• Molecules and Cells
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• v.42 no.3
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• pp.237-244
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• 2019

Understanding the mechanisms of cancer drug resistance is a critical challenge in cancer therapy. For many cancer drugs, various resistance mechanisms have been identified such as target alteration, alternative signaling pathways, epithelial-mesenchymal transition, and epigenetic modulation. Resistance may arise via multiple mechanisms even for a single drug, making it necessary to investigate multiple independent models for comprehensive understanding and therapeutic application. In particular, we hypothesize that different resistance processes result in distinct gene expression changes. Here, we present a web-based database, CDRgator (Cancer Drug Resistance navigator) for comparative analysis of gene expression signatures of cancer drug resistance. Resistance signatures were extracted from two different types of datasets. First, resistance signatures were extracted from transcriptomic profiles of cancer cells or patient samples and their resistance-induced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two large-scale drug sensitivity datasets representing ~1,000 cancer cell lines. All the datasets are available for download, and are conveniently accessible based on drug class and cancer type, along with analytic features such as clustering analysis, multidimensional scaling, and pathway analysis. CDRgator allows meta-analysis of independent resistance models for more comprehensive understanding of drug-resistance mechanisms that is difficult to accomplish with individual datasets alone (database URL: http://cdrgator.ewha.ac.kr).

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.10
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• pp.1515-1521
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• 2016

Anethole and garlic have an immune modulatory effects on avian coccidiosis, and these effects are correlated with gene expression changes in intestinal epithelial lymphocytes (IELs). In this study, we integrated gene expression datasets from two independent experiments and investigated gene expression profile changes by anethole and garlic respectively, and identified gene expression signatures, which are common targets of these herbs as they might be used for the evaluation of the effect of plant herbs on immunity toward avian coccidiosis. We identified 4,382 and 371 genes, which were differentially expressed in IELs of chickens supplemented with garlic and anethole respectively. The gene ontology (GO) term of differentially expressed genes (DEGs) from garlic treatment resulted in the biological processes (BPs) related to proteolysis, e.g., "modification-dependent protein catabolic process", "proteolysis involved in cellular protein catabolic process", "cellular protein catabolic process", "protein catabolic process", and "ubiquitin-dependent protein catabolic process". In GO analysis, one BP term, "Proteolysis", was obtained. Among DEGs, 300 genes were differentially regulated in response to both garlic and anethole, and 234 and 59 genes were either up- or down-regulated in supplementation with both herbs. Pathway analysis resulted in enrichment of the pathways related to digestion such as "Starch and sucrose metabolism" and "Insulin signaling pathway". Taken together, the results obtained in the present study could contribute to the effective development of evaluation system of plant herbs based on molecular signatures related with their immunological functions in chicken IELs.

• Genomics & Informatics
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• v.9 no.4
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• pp.173-180
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• 2011

Recent trends in generating multiple, large-scale datasets provide new challenges to manipulating the relationship of different types of components, such as gene expression and drug response data. Integrative analysis of compound response and gene expression datasets generates an opportunity to capture the possible mechanism of compounds by using signature genes on diverse types of cancer cell lines. Here, we integrated datasets of compound response and gene expression profiles on NCI60 cell lines and constructed a network, revealing the relationship for 801 compounds and 341 gene probes. As examples, obtusol, which shows an exclusive sensitivity on a small number of colon cell lines, is related to a set of gene probes that have unique overexpression in colon cell lines. We also found that the SLC7A11 gene, a direct target of miR-26b, might be a key element in understanding the action of many diverse classes of anticancer compounds. We demonstrated that this network might be useful for studying the mechanisms of varied compound response on diverse cancer cell lines.

• Molecular & Cellular Toxicology
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• v.3 no.sup4
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• pp.35.4-35.4
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• 2007

• Genomics & Informatics
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• v.4 no.3
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• pp.97-102
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• 2006

In acute leukemia patients, several successful methods of expression profiling have been used for various purposes, i.e., to identify new disease class, to select a therapeutic target, or to predict chemo-sensitivity and clinical outcome. In the present study, we tested the peripheral blood of 47 acute leukemia patients in an attempt to identify differentially expressed genes in AML and ALL using a Korean-made 10K oligo-nucleotide microarray. Methods: Total RNA was prepared from peripheral blood and amplified for microarray experimentation. SAM (significant analysis of microarray) and PAM (prediction analysis of microarray) were used to select significant genes. The selected genes were tested for in a test group, independently of the training group. Results: We identified 345 differentially expressed genes that differentiated AML and ALL patients (FWER<0.05). Genes were selected using the training group (n=35) and tested for in the test group (n=12). Both training group and test group discriminated AML and ALL patients accurately. Genes that showed relatively high expression in AML patients were deoxynucleotidyl transferase, pre-B lymphocyte gene 3, B-cell linker, CD9 antigen, lymphoid enhancer-binding factor 1, CD79B antigen, and early B-cell factor. Genes highly expressed in ALL patients were annexin A 1, amyloid beta (A4) precursor protein, amyloid beta (A4) precursor-like protein 2, cathepsin C, lysozyme (renal amyloidosis), myeloperoxidase, and hematopoietic prostaglandin D2 synthase. Conclusion: This study provided genome wide molecular signatures of Korean acute leukemia patients, which clearly identify AML and ALL. Given with other reported signatures, these molecular signatures provide a means of achieving a molecular diagnosis in Korean acute leukemia patents.

