• Toxicological Research
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• v.22 no.4
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• pp.323-328
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• 2006

Global gene expression profile was analyzed by microarray analysis of rat liver RNA after acute acetaminophen (APAP) administration. A single dose of 1g/kg body weight of APAP was given orally, and the liver samples were obtained after 24, 48 h, and 2 weeks. Histopathologic and biochemical studies enabled the classification of the APAP effect into injury (24 and 48 h) and regeneration (2 weeks) stages. The expression levels of 4900 clones on a custom rat gene microarray were analyzed and 484 clones were differentially expressed with more than a 1.625-fold difference(which equals 0.7 in log2 scale) at one or more time points. Two hundred ninety seven clones were classified as injury-specific clones, while 149 clones as regeneration-specific ones. Characteristic gene expression profiles could be associated with APAP-induced gene expression changes in lipid metabolism, stress response, and protein metabolism. We established a global gene expression profile utilizing microarray analysis in rat liver upon acute APAP administration with a full chronological profile that not only covers injury stage but also later point of regeneration stage.

• KSBB Journal
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• v.16 no.4
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• pp.381-388
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• 2001

A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.525-536
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• 2004

Gene expression profile is numerical data of gene expression level from organism, measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify disease with gene expression profile. Because all genes are not related to disease, it is needed to select related genes that is called feature selection, and it is needed to classify selected genes properly. This paper Proposes GA based method for searching optimal ensemble of feature-classifier pairs that are composed with seven feature selection methods based on correlation, similarity, and information theory, and six representative classifiers. In experimental results with leave-one-out cross validation on two gene expression Profiles related to cancers, we can find ensembles that produce much superior to all individual feature-classifier fairs for Lymphoma dataset and Colon dataset.

• BMB Reports
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• v.39 no.4
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• pp.457-463
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• 2006

Insulin resistance is commonly observed in patients prior to the development of type 2 diabetes and may predict the onset of the disease. We tested the hypothesis that impairment in insulin stimulated glucose-disposal in insulin resistant patients would be reflected in the gene expression profile of skeletal muscle. We performed gene expression profiling on skeletal muscle of insulin resistant and insulin sensitive subjects using microarrays. Microarray analysis of 19,000 genes in skeletal muscle did not display a significant difference between insulin resistant and insulin sensitive muscle. This was confirmed with real-time PCR. Our results suggest that insulin resistance is not reflected by changes in the gene expression profile in skeletal muscle.

• 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.

• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.62-71
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• 2005

For toxicity model in the kidney, renal cell carcinoma (RCC) is one of the most important model to assess the structural and functional alterations. Most RCCs are sporadic, and environmental agents are suspected to play a role in the etiology of the disease. In this study, we discovered novel evidence for previously unknown gene expression patterns related to progression according to cancer stage in RCC. Four clear cell RCC tissue samples along with five corresponding patient-matched normal kidney tissue samples were obtained from patients undergoing partial or radical nephrectomy. To examine the difference of gene expression profile in clear cell RCC, radioactive cDNA microarrays were used to evaluate changes in the expression of 1,152 genes in a total. Using $^{33}P-labeled$ probes, this method provided highly sensitive gene expression profiles including drug metabolism, and cellular signaling. 29 genes were identified with expression levels that differed by more than 2.0 value of z-ratio, compared with that in control. Whereas expression of 38 genes were decreased by less than-2.0 value of z-ratio. In conclusion, this study has identified 67 gene expression alterations in clear-cell type of RCC. Most notably, genes involved in cell growth were up-regulated in stage I more than stage III whereas genes involved in signal transduction were down-regulated in which both stage I and stage III. The identified alteraions of gene expression will likely give in sight in to clear cell RCC and tumor progression.

