• Restorative Dentistry and Endodontics
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• v.37 no.3
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• pp.142-148
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• 2012

Objectives: We analyzed gene-expression profiles after 14 day odontogenic induction of human dental pulp cells (DPCs) using a DNA microarray and sought candidate genes possibly associated with mineralization. Materials and Methods: Induced human dental pulp cells were obtained by culturing DPCs in odontogenic induction medium (OM) for 14 day. Cells exposed to normal culture medium were used as controls. Total RNA was extracted from cells and analyzed by microarray analysis and the key results were confirmed selectively by reverse-transcriptase polymerase chain reaction (RT-PCR). We also performed a gene set enrichment analysis (GSEA) of the microarray data. Results: Six hundred and five genes among the 47,320 probes on the BeadChip differed by a factor of more than two-fold in the induced cells. Of these, 217 genes were upregulated, and 388 were down-regulated. GSEA revealed that in the induced cells, genes implicated in Apoptosis and Signaling by wingless MMTV integration (Wnt) were significantly upregulated. Conclusions: Genes implicated in Apoptosis and Signaling by Wnt are highly connected to the differentiation of dental pulp cells into odontoblast.

• Genomics & Informatics
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• v.2 no.2
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• pp.92-98
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• 2004

A cDNA microarray experiment is one of the most useful high-throughput experiments in medical informatics for monitoring gene expression levels. Statistical analysis with a cDNA microarray medical data requires a normalization procedure to reduce the systematic errors that are impossible to control by the experimental conditions. Despite the variety of normalization methods, this. paper suggests a more general and synthetic normalization algorithm with a control gene set based on previous studies of normalization. Iterative normalization method was used to select and include a new control gene set among the whole genes iteratively at every step of the normalization calculation initiated with the housekeeping genes. The objective of this iterative normalization was to maintain the pattern of the original data and to keep the gene expression levels stable. Spatial plots, M&A (ratio and average values of the intensity) plots and box plots showed a convergence to zero of the mean across all genes graphically after applying our iterative normalization. The practicability of the algorithm was demonstrated by applying our method to the data for the human photo aging study.

• Genomics & Informatics
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• v.6 no.3
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• pp.136-141
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• 2008

A large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient’s disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.

• Genomics & Informatics
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• v.5 no.3
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• pp.124-128
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• 2007

Microarray technology enables us to measure the expression of tens of thousands of genes simultaneously under various experimental conditions. Clustering analysis is one of the most successful methods for analyzing microarray data using the assumption that co-expressed genes may be co-regulated. It is important to extract meaningful clusters from a long unordered list of clusters and to evaluate the functional homogeneity and heterogeneity of clusters. Many quality measures for clustering results have been suggested in different conditions. In the present study, we consider biological pathways as a collection of biological knowledge and used them as a reference for measuring the quality of clustering results and functional homogeneities. PathTalk visualizes and evaluates functional relationships between gene clusters and biological pathways.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.147-154
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• 2003

Microarray information system is a complex system to manage, analyze and interpretate microarray gene expression data. Establishment of well-defined development process is very essential for understanding the complexity and organization of the system. We performed object-oriented analysis using Unified Modeling Language (UML) in specifying, visualizing and documenting microarray information system. The object-oriented analysis consists of three major steps: (i) use case modeling to describe various functionalities from the user's perspective (ii) dynamic modeling to illustrate behavioral aspects of the system (iii) object modeling to represent structural aspects of the system. As a result of our modeling activities we provide the UML diagrams showing various views of the microarray information system. We believe that the object-oriented analysis ensures effective documentations and communication of information system requirements. Another useful feature of object-oriented technique is structural continuity to standard microarray data model MAGE-OM (Microarray Gene Expression Object Model). The proposed modeling e(forts can be applicable for integration of biomedical information system.

• Toxicological Research
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• v.27 no.1
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• pp.43-48
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• 2011

Even though exposure to benzene has been linked to a variety of cancers including leukemia, the detailed molecular mechanisms relevant to benzene-induced carcinogenesis remain to be clearly elucidated. In this study, we evaluated the effects of benzene on differential gene expression in a leukemia cell line. The K562 leukemia cell line used in this study was cultured for 3 h with 10 mM benzene and RNA was extracted. To analyze the gene expression profiles, a 41,000 human whole genome chip was employed for cDNA microarray analysis. We initially identified 6,562 genes whose expression was altered by benzene treatment. Among these, 3,395 genes were upregulated and 3,167 genes were downregulated by more than 2-fold, respectively. The results of functional classification showed that the identified genes were involved in biological pathways including transcription, cell proliferation, the cell cycle, and apoptosis. These gene expression profiles should provide us with further insights into the molecular mechanisms underlying benzene-induced carcinogenesis, including leukemia.