• Genomics & Informatics
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• v.13 no.2
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• pp.40-44
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• 2015

DNA microarray and next-generation sequencing provide data that can be used for the genetic analysis of multiple quantitative traits such as gene expression levels, transcription factor binding profiles, and epigenetic signatures. In particular, chromatin opening is tightly coupled with gene transcription. To understand how these two processes are genetically regulated and associated with each other, we examined the changes of chromatin accessibility and gene expression in response to genetic variation by means of quantitative trait loci mapping. Regulatory patterns commonly observed in yeast and human across different technical platforms and experimental designs suggest a higher genetic complexity of transcription regulation in contrast to a more robust genetic architecture of chromatin regulation.

• Experimental and Molecular Medicine
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• v.50 no.11
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• pp.7.1-7.12
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• 2018

Recent findings from The Cancer Genome Atlas project have provided a comprehensive map of genomic alterations that occur in hepatocellular carcinoma (HCC), including unexpected mutations in apolipoprotein B (APOB). We aimed to determine the clinical significance of this non-oncogenetic mutation in HCC. An Apob gene signature was derived from genes that differed between control mice and mice treated with siRNA specific for Apob (1.5-fold difference; P < 0.005). Human gene expression data were collected from four independent HCC cohorts (n = 941). A prediction model was constructed using Bayesian compound covariate prediction, and the robustness of the APOB gene signature was validated in HCC cohorts. The correlation of the APOB signature with previously validated gene signatures was performed, and network analysis was conducted using ingenuity pathway analysis. APOB inactivation was associated with poor prognosis when the APOB gene signature was applied in all human HCC cohorts. Poor prognosis with APOB inactivation was consistently observed through cross-validation with previously reported gene signatures (NCIP A, HS, high-recurrence SNUR, and high RS subtypes). Knowledge-based gene network analysis using genes that differed between low-APOB and high-APOB groups in all four cohorts revealed that low-APOB activity was associated with upregulation of oncogenic and metastatic regulators, such as HGF, MTIF, ERBB2, FOXM1, and CD44, and inhibition of tumor suppressors, such as TP53 and PTEN. In conclusion, APOB inactivation is associated with poor outcome in patients with HCC, and APOB may play a role in regulating multiple genes involved in HCC development.

• Molecules and Cells
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• v.42 no.11
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• pp.804-809
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• 2019

Oncogenic gain-of-function mutations are clinical biomarkers for most targeted therapies, as well as represent direct targets for drug treatment. Although loss-of-function mutations involving the tumor suppressor gene, STK11 (LKB1) are important in lung cancer progression, STK11 is not the direct target for anticancer agents. We attempted to identify cancer transcriptome signatures associated with STK11 loss-of-function mutations. Several new sensitive and specific gene expression markers (ENO3, TTC39C, LGALS3, and MAML2) were identified using two orthogonal measures, i.e., fold change and odds ratio analyses of transcriptome data from cell lines and tissue samples. Among the markers identified, the ENO3 gene over-expression was found to be the direct consequence of STK11 loss-of-function. Furthermore, the knockdown of ENO3 expression exhibited selective anticancer effect in STK11 mutant cells compared with STK11 wild type (or recovered) cells. These findings suggest that ENO3-based targeted therapy might be promising for patients with lung cancer harboring STK11 mutations.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.6
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• pp.529-537
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• 2019

Lung cancer is the most common cause of cancer deaths worldwide and several molecular signatures have been developed to predict survival in lung cancer. Increasing evidence suggests that proliferation and migration to promote tumor growth are associated with dysregulated ion channel expression. In this study, by analyzing high-throughput gene expression data, we identify the differentially expressed $K^+$ channel genes in lung cancer. In total, we prioritize ten dysregulated $K^+$ channel genes (5 up-regulated and 5 down-regulated genes, which were designated as K-10) in lung tumor tissue compared with normal tissue. A risk scoring system combined with the K-10 signature accurately predicts clinical outcome in lung cancer, which is independent of standard clinical and pathological prognostic factors including patient age, lymph node involvement, tumor size, and tumor grade. We further indicate that the K-10 potentially predicts clinical outcome in breast and colon cancers. Molecular signature discovered through $K^+$ gene expression profiling may serve as a novel biomarker to assess the risk in lung cancer.

• Journal of Life Science
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• v.26 no.10
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• pp.1137-1152
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• 2016

Cells sense, respond, and adapt to a low oxygen environment called hypoxia, which is widely involved in a variety of human diseases. Adaptation to low oxygen concentrations includes gene expression changes by inducing hypoxic genes and reducing aerobic genes. Recently, the mitochondrial respiratory chain has been implicated in the control of these oxygen-regulated genes when cells experience hypoxia. In order to obtain an insight into an effect of the mitochondrial respiratory chain on cellular response to hyxpoxia, we here examined whole genome transcript signatures of respiration-proficient and respiration-deficient budding yeasts exposed to hypoxia using DNA microarrays. By comparing whole transcriptomes to hypoxia in respiration-proficient and respiration-deficient yeasts, we found that there are several classes of oxygen-regulated genes. Some of them require the mitochondrial respiratory chain for their expression under hypoxia while others do not. We found that the majority of hypoxic genes and aerobic genes need the mitochondrial respiratory chain for their expression under hypoxia. However, we also found that there are some hypoxic and aerobic genes whose expression under hypoxia is independent of the mitochondrial respiratory chain. These results indicate a key involvement of the mitochondrial respiratory chain in oxygen-regulated gene expression and multiple mechanisms for controlling oxygen-regulated gene expression. In addition, we provided gene ontology analyses and computational promoter analyses for hypoxic genes identified in the study. Together with differentially regulated genes under hypoxia, these post-analysis data will be useful resources for understanding the biology of response to hypoxia.