• YAKHAK HOEJI
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• v.51 no.3
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• pp.179-185
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• 2007

Gastric cancer is one of the most common malignancies in Korea, but the predominant molecular event underlying gastric carcinogenesis remain unknown. Recently, DNA microarray technology has enabled the comprehensive analysis of gene expression level, and as such has yielded great insight into the molecular nature of cancer, However, despite the powerful approach of this techniques, the technical artifacts and/or bias in applied array platform limited the liability of resultant tens of thousand data points from microarray experiments. Therefore, we applied two different any platforms, such as olignucleotide microarray and cDNA microarray, to identify gastric cancer related large-scale molecular signature of the same human specimens. When thirty sets of matched human gastric cancer and normal tissues subjected to oligonucleotide microarray, total 623 genes were resulted as differently expressed genes in gastric cancer compared to normal tissues, and 252 genes for cDNA microarray analysis. In addition, forty three outlier genes which reflect the characteristic expression signature of gastric cancer beyond array platform and analytical protocol was recapitulated from two different expression profile. In conclusion, we were able to identify robust large-scale molecular changes in gastric cancer by applying two different platform of DNA microarray, this may facilitate to understand molecular carcinogenesis of gastric cancer.

• 한국생물공학회:학술대회논문집
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• 2001.11a
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• pp.745-746
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• 2001

Transcriptome analysis was performed for the production of recombinant protein in E. coli using DNA microarray containing 2,850 genes including all functionally known and putative ones. Changes in transcriptome were analyzed qualitatively and quantitatively to provide their physiological and metabolic meanings.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.29-34
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• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.587-598
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• 2006

The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.121-129
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• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.13-32
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• 2006

Biologists are attempting to group genes based on the temporal pattern of gene expression levels. So far, a number of methods have been proposed for clustering microarray data. However, the results of clustering depends on the genes selection, therefore the gene selection with significant expression difference is also very important to cluster for microarray data. Thus, this paper present the results of broad comparative studies to time course microarray data by considering methods of gene selection, clustering and cluster validation.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.20
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• pp.8957-8961
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• 2014

Background: Promoter hypermethylation leads to altered gene functions and may result in malignant cellular transformation. Thus, identification of biomarkers for hypermethylated genes could be useful for diagnosis, prognosis, and therapeutic treatment of oral squamous cell carcinoma (OSCC). Objectives: To screen hypermethylated genes with a microarray approach and to validate selected hypermethylated genes with the methylation-specific polymerase chain reaction (MSPCR). Materials and Methods: Genome-wide analysis of normal oral mucosa and OSCC tissues was conducted using the Illumina methylation microarray. The specified differential genes were selected and hypermethylation status was further verified with an independent cohort sample of OSCC samples. Candidate genes were screened using microarray assay and run by MSPCR analysis. Results: TP73, PIK3R5, and CELSR3 demonstrated high percentages of differential hypermethylation status. Conclusions: Our microarray screening and MSPCR approaches revealed that the signature candidates of differentially hypermethylated genes may possibly become potential biomarkers which would be useful for diagnostic, prognostic and therapeutic targets of OSCC in the near future.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.71-76
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• 2006

Comparative genomic hybridization (CGH) is a technique for studying chromosomal changes in cancer. As cancerous cells multiply, they can undergo dramatic chromosomal changes, including chromosome loss, duplication, and the translocation of DNA from one chromosome to another. Chromosome aberrations have previously been detected using optical imaging of whole chromosomes, a technique with limited sensitivity, resolution, quantification, and throughput. Efforts in recent years to use microarrays to overcome these limitations have been hampered by inadequate sensitivity, specificity and flexibility of the microarray systems. The oligonucleotide CGH microarray system overcomes several scientific hurdles that have impeded comparative genomic studies of cancer. This new system can reliably detect single copy deletions in chromosomes. The system includes a whole human genome microarray, reagents for sample preparation, an optimized microarray processing protocol, and software for data analysis and visualization. In this study, we determined the sensitivity, accuracy and reproducibility of the new system. Using this assay, we find that the performance of the complete system was maintained over a range of input genomic DNA from 5 ug down to 0.15 ug.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1731-1735
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• 2013

Objective: We aimed to identify key genes, pathways and function modules in the development of diffuse large B-cell lymphoma (DLBCL) with microarray data and interaction network analysis. Methods: Microarray data sets for 7 DLBCL samples and 7 normal controls was downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified with Student's t-test. KEGG functional enrichment analysis was performed to uncover their biological functions. Three global networks were established for immune system, signaling molecules and interactions and cancer genes. The DEGs were compared with the networks to observe their distributions and determine important key genes, pathways and modules. Results: A total of 945 DEGs were obtained, 272 up-regulated and 673 down-regulated. KEGG analysis revealed that two groups of pathways were significantly enriched: immune function and signaling molecules and interactions. Following interaction network analysis further confirmed the association of DEGs in immune system, signaling molecules and interactions and cancer genes. Conclusions: Our study could systemically characterize gene expression changes in DLBCL with microarray technology. A range of key genes, pathways and function modules were revealed. Utility in diagnosis and treatment may be expected with further focused research.