Abstract
The availability of microarray technologies has enabled biomedical researchers to explore expression levels of a complete genome simultaneously. The analysis of gene expression patterns can explain the biological basis of several pathological processes. Deepening in the understanding of the molecular processes underlying colorectal cancer might become of interest for the advance of its clinical management. This work presents the analysis of microarrays data using colon cancer samples in order to determine the differentially expressed genes underlying this disease process. The comparison of gene expression levels using a complete genome approach of tumor samples versus healthy controls allows the definition of a set of genes involved in the differentiation of both tissues. The analysis of these differentially expressed genes using Gene Ontology analysis permits the location of most prevalent processes that are altered during under this disease.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Fodor, S., Read, J., Pirrung, M., Stryer, L., Lu, A., Solas, D.: Light-directed, spatially addressable parallel chemical synthesis. Science 251, 767–773 (1991)
Pease, A., Solas, D., Sullivan, E., Cronin, M., Holmes, C., Fodor, S.: Lightgenerated oligonucleotide arrays for rapid DNA sequence analysis. Proceedings of the National Academy of Sciences 91, 5022–5026 (1994)
Schena, M., Shalon, D., Davis, R., Brown, P.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 20, 1008–1017 (1995)
Lin, S., Johnson, K.: Methods of Microarray Data Analysis. Kluwer Academic Publishers, Dordrecht (2000)
Brazma, A., Vilo, J.: Gene expression data analysis. Microbes and Infection 3, 823–829 (2001)
DeRisi, J., Penland, L., Brown, P., Bittner, M., Meltzer, P., Ray, M., Chen, Y., Su, Y., Trent, J.: Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics 14, 457–460 (1996)
Causton, H., Quackenbush, J., Brazma, A.: A Beginner’s Guide. Microarray Gene Expression and Data Analysis. Blackwell Publishing, Malden (2003)
Simon, R., Lam, A.P., Ngan, M., Gibiansky, L., Shrabstein, P.: The BRB ArrayTools development team (2005), http://linus.nci.nih.gov/BRB-ArrayTools.html
Al-Shahrour, F., Diaz-Uriarte, R., Dopazo, J.: FatiGO: a web tool for finding significant associations of Gene Ontology terms to groups of genes. Bioinformatics 20, 578–580 (2004)
Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C.A., Causton, H.C., Gaasterland, T., Glenisson, P., Holstege, F.C., Kim, I.F., Markowitz, V., Matese, J.C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M.: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics 29, 365–371 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Hernández, O. et al. (2005). Microarray Data Analysis and Management in Colorectal Cancer. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_39
Download citation
DOI: https://doi.org/10.1007/11573067_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29674-4
Online ISBN: 978-3-540-31658-9
eBook Packages: Computer ScienceComputer Science (R0)