• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.366-370
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• 2005

Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.30-36
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• 2006

Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathological information related to TMA data analysis. This study aims to create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathological data elements. We designed a Tissue Microarray Object Model (TMA-OM). Both the Array Information and the Experimental Procedure models are created by referring to Microarray Gene Expression Object Model, Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE), and the TMA Data Exchange Specifications (TMA DES). The Clinical and Histopathological Information model is created by using CAP Cancer Protocols and National Cancer Institute Common Data Elements (NCI CDEs). MGED Ontology, UMLS and the terms extracted from CAP Cancer Protocols and NCI CDEs are used to create a controlled vocabulary for unambiguous annotation. We implemented a web-based application for TMA-OM, supporting data export in XML format conforming to the TMA DES or the DTD derived from TMA-OM. TMA-OM provides a comprehensive data model for storage, analysis and exchange of TMA data and facilitates model-level integration of other biological models.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.272-278
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• 2004

Recently, Pepe et al. (2003) employed the receiver operating characteristic (ROC) approach to rank candidate genes from a microarray experiment that can be used for the biomarker development with the ultimate purpose of the population screening of a cancer, In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. This design is referred to as a reference design or an indirect design. Ideally, this experiment produces n pairs of microarray data, where each pair consists of two sets of microarray data resulting from reference versus normal tissue and reference versus tumor tissue hybridizations. However, for certain individuals either normal tissue or tumor tissue is not large enough for the experimenter to extract enough RNA for conducting the microarray experiment, hence there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ 'normal only' and $n_3$ 'tumor only' data for the microarray experiment with n patients, where n=$n_1$+$n_2$+$n_3$. We refer to this data set as a mixed data set, as it contains a mix of fully observed and partially observed pair data. This mixed data set was actually observed in the microarray experiment based on human tissues, where human tissues were obtained during the surgical operations of cancer patients. Pepe et al. (2003) provide the rationale of using ROC approach based on two independent samples for ranking candidate gene instead of using t or Mann -Whitney statistics. We first modify ROC approach of ranking genes to a paired data set and further extend it to a mixed data set by taking a weighted average of two ROC values obtained by the paired data set and two independent data sets.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.12
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• pp.1837-1841
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• 2003

In an attempt to isolate novel molecules that may play a regulatory role in adipocyte differentiation, we devised an experimental strategy to identify adipose tissue-specific genes by modifying cDNA microarray technique. We used genefilter membranes containing approximately 15,000 rat non-redundant EST clones of which 4,000 EST were representative clones of known genes and 11,000 ESTs were uncharacterized clones. A series of hybridization of genefilter membranes with cDNA probes prepared from various rat tissues and nucleic acids sequence analysis allowed us to identify two adipose-tissue specific genes, adipocyte-specific secretory factor (ADSF) and H-rev107. Verification of tissue-specific expression patterns of these two genes by Northern blot analysis showed that ADSF mRNA is exclusive expressed in adipose tissue and the H-rev107 mRNA is predominantly expressed in adipose tissue. Further analysis of gene expression of ADSF and H-rev107 during 3T3-L1 adipocyte differentiation revealed that the ADSF and H-rev107 gene expression patterns are closely associated with the adipocyte differentiation program, indicating their possible role in the regulation of adipose tissue development. Overall, we demonstrated an application of modified cDNA microarray technique in molecular cloning, resulting in identification of two novel adipose tissue-specific genes. This technique will also be used as a useful tool in identifying novel genes expressed in a tissue-specific manner.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.169-182
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• 2007

Receive operating characteristic (ROC) approach can be employed to rank candidate genes from a microarray experiment, in particular, for the biomarker development with the purpose of population screening of a cancer. In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. Ideally, this experiment produces n pairs of microarray data. However, it is often the case that there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ "normal only" and $n_3$ "tumor only" data for the microarray. We refer to this data set as a mixed data set. We develop a ROC approach on the mixed data set to rank candidate genes for the biomarker development for the colorectal cancer screening. It turns out that the correlation between two ranks in terms of ROC and t statistics based on the top 50 genes of ROC rank is less than 0.6. This result indicates that employing a right approach of ranking candidate genes for the biomarker development is important for the allocation of resources.

• Proceedings of the Korean Society of Toxicology Conference
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• 2000.11a
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• pp.31-41
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• 2000

The major issue in the post genome sequencing era is determination of gene expression patterns in variety of biological systems. A microarray system is a powerful technology for analyzing the expression profile of thousands of genes at one experiment. In this study, we constructed cDNA microarray which carries 2,304 cDNAS derived from oligo-capped mouse cDNA library. Using this hand-made microarray we determined gene expression in various biological systems. To determine tissue specific genes, we compared Nine genes were highly-expressed in adult mouse brain compared to kidney, liver, and skeletal muscle. Tissue distribution analysis using DNA microarray extracted 9 genes that were predominantly expressed in the brain. A database search showed that five of the 9 genes, MBP, SC1, HiAT3, S100 protein-beta, and SNAP25, were previously known to be expressed at high level in the brain and in the nervous system. One gene was highly sequence similar to rat S-Rex-s/human NSP-C, suggesting that the gene is a mouse homologue. The remaining three genes did not match to known genes in the GenBank/EMBL database, indicating that these are novel genes highly-expressed in the brain. Our DNA microarray was also used to detect differentiation specific genes, hormone dependent genes, and transcription-factor-induced genes. We conclude that DNA microarray is an excellent tool for identifying differentially expressed genes.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.1
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• pp.11-18
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• 2008

