• Journal of Life Science
• /
• v.18 no.1
• /
• pp.75-83
• /
• 2008

This studies were designed to elucidate whether inhibitors of topoisomerase regulate function and activity of topoisomerase, and gene expression in HL-60 human leukemia cells. HL-60 cells were treated with 10-hydroxycamptothecin or doxorubicin, total RNA was isolated, and expressed genes were investigated with human oligonucleotide microarray containing 10K gene, respectively. Expression profiles of the human leukemia HL-60 cells treated with 10-hydroxycamptothecin (10-CIT) or doxorubicin associated with signal transduction,. cell adhesion, cell cycle, cell growth, cell proliferation, cell differentiation, transcription and immune response, especially genes related with transcription and cell growth. In HL-60 cells treated with 10-CPT, the expression of topoisomerase III${\alpha}$, III${\beta}$ and I gene from oligo chip microarray analysis were increased over, but the expression of topoisomerase II${\alpha}$ and II${\beta}$ gene were decreased over. In contrast, the expression of topoisomerase II${\alpha}$ and II${\beta}$ gene were increased over in HL-60 cells treated with doxorubicin, whereas the expression of topoisomerase III${\alpha}$ and III${\beta}$ mRNA remained no significant change. These results suggest that these data may be useful for novel therapeutic markers.

• The Journal of Korean Medicine
• /
• v.31 no.2
• /
• pp.91-108
• /
• 2010

Background & Objective: The aim of this study was to investigate the effect of P. radix on the inflammatory related gene expression in IL-$1{\beta}$-stimulated primary human gingival fibroblast using Whole Transcript Sense Target (WT-ST). Method: Human gingival fibroblast was incubated with P. radix [100 or $200\;{\mu}g/ml$], and IL-$1{\beta}$ [$1ng/m{\ell}$] added an hour later. After 24h, total RNA was extracted using RNeasy Mini Kit and the whole gene expression patterns were performed using WT-ST Labeling $Assay^{(R)}$. Result: In the DEG results, 782 genes were up-regulated in the IL-$1{\beta}$-treated group as compared to control and among those, 43 genes were associated with inflammation. 981 genes were down-regulated after treatment with IL-$1{\beta}$ and of those 7 genes were associated with inflammation. 1439 genes were up-regulated after treatment with P. radix plus IL-$1{\beta}$-treated when compared to IL-$1{\beta}$-treated alone group and 1225 genes were down-regulated in the same condition. Among the down-regulated genes, 5 were associated with inflammation- and inhibitor genes such as GDF15 and LIF. In the analysis of the P. radix plus IL-$1{\beta}$-treated group, the most significant pathways were the cytokine-cytokine receptor interaction, toll-like receptor signaling, JAK-STAT signaling and tyrosine metabolism. The gene expression patterns in the P. radix $200{\mu}g/m{\ell}$ plus IL-$1{\beta}$-treated group appear to be more involved in the metabolism-related pathways than in the $100{\mu}g/m{\ell}$ plus IL-$1{\beta}$-treated group. Conclusion & Discussion: By microarray analysis of gene expression data, we are able to identify gene expression patterns associated with not only anti-inflammation effect but also transcription function of P. radix.

• Proceedings of the Korean Statistical Society Conference
• /
• 2003.10a
• /
• pp.173-177
• /
• 2003

When we have both a paired data set and two independent data sets, neither a paired t-test nor a two-sample t-test can be used to detect differences between two samples. In order to identify differentially expressed genes in a mixed data set, a new test statistic is proposed.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.04a
• /
• pp.706-708
• /
• 2002

DNA 마이크로어레이 실험으로 나온 데이터들을 클러스터링하는 것은 유전자의 기능과 유전자의 네트워크를 파악해 나가는데 도움을 주게 된다. 계층적 클러스터링(hierarchical clustering) 방법은 그러한 실험 분석에서 가장 보편적으로 사용되는 방법이다. 본 논문에서는 계층적 클러스터링을 통해서 나온 결과 트리에 대해서, 트리의 리프 노드들을 재배열함으로써, 인접한 리프 노드들간의 거리의 종합이 최소가 되도록 하는 문제인 리프오더링 방법을 다루었고, 새로운 리프오더링 알고리즘을 제안하였다. 그리고, 이를 포함한 여러 리프오더링 방법들에 대한 실험 및 생물학적인 분석을 하였다.

