Analysis of gene expression for studying tumor progression: the case of glucocorticoid administration |
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Authors: | Mario Huerta José Fernández-Márquez Jose Luis Cabello Alberto Medrano Enric Querol Juan Cedano |
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Institution: | 1. Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain;2. Escola Tècnica Superior de Ingenieria, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain;3. Laboratory of Immunology, Regional Norte, Universidad de la Republica, Gral. Rivera 1350, Salto 50.000, Uruguay |
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Abstract: | BackgroundGlucocorticoids are commonly used as adjuvant treatment for side-effects and have anti-proliferative activity in several tumors but, on the other hand, their proliferative effect has been reported in several studies, some of them involving the spread of cancer. We shall attempt to reconcile these incongruities from the genomic and tissue-physiology perspectives with our findings.MethodsAn accurate phenotype analysis of microarray data can help to solve multiple paradoxes derived from tumor-progression models. We have developed a new strategy to facilitate the study of interdependences among the phenotypes defined by the sample clusters obtained by common clustering methods (HC, SOTA, SOM, PAM). These interdependences are obtained by the detection of non-linear expression-relationships where each fluctuation in the relationship implies a phenotype change and each relationship typology implies a specific phenotype interdependence. As a result, multiple phenotypic changes are identified together with the genes involved in the phenotype transitions. In this way, we study the phenotypic changes from microarray data that describe common phenotypes in cancer from different tissues, and we cross our results with biomedical databases to relate the glucocorticoid activity to the phenotypic changes.Results11,244 significant non-linear expression relationships, classified into 11 different typologies, have been detected from the data matrix analyzed. From them, 415 non-linear expression relationships were related to glucocorticoid activity. Studying them, we have found the possible reason for opposite effects of some stressor agents like dexamethasone on tumor progression and it has been confirmed by literature. This hidden reason has resulted in being linked with the type of tumor progression of the tissues. In the first type of tumor progression found, new cells can be stressed during proliferation and stressor agents increase tumor proliferation. In the second type, cell stress and tumor proliferation are antagonists so, therefore, stressor agents stop tumor proliferation in order to stress the cells. The non-linear expression relationships among DUSP6, FERMT2, FKBP5, EGFR, NEDD4L and CITED2 genes are used to synthesize these findings. |
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Keywords: | HC hierarchical clustering SOTA self-organizing tree algorithm SOM Self-Organizing Map PAM partitioning around medoids PC principal components NEDD4L neural precursor cell expressed developmentally down-regulated 4-like DUSP6 dual specificity phosphatase 6 FERMT2 Fermitin family member 2 ENaC Epithelial Na+ Channel CACNB1 voltage-dependent calcium channel FKBP5 FK506 binding protein 5 EGFR epidermal growth factor receptor CITED2 Cbp/p300-interacting transactivator with Glu/Asp-rich carboxy-terminal domain 2 |
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