• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.81-89
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• 2019

This study was analyzed about the relationship between culture microenvironment and cell differentiation of HPV 16 E6/E7-transfected immortalized oral keratinocyte(IHOK). By the alteration of culture environment, IHOK-EF and IHOK-EFKGM were obtained, and the modulation of cell properties was observed by cell proliferation assay, immunofluorescence, microarray, and quantitative real-time PCR analysis. IHOK-EF losed the properties of epithelial cells and obtained the properties of mesenchymal cells, and in the result of microarray analysis, genes related to the inhibition of differentiation such as IL6, TWIST1, and ID2 were highly expressed in IHOK-EF. When the culture environment was recovered to initial environment, these changes were recovered partially, presenting the return of genes involved in the inhibition of differentiation such as IL6, and ID2, particularly. This study will contribute to understand adjustment aspect for cell surviving according to the change of culture microenvironment in the study for determining the cell characteristic, and facilitate therapeutic approach for human disease by applying surviving study according to the change of cancer microenvironment.

• Molecules and Cells
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• v.20 no.1
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• pp.57-68
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• 2005

Murine erythroleukemia (MEL) cells are widely used to study erythroid differentiation thanks to their ability to terminally differentiate in vitro in response to chemical induction. At the molecular level, not much is known of their terminal differentiation apart from activation of adult-type globin gene expression. We examined changes in gene expression during the terminal differentiation of these cells using microarray-based technology. We identified 180 genes whose expression changed significantly during differentiation. The microarray data were analyzed by hierarchical and k-means clustering and confirmed by semi-quantitative RT-PCR. We identified several genes including H1f0, Bnip3, Mgl2, ST7L, and Cbll1 that could be useful markers for erythropoiesis. These genetic markers should be a valuable resource both as potential regulators in functional studies of erythroid differentiation, and as straightforward cell type markers.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.444-450
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• 2005

Epidermal growth factor (EGF) activates many intracellular effector molecules, which subsequently influence the expression levels of many genes involved in cell growth, apoptosis and signal transduction, etc. In this study, the early response of gene expressions due to EGF treatment was monitored using oligonucleotide DNA microarrays in rat schwannoma cell lines. An immunoblotting experiment showed the successful activation of EGF receptors and an effector protein, STAT5, due to EGF treatment. The microarray study showed that 35 genes were significantly induced and 2 were repressed within 60 min after the treatment. The list of induced genes included early growth response 1, suppressor of cytokine signaling 3, c-fos, interferon regulatory factor 1 and early growth response 2, etc. According to the microarray data, six of these were induced by more than 10-fold, and showed at least two different induction patterns, indicating complicated regulatory mechanisms in the EGF signal transduction.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.161-164
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• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• Interdisciplinary Bio Central
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• v.2 no.2
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• pp.4.1-4.6
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• 2010

The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student's t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups. The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.

• Environmental Analysis Health and Toxicology
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• v.20 no.2 s.49
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• pp.187-194
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• 2005

Octachlorostyrene (OCS) is a primarily concerning chemical in many countries because of its persistent and bioaccumulative properties in the environment. OCS is not commercially manufactured or used but it may be produced during incineration or chemical synthetic processes involving chlorinated compounds. There are several reports that OCS was found in the waters, sediments, fish, mussels, and also in human tissues. However, systematic studies on the OCS toxicities are scarce in literature. In this study, we tried to get the gene expression data using medaka DNA chip to identify biomarkers of OCS exposure. Medaka (Oryzias latipes.) was exposed to OCS 1 ppm for 2 days and 10 days, respectively. Total RNA was extracted and purified by guanidine thiocyanate method and the Cy3- and Cy5-labelled cDNAs produced by reverse trancription of the RNA were hybridized to medaka microarray. As results, eighty five genes were found to be down-or up regulated by OCS. Some of the genes were listed and confirmed by real-time PCR.

• The Korean Journal of Physiology and Pharmacology
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• v.10 no.4
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• pp.173-180
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• 2006

Microglial activation is thought to play a role in the pathogenesis of many brain disorders. Therefore, understanding the response of microglia to noxious stimuli may provide insights into their role in disorders such as stroke and neurodegeneration. Many genes involved in this response have been identified individually, but not systematically. In this regards, the microarray system permitted to screen a large number of genes in biological or pathological processes. Therefore, we used microarray technology to evaluate the effect of oxygen glucose deprivation (OGD) and reperfusion on gene expression in microglia under ischemia-like and activating conditions. Primary microglial cultures were prepared from postnatal mice brain. The cells were exposed to 4 hrs of OGD and 1 h of reperfusion at $37^{\circ}C$. Isolated mRNA were run on GeneChips. After OGD and reperfusion, ＞2-fold increases of 90 genes and ＞2-fold decrease of 41 genes were found. Among the genes differentially increased by OGD and reperfusion in microglia were inflammatory and immune related genes such as prostaglandin E synthase, $IL-1{\beta}$, and $TNF-{\alpha}$. Microarray analysis of gene expression may be useful for elucidating novel molecular mediators of microglial reaction to reperfusion injury and provide insights into the molecular basis of brain disorders.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.29-44
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• 2001

DNA damage by physical insult including UV and g-radiation might provoke genetic alterations in cells, which is followed by either acute cell death or tumorigenesis. The responsiveness to g-radiation depends on cellular context of target cells. To understand the mechanisms of checkpoint control, repair and cell death following genotoxic stimu]i, cDNA microarray can provide the gene expression profile. To make a profile of gene expression in irradiated Jurkat T cells, we hybridized the cDNA microarray using cDNA from g-irradiated Jurkat T cells. Jurkat T cells were exposed to 4Gy to 16Gy, and total RNA were extracted at 4 to 24 hrs after irradiation. The hybridization of the microarray to fluorescence-labeled cDNA from treated and untreated cells was analyzed by bioinformatic analysis to address relative changes in expression levels of the genes present in the array. Responses varied widely in different time points, suggesting acute stress response and chronic restoration or cell death. From these results we could select 384 genes related to radiation response in Tcells, and radiation response might be different in various types of cells. Using Radchip, we could separate "the exposed" from control PBMCs. We propose that Radchip might be useful to check the radiation research as well as radiation carcinogenesis.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.124-130
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• 2004

Microarray expression datasets are incessantly cumulated with the aid of recent technological advances. One of the first steps for analyzing these data under various experimental conditions is determining differentially expressed genes (DEGs) in each condition. Reasonable choices of thresholds for determining differentially expressed genes are used for the next -step-analysis with suitable statistical significances. We present a model for identifying DEGs using pathway information based on the global connectivity structure. Pathway information can be regarded as a collection of biological knowledge, thus we are tying to determine the optimal threshold so that the consequential connectivity structure can be the most compatible with the existing pathway information. The significant feature of our model is that it uses established knowledge as a reference to determine the direction of analyzing microarray dataset. In the most of previous work, only intrinsic information in the miroarray is used for the identifying DEGs. We hope that our proposed method could contribute to construct biologically meaningful network structure from microarray datasets.