• Journal of Microbiology and Biotechnology
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• v.32 no.6
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• pp.718-729
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• 2022

Esophageal squamous cell carcinoma (ESCC) is the most common primary esophageal malignancy with poor prognosis. Here, due to the necessity for exploring potential therapies against ESCC, we obtained the gene expression data on ESCC from the TCGA and GEO databases. Venn diagram analysis was applied to identify common targets. The protein-protein interaction network was constructed by Cytoscape software, and the hub targets were extracted from the network via cytoHubba. The potential hub nodes as drug targets were found by pharmacophore-based virtual screening and molecular modeling, and the antitumor activity was evaluated through in vitro studies. A total of 364 differentially expressed genes (DEGs) in ESCC were identified. Pathway enrichment analyses suggested that most DEGs were mainly involved in the cell cycle. Three hub targets were retrieved, including CENPF, CCNA2 (cyclin A), and CCNB1 (cyclin B1), which were highly expressed in esophageal cancer and associated with prognosis. Moreover, amentoflavone, a promising drug candidate found by pharmacophore-based virtual screening, showed antiproliferative and proapoptotic effects and induced G1 in esophageal squamous carcinoma cells. Taken together, our findings suggested that amentoflavone could be a potential cell cycle inhibitor targeting cyclin B1, and is therefore expected to serve as a great therapeutic agent for treating esophageal squamous cell carcinoma.

• Journal of Plant Biotechnology
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• v.47 no.4
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• pp.273-282
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• 2020

In Vietnam, Celastrus hindsii Benth, a medicinal plant rich in secondary metabolites, has been used to alleviate distress caused by ulcers, tumors, and inflammation for generations. The occurrence of two phenotypes, Broad Leaf (BL) and Narrow Leaf (NL), has raised questions about the selection of appropriate varieties for conservation and crop improvement to enhance medicinal properties. This study examined molecular differences in C. hindsii by comparing protein profiles between the NL and BL types using 2D-PAGE and MS. Peptide sequences and proteins were identified by matching MS data against the MSPnr100 databases and verified using the MultiIdent tool on ExPASy and the Blast2GO software. Our results revealed notable variations in protein abundance between the NL and BL proteomes. Selected proteins were confidently identified from 12 protein spots, thereby highlighting the molecular variation between NL and BL proteomes. Upregulated proteins in BL were found to be associated with flavonoid and amino acid biosynthesis as well as nuclease metabolism, which probably attributed to the intraspecific variations. Several bioactive proteins identified in this study can have applications in cancer therapeutics. Therefore, the BL phenotype characterized by healthier external morphological features has higher levels of bioactive compounds and could be better suited for medicinal use.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.46-55
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• 2006