• The Korean Journal of Applied Statistics
• /
• v.18 no.2
• /
• pp.371-379
• /
• 2005

In this paper, we propose a pattern consistency index for detecting heterogeneous time series that deviate from the representative pattern of each cluster in clustering time course gene expression data using the Pearson correlation coefficient. We examine its usefulness by applying this index to serum time course gene expression data from microarrays.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2000.11a
• /
• pp.45-52
• /
• 2000

Genomic approach produces massive amount of data within a short time period, New high-throughput automatic sequencers can generate over a million nucleotide sequence information overnight. A typical DNA chip experiment produces tens of thousands expression information, not to mention the tens of megabyte image files, These data must be handled automatically by computer and stored in electronic database, Thus there is a need for systematic approach of data collection, processing, and analysis. DNA sequence information is translated into amino acid sequence and is analyzed for key motif related to its biological and/or biochemical function. Functional genomics will play a significant role in identifying novel drug targets and diagnostic markers for serious diseases. As an enabling technology for functional genomics, bioinformatics is in great need worldwide, In Korea, a new functional genomics project has been recently launched and it focuses on identi☞ing genes associated with cancers prevalent in Korea, namely gastric and hepatic cancers, This involves gene discovery by high throughput sequencing of cancer cDNA libraries, gene expression profiling by DNA microarray and proteomics, and SNP profiling in Korea patient population, Our bioinformatics team will support all these activities by collecting, processing and analyzing these data.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2000.11a
• /
• pp.61-68
• /
• 2000

Now that the complete genomes of numerous organisms have been ascertained, key problems in molecular biology include determining the functions of the genes in each organism, the relationships that exist among these genes, and the regulatory mechanisms that control their operation. These problems can be partially addressed by using machine learning methods to induce predictive models from available data. My group is applying and developing machine learning methods for several tasks that involve characterizing gene regulation. In one project, for example, we are using machine learning methods to identify transcriptional control elements such as promoters, terminators and operons. In another project, we are using learning methods to identify and characterize sets of genes that are affected by tumor promoters in mammals. Our approach to these tasks involves learning multiple models for inter-related tasks, and applying learning algorithms to rich and diverse data sources including sequence data, microarray data, and text from the scientific literature.

• Radiation Oncology Journal
• /
• v.21 no.1
• /
• pp.54-65
• /
• 2003

Purpose : To analyze the gene expression Profiles of uterine ceulcal cancer, and its variation after radiation therapy, with or without concurrent chemotherapy, using a CDNA microarray. Materials and Methods :Sixteen patients, 8 with squamous ceil carcinomas of the uterine cervix, who were treated with radiation alone, and the other 8 treated w14h concurrent chemo-radiation, were Included in the study. Before the starling of the treatment, tumor biopsies were carried out, and the second time biopsies were peformed after a radiation dose of 16.2$\~$27 Gy. Three normal cervix tissues were used as a control group. The microarray experiments were peformed with 5 groups of the total RNAs extracted individually and then admixed as control, pre-radiation therapy alone, during-radiation therapy alone, pre-chemoradiation therapy, and during-chemoradlation therapy. The 33P-iabeled CDNAS were synthesized from the total RNAs of each group, by reverse transcription, and then they were hybridized to the CDNA microarray membrane. The gene expression of each microarrays was captured by the intensity of each spot produced by the radioactive isotopes. The pixels per spot were counted with an Arrayguage, and were exported to Microsoft Excel The data were normalized by the Z transformation, and the comparisons were peformed on the Z-ratio values calculated. Results : The expressions of 15 genes, including integrin linked kinase (ILK), CDC28 protein kinase 2, Spry 2, and ERK 3, were increased with the Z-ratio values of over 2.0 for the cervix cancer tissues compared to those for the normal controls. Those genes were involved In cell growth and proliferation, cell cycle control, or signal transduction. The expressions of the other 6 genes, Including G protein coupled receptor kinase 5, were decreased with the Z-ratio values of below -2.0. After the radiation thorapy, most of the genes, with a previously Increase expressions, represented the decreased expression profiles, and the genes, with the Z-ratio values of over 2.0, were cyclic nucleotlde gated channel and 3 Expressed sequence tags (EST). In the concurrent chemo-radiation group, the genes involved in cell growth and proliferation, cell cycle control, and signal transduction were shown to have increased expressions compared to the radiation therapy alone group. The expressions of genes involved in anglogenesis (angiopoietln-2), immune reactions (formyl peptide receptor-iike 1), and DNA repair (CAMP phosphodiesterase) were increased, however, the expression of gene involved In apoptosls (death associated protein kinase) was decreased. Conclusion : The different kinds of genes involved in the development and progression of cervical cancer were identified with the CDNA microarray, and the proposed theory is that the proliferation signal stalls with ILK, and is amplified with Spry 2 and MAPK signaling, and the cellular mitoses are Increased with the increased expression oi Cdc 2 and cell division kinases. After the radiation therapy, the expression profiles demonstrated 4he evidence of the decreased cancer cell proliferation. There was no sigificant difference in the morphological findings of cell death between the radiation therapy aione and the chemo-radiation groups In the second time biopsy specimen, however, the gene expression profiles were markedly different, and the mechanism at the molecular level needs further study.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.39 no.1
• /
• pp.54-63
• /
• 2010