• Molecular & Cellular Toxicology
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• v.2 no.4
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• pp.273-278
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• 2006

95 squamous cell lung carcinoma samples (normal tissue: 40 samples, tumor: 55 samples) were analyzed with 8 K cDNA microarray. 1-way ANOVA test was employed to select differentially expressed genes in tumor with FDR<0.01. Among the selected 1,655 genes, final 212 genes were chosen according to the expression fold change and used for following analysis. The expression of up-regulated 64 genes was verified with Reverse Transcription PCR and 10 genes were identified as candidates for SCC markers. In our opinion, those candidates can be exploited as diagnostic or therapeutic purposes. Gene Ontology (GO) based analysis was performed using those 212 genes, and following categories were revealed as significant biological processes: Immune response (GO: 0006955), antigen processing (GO: 0030333), inflammatory response (GO: 0006954), Cell adhesion (GO: 0007155), and Epidermis differentiation (GO: 0008544). Gene set enrichment analysis (GSEA) also carried out on overall gene expression profile with 522 functional gene sets. Glycolysis, cell cycle, K-ras and amino acid biosynthesis related gene sets were most distinguished. These results are consistent with the known characteristics of SCC and may be interconnected to rapid cell proliferation. However, the unexpected results from ERK activation in squamous cell carcinoma gripped our attention, and further studies are under progress.

• The Korean Journal of Food And Nutrition
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• v.25 no.4
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• pp.863-870
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• 2012

Though hydrogen peroxide ($H_2O_2$) causes a deleterious effect to cells with its reactive oxygen species resulting in cell death, S-allyl cysteine (SAC, a bioactive organosulfur compound of aged garlic extract) has been known to have a cytoprotective effect. Few reported profiles of gene expression of $H_2O_2$ and SAC treated human cord blood derived mesenchymal stem cells (MSC). This study revealed changes in the profile of twenty-one genes grouped by oxidative stress, antioxidant, cell death, anti-apoptosis and anti-aging by quantitative real time PCR. A concentration of $100{\mu}M$ of SAC or $50{\mu}M$ of $H_2O_2$ was applied to MSC which show moderate growth and apoptosis pattern. $H_2O_2$ treatment enhanced expression of eleven genes out of twenty-one genes compared with that of control group, on the contrary SAC suppressed expression of eighteen genes out of twenty-one genes except C ros oncogene. SAC decreased expression of oxidative stress genes such as SOD1, CAT and GPX. These results seemed consistent with reports which elucidated over-expression of NF-${\kappa}$B by $H_2O_2$, and suppression of it by SAC. This study will confer basic information for further experiments regarding the effects of SAC on gene levels.

• Molecular & Cellular Toxicology
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• v.5 no.1
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• pp.44-50
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• 2009

This study was conducted to evaluate the inflammation mechanisms of tumor necrosis factor-$\alpha$ (TNF-$\alpha$), interleukin-4 (IL-4), and IL-$1{\beta}$-induced stimulation of A549 human epithelial cells. In the present study, A549 cells were stimulated with TNF-$\alpha$, IL-4 and IL-$1{\beta}$ to induce expression of chemokines and adhesion molecules involved in eosinophil chemotaxis. The effects of TNF-$\alpha$, IL-4 and IL-$1{\beta}$ on gene expression profiles in A549 cells were evaluated by oligonucleotide microarray and Real time RT-PCR. The gene expression profiles for the A549 cells varied depending on the cytokines. Also, the results of the microarray and Real time RT-PCR revealed that inflammatory-related genes were up-regulated in cytokine stimulated A549 cells. Cytokines can affect inflammation in A549 cells. A microarray-based genomic survey is a high-throughput approach that enables evaluation of gene expression in cytokine stimulated cell lines.

• BMB Reports
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• v.37 no.5
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• pp.612-617
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• 2004

Autosomal dominant polycystic kidney disease (ADPKD) is a common genetic disorder characterized by the formation of fluid-filled cysts in the kidney and progressive renal failure. Other manifestations of ADPKD include the formation of cysts in other organs (liver, pancreas, and spleen), hypertension, cardiac defects, and cerebral aneurysms. The loss of function of the polycystin -1 and -2 results in the formation of epithelium-lined cysts, a process that depends on initial epithelial proliferation. cDNA microarrays powerfully monitor gene expression and have led to the discoveries of pathways regulating complex biological processes. We undertook to profile the gene expression patterns of epithelial cells derived from the cysts of ADPKD patients using the cDNA microarray technique. Candidate genes that were differently expressed in cyst tissues were identified. 19 genes were up-regulated, and 6 down-regulated. Semi-quantitative RT-PCR results were consistent with the microarray findings. To distinguish between normal and epithelial cells, we used the hierarchical method. The results obtained may provide a molecular basis for understanding the biological meaning of cytogenesis.