form the basis of an early detection test and may be adapted to a protein chip format.
[Proc Amer Assoc Cancer Res, Volume 46, 2005]