• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Molecular & Cellular Toxicology
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• v.3 no.4
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• pp.246-253
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• 2007

Doxorubicin and daunorubicin are excellent chemotherapeutic agents utilized for several types of cancer but the irreversible cardiac damage is the major limitation for its use. The biochemical mechanisms of doxorubicin- and daunorubicin- induced cardiotoxicity remain unclear. There are many reports on toxicity of doxorubicin and doxorubicin in cardiomyocytes, but effects in cardiovascular system by these drugs are almost not reported. In this study, we investigated gene expression profiles in human umbilical vein endothelial cells (HUVECs) to better understand the causes of doxorubicin and doxorubicininduced cardiovascular toxicity and to identify differentially expressed genes (DEGs). Through the clustering analysis of gene expression profiles, we identified 124 up-regulated common genes and 298 down-regulated common genes changed by more than 1.5-fold by all two cardiac toxicants. HUVECs responded to doxorubicin and doxorubicin damage by increasing levels of apoptosis, oxidative stress, EGF and lipid metabolism related genes. By clustering analysis, we identified some genes as potential markers on apoptosis effects of doxorubicin and doxorubicin. Six genes of these, BBC3, APLP1, FAS, TP53INP, BIRC5 and DAPK were the most significantly affected by doxorubicin and doxorubicin. Thus, this study suggests that these differentially expressed genes may play an important role in the cardiovascular toxic effects and have significant potential as novel biomarkers to doxorubicin and doxorubicin exposure.

• Molecular & Cellular Toxicology
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• v.3 no.4
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• pp.231-237
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• 2007

Exposure to DNA-damaging agents can elicit a variety of stress-related responses that may alter the expression of genes associated with numerous biological pathways. We used 19 k whole human genome chip to detect gene expression profiles and potential signature genes in human normal hepatocytes (THLE-3) by treatment of five direct acting mutagens, furylfuramide (AF-2), N-nitroso-N-methylurea (MNU), methylmethanesulfonate (MMS), 4-nitroquinoline-N-oxide (4-NQO) and 2-nitrofluorene (2NF) of the $IC_{20}$ concentration for 3 h. Fifty one up-regulated common genes and 45 down-regulated common genes above 1.5-fold by five direct-acting mutagens were identified by clustering analysis. Many of these changed genes have some association with apoptosis, control of cell cycle, regulation of transcription and signal transduction. Genes related to these functions, as TP73L, E2F5, MST016, SOX5, MAFB, LIF, SII3, TFIIS, EMR1, CYTL1, CX3CR1 and RHOH are up-regulated. Down-regulated genes are ALOX15B, xs155, IFITM1, BATF, VAV2, CD79A, DCDC2, TNFSF8 and KOX8. We suggest that gene expression profiling on mutagens by toxicogenomic analysis affords promising opportunities to reveal potential new mechanistic markers of genotoxicity.

• The Journal of Korean Obstetrics and Gynecology
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• v.24 no.3
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• pp.28-47
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• 2011

Objectives: This study was performed to investigate the effects of Angelicae gigantis Radix (AGR) which is one of the most useful herbal-drug to treat patients with Polycystic Ovary Syndrome (PCOS) in Oriental medicine on gene expression of ovary tissue. Methods: The effects of AGR on gene expression of ovary tissue resected from PCOS induced rats using single injection of $\ss$-Estradiol 17-valerate (EV) was measured using microarray technique, and the functional analysis on these genes was conducted. Results: Total 2,812 genes were up-regulated or down-regulated, 1,421 genes were up-regulated, 1,391 genes were down-regulated by induction of PCOS. Up-regulated genes were mainly involved in biological function such as cell signalling pathways and inflammatory response. Expression levels of 1,442 genes were restored to those of naive animals by administration of AGR. 558 genes were restored to those of naive animals, which were lowered by induction of PCOS. 884 genes were lowered to naive levels, which were elevated by induction of PCOS. The functions of restored genes were partially involved in the restoration of expression levels, which were changed by induction of PCOS. Especially, up-regulated gene by induction of PCOS were mainly involved in these changes. These results mean restorative effects of AGR on damaged functions by induction of PCOS. The network of total protein interactions was measured using cytoscape program, and some key molecules, such as IRS2, MCM10, ORC2L related in up-regulated genes, CTBP2, CD44, RHOA, related in down-regulated genes that can be used for elucidation of therapeutical mechanism of medicine in future were identified. Conclusion: Restored genes by AGR were thought to have common pathways related in regulation of gene expressions. Especially, genes in restored expression levels by AGR, which were up-regulated by induction of PCOS, were regulated by 9 of common transcription factors, genes in restored expression levels by AGR, which were down-regulated by induction of PCOS, were involved in 25 of common transcription factors.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1691-1699
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• 2016

Ovarian cancer is possibly the sixth most common malignancy worldwide, in Mexico representing the fourth leading cause of gynecological cancer death more than 70% being diagnosed at an advanced stage and the survival being very poor. Ovarian tumors are classified according to histological characteristics, epithelial ovarian cancer as the most common (~80%). We here used high-density microarrays and a systems biology approach to identify tissue-associated deregulated genes. Non-malignant ovarian tumors showed a gene expression profile associated with immune mediated inflammatory responses (28 genes), whereas malignant tumors had a gene expression profile related to cell cycle regulation (1,329 genes) and ovarian cell lines to cell cycling and metabolism (1,664 genes).

