Chromosome correlation maps of gene expression signatures could provide useful information on gene regulatory mechanisms in urinary bladder cancer

<p>Chromosome correlation maps display correlations between gene expression patterns on the same chromosome and are considered of major importance in the understanding of how gene expression is regulated. It has not yet been elucidated based on chromosome correlation, whether gene expression among same chromosomes from different tumor samples is governed by similar patterns; and if it exists we do not know whether it is of linear nature or not. In the present study we used urinary bladder carcinoma as the model of our hypothesis. Following microarray experimentation in combination with raw microarray data extraction from the GEO, we collected a data cohort of 129 bladder cancer and 17 normal samples and performed network analysis for the co-deregulated genes using Ingenuity Pathway Analysis (IPA). Chromosome mapping, mathematical modeling and data simulations were performed using the WebGestalt and Matlab software. The top deregulated molecules among all bladder cancer samples were implicated in the PI3K/AKT signaling, cell cycle, Myc-mediated apoptosis signaling and ERK5 signaling pathways. Their most prominent molecular and cellular functions were related to cell cycle, cell death, gene expression, molecular transport and cellular growth and proliferation. Chromosome correlation maps allowed us to detect significantly co-expressed genes along the chromosomes. We identified strong correlations among tumors of Tα-grade 1, as well as for those of Tα-grade 2, in chromosomes 1, 2, 3, 7, 12 and 19. Chromosomal domains of gene co-expression were revealed for the normal tissues, as well. The expression data were further simulated, exhibiting an excellent fit (0.7</p

Thermodynamic transitions on metabolism and proliferation of glucocorticoid-treated acute leukemia cells

<p>Glucocorticoids play an essential part in anti-leukemic therapies. Resistance is considered crucial for disease prognosis. Glucocorticoids influence the metabolic properties of the cell and consequently the leukemic cells. We have previously outlined the differences that emerge from glucocorticoid treatment used in various concentrations, and lower concentrations manifested a mitogenic effect. A critical established glucocorticoid action is the apoptotic effect they exert on leukemic cells. However, little is known about the molecular response of malignant cells following glucocorticoid exposure. Even less is known about the cell proliferation dynamics governing leukemic cells under glucocorticoid influence. Growth and metabolic features are assumed to be of nonlinear nature. A model based prediction of glucocorticoid effects is derived by applying a non-linear fitting approximation to the measured parameters. Additionally, borrowing principles from the metabolic engineering and thermodynamics disciplines, we calculated the required energetics for cell proliferation under prednisolone treatment. Finally, we utilized a previously reported microarray dataset, to examine whether the predicted and measured parameters of the metabolism and proliferation under glucocorticoids are reflected in gene expression. Hence, making such an approach more pragmatic since those genes could shed light into the mechanisms of glucocorticoid-induced apoptotic resistance action, and subsequently identify novel targets for more efficient glucocorticoid treatments. We have eventually attempted to answer the basic question of what the thermodynamic mechanisms in the transition of the cell population from one state to the next are.</p