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Comparative Analysis of Cancer Stem Cell Gene Expressions with respect to Intratumor Heterogenity

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Title: Comparative Analysis of Cancer Stem Cell Gene Expressions with respect to Intratumor Heterogenity
Author(s): Asan-Liski, Bakiye Hilal
Contributor: University of Helsinki, Faculty of Science, Department of Mathematics and Statistics
Language: English
Acceptance year: 2014
Abstract:
Evolutionary mechanisms in living organisms exist in the most microscopic organisms too, such as populations of tumor cells. This process causes cancer to be one of the most difficult disease to cure for the objective of the evolutionary process taking place within the cells is to make the cancerous population resistant to treatment, while causing them to change in several ways and increase in number. One critical result of this process is called tumor heterogeneity, a term which is used to describe how each tumor population has distinctive properties within and between tumors. Cancer stem cells, small groups of cells that have the capability to form tumors, are one of the foundations of tumor heterogeneity. This study surveys expressions of known Cancer stem cells in different cancer types to note the effects of intratumor heterogeneity (heterogeneity within tumors). This comparative analysis consists of comparisons between normal tissues and cancerous tissues of the same cancer type (colorectal cancer) and between primary cancer tissues and metastatic cancer tissues of the same cancer type (colorectal cancer). The expectation is to observe cancer stem cells to be more expressive in metastatic tissues as they are source of transformation in primary cancers to become more aggressive metastatic cancers. All data used in this study were downloaded from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) website and data analysis were done with R/Bioconductor tools. After extracting the differentially expressed genes between the groups, functional annotation was carried out with The Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Tools.


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