Baicalin, a flavonoid, was shown to have diverse effects such as anti-inflammatory, anti-cancer, anti-viral, anti-bacterial and others. Recently, we found that the baicalin inhibits adipogenesis through the modulations of anti-adipogenic and pro-adipogenic factors of the adipogenesis pathway. In the present study, we further characterized the molecular mechanism of the anti-adipogenic effect of baicalin using microarray technology. Microarray analyses were conducted to analyze the gene expression profiles during the differentiation time course (0 day, 2 day, 4 day and 7 day) in 3T3-L1 cells with or without baicalin treatment. We identified a total of 3972 genes of which expressions were changed more than 2 fold. These 3972 genes were further analyzed using hierarchical clustering analysis, resulting in 20 clusters. Four clusters among 20 showed clearly up-regulated expression patterns (cluster 8 and cluster 10) or clearly down-regulated expression patterns (cluster 12 and cluster 14) by baicalin treatment for over-all differentiation period. The cluster 8 and cluster 10 included many genes which enhance cell proliferation or inhibit adipogenesis. On the other hand, the cluster 12 and cluster 14 included many genes which are related with proliferation inhibition, cell cycle arrest, cell growth suppression or adipogenesis induction. In conclusion, these data provide detailed information on the molecular mechanism of baicalin-induced inhibition of adipogenesis.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.1
• /
• pp.111-116
• /
• 2013

Background: Cigarette smoking is the major risk factor for development of lung cancer. Identification of effects of tobacco on airway gene expression may provide insight into the causes. This research aimed to compare gene expression of large airway epithelium cells in normal smokers (n=13) and non-smokers (n=9) in order to find genes which discriminate the two groups and assess cigarette smoking effects on large airway epithelium cells.Materials and Methods: Genes discriminating smokers from non-smokers were identified by applying a neural network clustering method, growing self-organizing maps (GSOM), to microarray data according to class discrimination scores. An index was computed based on differentiation between each mean of gene expression in the two groups. This clustering approach provided the possibility of comparing thousands of genes simultaneously. Results: The applied approach compared the mean of 7,129 genes in smokers and non-smokers simultaneously and classified the genes of large airway epithelium cells which had differently expressed in smokers comparing with non-smokers. Seven genes were identified which had the highest different expression in smokers compared with the non-smokers group: NQO1, H19, ALDH3A1, AKR1C1, ABHD2, GPX2 and ADH7. Most (NQO1, ALDH3A1, AKR1C1, H19 and GPX2) are known to be clinically notable in lung cancer studies. Furthermore, statistical discriminate analysis showed that these genes could classify samples in smokers and non-smokers correctly with 100% accuracy. With the performed GSOM map, other nodes with high average discriminate scores included genes with alterations strongly related to the lung cancer such as AKR1C3, CYP1B1, UCHL1 and AKR1B10. Conclusions: This clustering by comparing expression of thousands of genes at the same time revealed alteration in normal smokers. Most of the identified genes were strongly relevant to lung cancer in the existing literature. The genes may be utilized to identify smokers with increased risk for lung cancer. A large sample study is now recommended to determine relations between the genes ABHD2 and ADH7 and smoking.