• Genomics & Informatics
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• v.3 no.2
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• pp.66-72
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• 2005

The epidermis is a physiological barrier to protect organisms against environment. During the aging process, skin tissues undergo various changes including morphological and functional changes. The transcriptional regulation of genes is part of cellular reaction of aging process. In order to examine the changes of gene expression during the aging process, we used the primary cell culture system of human keratinocytes. Since UV radiation is the most important environmental skin aggressor, causing skin cancer and other problems including premature skin aging, we examined the changes of gene expression in human keratinocytes after UV irradiation using oligonucleotide microarray containing over 10,000 genes. We also compared the gene expression patterns of the senescent and UV treated cells. Expression of the variety of genes related to transcription factors, cell cycle regulation, immune response was altered in human keratinocytes. Some of down-regulated genes are represented in both senescent and UV treated cells. The results may provide a new view of gene expression following UVB exposure and aging process in human keratinocytes.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.617-626
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• 2009

A series of recent papers reported that the inter-gene correlations in Affymetrix microarray data sets were strong and long-ranged, and the assumption of independence or weak dependence among gene expression signals which was often employed without justification was in conflict with actual data. Qui et al. (2005) indicated that applying the nonparametric empirical Bayes method in which test statistics were pooled across genes for performing the statistical inference resulted in the large variance of the number of differentially expressed genes. Qui et al. (2005) attributed this effect to strong and long-ranged inter-gene correlations. Klebanov and Yakovlev (2007) demonstrated that the inter-gene correlations provided a rich source of information rather than being a nuisance in the statistical analysis and they developed, by transforming the original gene expression sequence, a sequence of independent random variables which they referred to as a ${\delta}$-sequence. We note in this report using two cDNA microarray data sets experimented in this country that the strong and long-ranged inter-gene correlations were still valid in cDNA microarray data and also the ${\delta}$-sequence of independence could be derived from the cDNA microarray data. This note suggests that the inter-gene correlations be considered in the future analysis of the cDNA microarray data sets.

• Genomics & Informatics
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• v.10 no.1
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• pp.23-32
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• 2012

There are lots of studies attempting to identify the expression changes in oral squamous cell carcinoma. Most studies include insufficient samples to apply statistical methods for detecting significant gene sets. This study combined two small microarray datasets from a public database and identified significant genes associated with the progress of oral squamous cell carcinoma. There were different expression scales between the two datasets, even though these datasets were generated under the same platforms - Affymetrix U133A gene chips. We discretized gene expressions of the two datasets by adjusting the differences between the datasets for detecting the more reliable information. From the combination of the two datasets, we detected 51 significant genes that were upregulated in oral squamous cell carcinoma. Most of them were published in previous studies as cancer-related genes. From these selected genes, significant genetic pathways associated with expression changes were identified. By combining several datasets from the public database, sufficient samples can be obtained for detecting reliable information. Most of the selected genes were known as cancer-related genes, including oral squamous cell carcinoma. Several unknown genes can be biologically evaluated in further studies.

• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.52-61
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• 2005

Transcript profiling is a particularly valuable tool in the field of steroid receptor biology, as these receptors are ligand-activated transcription factors and therefore exert their initial effects through altering gene expression in responsive cells. Also, an awareness of endocrine disrupting chemicals (EDCs) and their potential screening methods to identify endocrine activity have been increased. Here we developed an in-house cDNA microarray, named KISTCHIP-400 ver. 1.0, with 416 clones, based on public database and research papers. These clones contained estrogen, androgen, thyroid hormone & receptors, sex hormone signal transduction & regulation, c-fos, c-myc, ps2 gene, metabolism related genes etc. Also, to validate the KISTCHIP-400 ver. 1.0, we investigated gene expression profiles with reference hormones, $10^{8}\;M\;17{\beta}-estradiol,\;10^{-7}\;M\;testosterone\;and\;10^{-7}\;M$ progesterone in MCF-7 cell line. As the results, gene expression profiles of three reference hormones were distinguished from each other with significant and identified 33 $17{\beta}-estradiol$ responsive genes. This study is in first step of validation for KISTCHIP-400 ver. 1.0, as following step transcriptional profile analysis on not only low concentrations of EDCs but suspected EDCs using KISTCHIP-400 ver. 1.0 is processing. Our results indicate that the developed microarray may be a useful laboratory tool for screening EDCs and elucidating endocrine disrupting mechanism.