We created a cDNA microarray representing approximately 3,500 pig genes for functional genomic studies. The array elements were selected from 6,494 cDNA clones identified in a large-scale expressed sequence tag (EST) project. These cDNA clones came from normalized and subtracted porcine adipose tissue cDNA libraries. Sequence similarity searches of the 3,426 ESTs represented on the array using BLASTN identified 2,790 (81.4%) as putative human orthologs, with the remainder consisting of "novel" genes or highly divergent orthologs. We used the gene microarray to profile transcripts expressed by adipose tissue of fatty Chinese Xiang pig (XP) and muscley Large White (LW). Microarray analysis of RNA extracted from adipose tissue of fatty XP and muscley LW identified 81 genes that were differently expressed two fold or more. Transcriptional differences of four of these genes, adipocyte fatty acid binding protein (aP2), stearyl-CoA desaturase (SCD), sterol regulatory element binding transcription factor 1 (SREBF1) and lipoprotein lipase (LPL) were confirmed using SYBR Green quantitative RT-PCR technology. Our results showed that high expression of SCD and SREBF1 may be one of the reasons that larger fat deposits are observed in the XP. In addition, our findings also illustrate the potential power of microarrays for understanding the molecular mechanisms of porcine development, disease resistance, nutrition, fertility and production traits.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.121-129
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• 2006

Two color or cDNA microarrays are extensively used to study relative expression levels of thousands of genes simultaneously. 0かy two tissue samples can be hybridized on a single microarray slide. Thus, a microarray slide necessarily forms an incomplete block design with block size two when more than two tissue samples are under study. We also need to control for variability in gene expression values due to the two dyes. Thus, red and green dyes form the second blocking factor in addition to slides. General design problem for these microarray experiments is discussed in this paper. Designs for factorial cDNA microarrays are also discussed.

• Journal of Pharmacopuncture
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• v.7 no.2
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• pp.19-28
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• 2004

봉독은 관절염 치료를 비롯한 여러 질환에 그 응용범위가 넓어지고 있으며 기전규명과 새로운 치료효과 개발을 위한 연구가 필요하다. 연골의 파괴는 진행된 각종 관절병증의 공통 병리기전이며 연골세포의 기능이상은 이 기전에 중요한 의미를 지닌다. 사람 연골세포의 특성을 유지하고 있는 HTB-94 연골육종세포를 배양하고 봉독을 처치했을 때의 유전자 발현양상을 microarray를 이용하여 관찰하였다. 대조군에 비해 4배 이상 발현의 차이가 있는 경우를 유의한 것으로 보았을 때 microarray의 344개 유전자중 봉독처치시 발현이 증강되는 유전자는 없었으며 발현이 억제되는 유전자는 interleukin 6 receptor, interleukin 1 alpha, tissue inhibitor of metalloproteinase 1, matrix metalloproteinase 1, tumor necrosis factor (ligand) superfamily, members 4, 8 and 12, and caspases 2, 6, and 10등 35개가 관찰되었다. Microarray를 통한 유전자발현 분석을 통해 관절염에 대한 봉독치료의 기전을 시사하는 유용한 자료를 얻을 수 있었으며 앞으로 보다 넓은 범위에 대한 연구가 필요할 것이다.

• Journal of Periodontal and Implant Science
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• v.50 no.5
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• pp.313-326
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• 2020

Purpose: This study was conducted to analyze specific RNA expression profiles in gingival tissue and saliva samples in periodontitis patients and healthy individuals, and to determine their correlations in light of the potential use of microarray-based analyses of saliva samples as a periodontal monitoring tool. Methods: Gingival tissue biopsies and saliva samples from 22 patients (12 with severe periodontitis and 10 with a healthy periodontium) were analyzed using transcriptomic microarray analysis. Differential gene expression was assessed, and pathway and clustering analyses were conducted for the samples. The correlations between the results for the gingival tissue and saliva samples were analyzed at both the gene and pathway levels. Results: There were 621 differentially expressed genes (DEGs; 320 upregulated and 301 downregulated) in the gingival tissue samples of the periodontitis group, and 154 DEGs (44 upregulated and 110 downregulated) in the saliva samples. Nine of these genes overlapped between the sample types. The periodontitis patients formed a distinct cluster group based on gene expression profiles for both the tissue and saliva samples. Database for Annotation, Visualization and Integrated Discovery analysis revealed 159 enriched pathways from the tissue samples of the periodontitis patients, as well as 110 enriched pathways In the saliva samples. Thirty-four pathways overlapped between the sample types. Conclusions: The present results indicate the possibility of using the salivary transcriptome to distinguish periodontitis patients from healthy individuals. Further work is required to enhance the extraction of available RNA from saliva samples.