• Genomics & Informatics
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• v.18 no.4
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• pp.39.1-39.8
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• 2020

Alzheimer's disease (AD) is a chronic, progressive brain disorder that slowly destroys affected individuals' memory and reasoning faculties, and consequently, their ability to perform the simplest tasks. This study investigated the hub genes of AD. Proteins interact with other proteins and non-protein molecules, and these interactions play an important role in understanding protein function. Computational methods are useful for understanding biological problems, in particular, network analyses of protein-protein interactions. Through a protein network analysis, we identified the following top 10 hub genes associated with AD: PTGER3, C3AR1, NPY, ADCY2, CXCL12, CCR5, MTNR1A, CNR2, GRM2, and CXCL8. Through gene enrichment, it was identified that most gene functions could be classified as integral to the plasma membrane, G-protein coupled receptor activity, and cell communication under gene ontology, as well as involvement in signal transduction pathways. Based on the convergent functional genomics ranking, the prioritized genes were NPY, CXCL12, CCR5, and CNR2.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.5
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• pp.1291-1300
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• 2004

Baicalin, a biologically active flavonoid form the roots of Scutallaria baicalensis (Skullcap), have been reported to not only function as anti-oxidants but also cause anticancer effect. We investigated the mechanism of baicalin-induced cytotoxicity and the macro scale gene expression analysis in leukemia cell line, HL-60 cells. Baicalin (10 μM) were used to treat the cells for 6h, 12h, 24h, 48h and 72h. In a human cDNAchip study of 65,000 genes evaluated 6, 12, 24, 48. 72 hours after treated with Baicalin in HL-60 cells. Hierarchical cluster against the genes which showed expression changes by more than two fold. One hundred one genes were grouped into 6 clusters according to their profile of expression by a hierarchical clustering algorithm. For genes differentially expressed in response to baicalin treatment, we tested functional classes based on Gene Ontology (GO) terms. This study provides the most comprehensive available survey of gene expression changes in response to baicalin treatment in HL-60 cell line.

• Molecular & Cellular Toxicology
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• v.5 no.3
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• pp.179-186
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• 2009

Hexachlorobenzene (HCB) is a bioaccumulative, persistent, and toxic pollutant. HCB is one of the 12 priority of Persistent Organic Pollutants (POPs) intended for global action by the United Nations Environment Program (UNEP) Governing Council. POPs are organic compounds that are resistant to environmental degradation through chemical, biological, and photolytic processes. Some of HCB is ubiquitous in air, water, soil, and biological matrices, as well as in major environmental compartments. HCB has effects on various organs such as thyroid, bone, skin, kidneys and blood cells and especially, revealed strong toxicity to liver. In this study, we identified genes related to hepatotoxiciy induced by HCB in human hepatocellular carcinoma (HepG2) cells using microarray and gene ontology (GO) analysis. Through microarray analysis, we identified 96 up- and 617 down-regulated genes changed by more than 1.5-fold by HCB. And after GO analysis, we determined several key pathways which known as related to hepatotoxicity such as metabolism of xenobiotics by cytochrome P450, complement and coagulation cascades, and tight junction. Thus, our present study suggests that genes expressed by HCB may provide a clue for hepatotoxic mechanism of HCB and gene expression profiling by toxicogenomic analysis also affords promising opportunities to reveal potential new mechanistic markers of toxicity.

• BMB Reports
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• v.45 no.2
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• pp.120-125
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• 2012

We have developed a biologist-friendly, Java GUI application (GoBean) for GO term enrichment analysis. It was designed to be a comprehensive and flexible GUI tool for GO term enrichment analysis, combining the merits of other programs and incorporating extensive graphic exploration of enrichment results. An intuitive user interface with multiple panels allows for extensive visual scrutiny of analysis results. The program includes many essential and useful features, such as enrichment analysis algorithms, multiple test correction methods, and versatile filtering of enriched GO terms for more focused analyses. A unique graphic interface reflecting the GO tree structure was devised to facilitate comparisons of multiple GO analysis results, which can provide valuable insights for biological interpretation. Additional features to enhance user convenience include built in ID conversion, evidence code-based gene-GO association filtering, set operations of gene lists and enriched GO terms, and user -provided data files. It is available at http://neon.gachon.ac.kr/GoBean/.

