By David W. Petersen, Ernest S. Kawasaki (auth.), Simone Mocellin M.D., Ph.D. (eds.)
Cancer is a heterogeneous disorder in so much respects, together with its cellularity, diverse genetic changes and various medical behaviors. conventional molecular analyses are reductionist, assessing just one or a number of genes at a time, hence operating with a biologic version too particular and restricted to confront a procedure whose scientific consequence might be ruled by way of the mixed impact of many genes. the potential for practical genomics is big, simply because for every test, millions of proper observations will be made concurrently. hence, DNA array, as different excessive throughput applied sciences, could catalyze and finally speed up the improvement of data in tumor phone biology. even if in its infancy, the implementation of DNA array know-how in melanoma examine has already supplied investigators with novel info and interesting new hypotheses at the molecular cascade resulting in cancerogenesis, tumor aggressiveness and sensitivity to antineoplastic brokers. Given the innovative implications that using this expertise may need within the medical administration of melanoma sufferers, ideas of DNA array-based tumor gene profiling have to be truly understood for the information to be thoroughly interpreted and liked. within the current e-book, written through best specialists in each one box, the technical beneficial properties characterizing this robust laboratory software are mentioned, and the functions to date defined within the box of oncology are reviewed.
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Extra info for Microarray Technology and Cancer Gene Profiling
For the hundreds or thousands of genes in the array, these changes should balance out so that the total quantity of RNA hybridizing to the array from each sample is the same. Consequendy, the total integrated intensity computed for all the elements in the array should be the same in both the Cy3 and Cy5 channels. Under this assumption, a normalization factor can be calculated and used to rescale the intensity for each gene in the array. The second normalization method hinges upon regression techniques.
It must be stressed that in every case the results of the assay are dependent upon the quality of the input RNA. For eukaryotic samples, oligonucleotide-based arrays use double-stranded cDNA that is synthesized from total RNA or purified poly-A messenger RNA isolated from tissue or cells. Depending on the amount of starting material, two procedures can be used: the one-cycle or the two-cycle Eukaryotic Target Labeling. 2 |lg to 2 |ig) is first reverse transcribed in the first-strand cDNA synthesis reaction using the a reverse transcriptase, an oncoretroviral enzyme that uses RNA as a template for the synthesis of a single-stranded cDNA.
Rogge L, Bianchi E, Biffi M et al. Transcript imaging of the development of human T helper cells using oligonucleotide arrays. Nat Genet 2000; 25(1):96-101. 14. Brazma A, Vilo J. Gene expression data analysis. FEBS Lett 2000; 480(1): 17-24. 15. Eisen MB, Spellman PT, Brown P O et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998; 95(25): 14863-8. 16. Nikkila J, Toronen P, Kaski S et al. Analysis and visualization of gene expression data using selforganizing maps.
Microarray Technology and Cancer Gene Profiling by David W. Petersen, Ernest S. Kawasaki (auth.), Simone Mocellin M.D., Ph.D. (eds.)