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.3
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• pp.743-752
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• 2012

Hepatocellular carcinoma is a common disorder worldwide which ranks 5th and 7th most common cancer among men and women. In recent years, different incidence trends have been observed in various regions, but the reasons are not completely understood. However, due to the great public efforts in HCC prevention and alternation of lifestyle, the roles of some well documented risk factors played in hepatocarcinogenesis might have changed. This paper summarizes both the environmental and host related risk factors of hepatocellular carcinoma including well established risk factors such as hepatitis virus infection, aflatoxin and alcohol, as well as possible risk factors such as coffee drinking and other dietary agents.

• Genomics & Informatics
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• v.20 no.2
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• pp.20.1-20.13
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• 2022

Recent studies have focused on the early detection of ovarian cancer (OC) using tumor materials by liquid biopsy. The mechanisms of microRNAs (miRNAs) to impact OC and signaling pathways are still unknown. This study aims to reliably perform functional analysis of previously validated circulating miRNAs' target genes by using pathfindR. Also, overall survival and pathological stage analyses were evaluated with miRNAs' target genes which are common in the The Cancer Genome Atlas and GTEx datasets. Our previous studies have validated three downregulated miRNAs (hsa-miR-885-5p, hsa-miR-1909-5p, and hsa-let7d-3p) having a diagnostic value in OC patients' sera, with high-throughput techniques. The predicted target genes of these miRNAs were retrieved from the miRDB database (v6.0). Active-subnetwork-oriented Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted by pathfindR using the target genes. Enrichment of KEGG pathways assessed by the analysis of pathfindR indicated that 24 pathways were related to the target genes. Ubiquitin-mediated proteolysis, spliceosome and Notch signaling pathway were the top three pathways with the lowest p-values (p < 0.001). Ninety-three common genes were found to be differentially expressed (p < 0.05) in the datasets. No significant genes were found to be significant in the analysis of overall survival analyses, but 24 genes were found to be significant with pathological stages analysis (p < 0.05). The findings of our study provide in-silico evidence that validated circulating miRNAs' target genes and enriched pathways are related to OC and have potential roles in theranostics applications. Further experimental investigations are required to validate our results which will ultimately provide a new perspective for translational applications in OC management.

• Genomics & Informatics
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• v.19 no.1
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• pp.10.1-10.7
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• 2021

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC₅₀ and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC₅₀ and 0.81 for AUC. We identify common predictor genes for IC₅₀ and AUC, with which the performance was similar to those with genes separately found for IC₅₀ and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC₅₀, 0.7945 for AUC) with 321 predictor genes.

• 대한방사선방어학회:학술대회논문집
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• 2010.04a
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• pp.48-51
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• 2010

• Korean Journal of Poultry Science
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• v.47 no.1
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• pp.9-19
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• 2020

Avian influenza (AI) viruses are highly contagious viruses that infect many bird species and are zoonotic. Ducks are resistant to the deadly and highly pathogenic avian influenza virus (HPAIV) and remain asymptomatic to the low pathogenic avian influenza virus (LPAIV). In this study, we identified common differentially expressed genes (DEGs) after a reanalysis of previous transcriptomic data for the HPAIV and LPAIV infected duck lung cells. Microarray datasets from a previous study were reanalyzed to identify common target genes from DEGs and their biological functions. A total of 731 and 439 DEGs were identified in HPAIV- and LPAIV-infected duck lung cells, respectively. Of these, 227 genes were common to cells infected with both viruses, in which 193 genes were upregulated and 34 genes were downregulated. Functional annotation of common DEGs revealed that translation related gene ontology (GO) terms were enriched, including ribosome, protein metabolism, and gene expression. REACTOME analyses also identified pathways for protein and RNA metabolism as well as for tissue repair, including collagen biosynthesis and modification, suggesting that AIVs may evade the host defense system by suppressing host translation machinery or may be suppressed before being exported to the cytosol for translation. AIV infection also increased collagen synthesis, showing that tissue lesions by virus infection may be mediated by this pathway. Further studies should focus on these genes to clarify their roles in AIV pathogenesis and their possible use in AIV therapeutics.