• Molecular & Cellular Toxicology
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• v.5 no.2
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• pp.99-107
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• 2009

Naphthalene is bicyclic aromatic compound that is widely used in various domestic and commercial applications including lavatory scent disks, soil fumigants and moth balls. Exposure to naphthalene results in the development of bronchiolar damage, cataracts and hemolytic anemia in humans and laboratory animals. However, little information is available regarding the mechanism of naphthalene toxicity. We investigated gene expression profiles and potential signature genes in human hepatocellular carcinoma HepG2 cells and human promyelocytic leukemia HL-60 cells after 3 h and 48 h incubation with the IC$_{20}$ and IC$_{50}$ of naphthalene by using 44 k agilent whole human genome oligomicroarray and operon human whole 35 k oligomicroarray, respectively. We identified 616 up-regulated genes and 2,088 down-regulated genes changed by more than 2-fold by naphthalene in HepG2 cells. And in HL-60, we identified 138 up-regulated genes and 182 down-regulated genes changed by more than 2-fold. This study identified several interesting targets and functions in relation to naphthalene-induced toxicity through a gene ontology analysis method. Apoptosis and cell cycle related genes are more commonly expressed than other functional genes in both cell lines. In summary, the use of in vitro models with global expression profiling emerges as a relevant approach toward the identification of biomarkers associated with toxicity after exposure to a variety of environmental toxicants.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.

• Journal of Animal Science and Technology
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• v.62 no.6
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• pp.765-776
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• 2020

The retinal degenerative disease, progressive retinal atrophy (PRA) is a major reason of vision impairment in canine population. Canine PRA signifies an inherently dissimilar category of retinal dystrophies which has solid resemblances to human retinis pigmentosa. Even though much is known about the biology of PRA, the knowledge about the intricate connection among genetic loci, genes and pathways associated to this disease in dogs are still remain unknown. Therefore, we have performed a genome wide association study (GWAS) to identify susceptibility single nucleotide polymorphisms (SNPs) of PRA. The GWAS was performed using a case-control based association analysis method on PRA dataset of 129 dogs and 135,553 markers. Further, the gene-set and pathway analysis were conducted in this study. A total of 1,114 markers associations with PRA trait at p < 0.01 were extracted and mapped to 640 unique genes, and then selected significant (p < 0.05) enriched 35 gene ontology (GO) terms and 5 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways contain these genes. In particular, apoptosis process, homophilic cell adhesion, calcium ion binding, and endoplasmic reticulum GO terms as well as pathways related to focal adhesion, cyclic guanosine monophosphate)-protein kinase G signaling, and axon guidance were more likely associated to the PRA disease in dogs. These data could provide new insight for further research on identification of potential genes and causative pathways for PRA in dogs.

• Mycobiology
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• v.50 no.1
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• pp.1-11
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• 2022

The ascomycete fungus Cordyceps militaris infects lepidopteran larvae and pupae and forms characteristic fruiting bodies. Owing to its immune-enhancing effects, the fungus has been used as a medicine. For industrial application, this fungus can be grown on geminated soybeans as an alternative protein source. In our study, we performed a comprehensive transcriptomic analysis to identify core gene sets during C. militaris cultivation on germinated soybeans. RNA-Seq technology was applied to the fungal cultures at seven-time points (2, 4, and 7-day and 2, 3, 5, 7-week old cultures) to investigate the global transcriptomic change. We conducted a time-series analysis using a two-step regression strategy and chose 1460 significant genes and assigned them into five clusters. Characterization of each cluster based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases revealed that transcription profiles changed after two weeks of incubation. Gene mapping of cordycepin biosynthesis and isoflavone modification pathways also confirmed that gene expression in the early stage of GSC cultivation is important for these metabolic pathways. Our transcriptomic analysis and selected genes provided a comprehensive molecular basis for the cultivation of C. militaris on germinated soybeans.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.197-207
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• 2010

Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.697-709
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• 2022

Objective : The present study aimed to identify the function of ischemic stroke (IS) patients' peripheral blood and its role in IS, explore the pathogenesis, and provide direction for clinical research progress by comprehensive bioinformatics analysis. Methods : Two datasets, including GSE58294 and GSE22255, were downloaded from Gene Expression Omnibus database. GEO2R was utilized to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using the database annotation, visualization and integrated discovery database. The protein-protein interaction (PPI) network of DEGs was constructed by search tool of searching interactive gene and visualized by Cytoscape software, and then the Hub gene was identified by degree analysis. The microRNA (miRNA) and miRNA target genes closely related to the onset of stroke were obtained through the miRNA gene regulatory network. Results : In total, 36 DEGs, containing 27 up-regulated and nine down-regulated DEGs, were identified. GO functional analysis showed that these DEGs were involved in regulation of apoptotic process, cytoplasm, protein binding and other biological processes. KEGG enrichment analysis showed that these DEGs mediated signaling pathways, including human T-cell lymphotropic virus (HTLV)-I infection and microRNAs in cancer. The results of PPI network and cytohubba showed that there was a relationship between DEGs, and five hub genes related to stroke were obtained : SOCS3, KRAS, PTGS2, EGR1, and DUSP1. Combined with the visualization of DEG-miRNAs, hsa-mir-16-5p, hsa-mir-181a-5p and hsa-mir-124-3p were predicted to be the key miRNAs in stroke, and three miRNAs were related to hub gene. Conclusion : Thirty-six DEGs, five Hub genes, and three miRNA were obtained from bioinformatics analysis of IS microarray data, which might provide potential targets for diagnosis and treatment of IS.