(주)지노믹트리는 DNA 마이크로어레이 기술을 기반으로 하는 분자진단회사로서, 다음의 세가지 사업에 전력하고 있다. 첫째는 독창적이며 특화된 바이오마커 발굴기술 (MAGIC system)을 바탕으로 각종 암진단을 위한 바이오마커 개발연구 두 번째는 당사의 원천 기술인 다중동시검출 시스템을 이용한 질병 진단 시스템 및 증폭시스템 세 번째는 마이크로어레이 기술을 이용한 유전자 발현 분석, Array CGH, DNA 메틸레이션 분석 그리고 miRNA 검출 등의 지노믹스시대의 연구를 위한 토탈솔루션을 제공하고 있다. 지난 5년간의 마이크로어레이 기반기술을 이용한 자체연구 활동을 수행하면서 축적된 마이크로어레이 관련기술 노-하우들을 국내 마이크로어레이 연구자들에게 공급하기 위하여 노력하고 있다. 특히 당사의 지노믹서비스 부문은 유전자 발현 분석 솔루션 제공을 위해서 자체적으로 제작하여 공급하고 있는 human cDNA(17K/25K) 및 rat cDNA (5.0K) 마이크로어레이, Human (22K) 및 mouse (10K) 올리고뉴클레오타이드 마이크로 어레이 그리고 미생물 연구를 위한 대장균 (6K) 및 폐렴균 (2.2K) 올리고뉴클레오타이드 마이크로어레이 제공 및 이를 이용한 유전자 발현 분석 서비스를 제공하고 있다. 체적으로 제작되는 마이크로어레이 서비스는 2001년 도입한 ISO9001 품질인증시스템의 기반하에서 제작부터 생산까지의 엄격한 품질관리 과정을 거쳐서 고품질의 마이크로어레이를 이용한 분석서비스를 제공 하고 있다. 또한 고객요구형 서비스를 위하여 국외 유수의 마이크로어레이 회사 (Agilent, Microarray Inc, TIGR, Eurogentec 등)의 whole genome 기반의 마이크로어레이 제품을 이용한 분석서비스를 제공하고 있으며 마이크로어레이 실험을 위해서 필수적으로 이용되고 있는 시약 (labeling kit), 마이크로어레이 hybridization을 위한 hardware (hybridization chamber, hnay centrifuge)등을 자체적으로 개발하여 공급하고 있다. DNA copy number 측정을 위한 Array CGH 분석을 위해서는 자체적으로 제작공구하고 있는 human cDNA 마이크로어레이 (17K/25K) 그기고 rat (5.0K) 마이크로어레이를 이용한 분석서비스 및 whole genome 기반의 Agilent 올리고뉴클레오타이드 CGH 어레이 (44K, 35Kb resolution)를 이용한 분석서비스를 제공하고 있다. Epigenetic study를 하는 연구자들을 위한 메틸레이션 마이크로어레이 분석 서비스를 제공하고 있다. 기존분석법인 Bisulfite 처리기반의 분석이 아닌 enzyme digestion후 PCR 증폭방법을 이용한 분석방법을 이용함으로써, bisulfite 처리에 의한 DNA 손실문제를 최소화 하였다. 현재 50개의 문헌을 통해 잘 보고된 메틸레이션 유전자들에 대한 분석서비스를 제공하고 있으며, 지속적으로 표적컨텐츠의 숫자를 증가시킬 예정이다. 최근 많은 연구자들의 관심을 끌고 있는 micro RNA 검출을 위한 DNA 마이크로어레이 서비스를 제공할 예정이다 (2006년 3월 출시). 현재 까지 알려진 약 320개의 모든 miRNA를 탑재하고 있는 소형 DNA 마이크로어레이를 이용한 분석서비스로서 1장의 마이크로어레이 실험을 통하여 알려진 모든 miRNA의 비교분석이 가능하다. 마이크로어레이 실험 뿐만 아니라 data 분석을 위한 software도 상당히 중요한 비중을 차지하고 있다 이를 위하여 (주)지노믹트리는 Agilent에서 개발한 GeneSpring GX (유전자 발현 분석), Signet (마이크로어레이 database) 및 GeneSpring GT (SNP 분석)를 공급하고 있다. 통계적인 기반 지식의 없은 일반 user들을 위한 간편하면서도 종합적인 기능을 포함하고 있는 우수한 프로그램으로 이미 국제적으로 많은 인정을 받고 있다. (주)지노믹트리는 국내외 많은 연구자들의 경제적, 시간적 연구여건을 고려한 마이크로어레이 토탈솔루션을 제공하고 있으며, 실험 분석에서 data 마이닝 그리고 마이크로어레이 실험 디자인에 이르는 토탈솔루션을 제공하고 있다.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.299-308
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• 2007

Purpose: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Materials and Methods: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Results: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Conclusion: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.

• Interdisciplinary Bio Central
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• v.4 no.1
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• pp.2.1-2.7
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• 2012

Introduction: Histone deacetylases (HDAC) are a class of enzymes that remove acetyl groups from ${\varepsilon}$-N-acetyl lysine amino acids of histone proteins. Their action is opposite to that of histone acetyltransferase that adds acetyl groups to these lysines. Only few HDAC inhibitors are approved and used as anti-cancer therapeutics. Thus, discovery of new and potential HDAC inhibitors are necessary in the effective treatment of cancer. Materials and Methods: This study proposed a method using support vector machine (SVM) to classify HDAC8 inhibitors and non-inhibitors in early-phase virtual compound filtering and screening. The 100 experimentally known HDAC8 inhibitors including 52 inhibitors and 48 non-inhibitors were used in this study. A set of molecular descriptors was calculated for all compounds in the dataset using ADRIANA. Code of Molecular Networks. Different kernel functions available from SVM Tools of free support vector machine software and training and test sets of varying size were used in model generation and validation. Results and Conclusion: The best model obtained using kernel functions has shown 75% of accuracy on test set prediction. The other models have also displayed good prediction over the test set compounds. The results of this study can be used as simple and effective filters in the drug discovery process.

• BMB Reports
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• v.43 no.8
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• pp.547-553
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• 2010

Biochemical tests such as aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are useful for diagnosing patients with liver disease. In this study, we tested the association between copy number variation and the hepatic biomarkers AST and ALT based on 8,842 samples from population-based cohorts in Korea. We used Affymetrix Genome-Wide Human 5.0 arrays and identified 10,534 CNVs using HelixTree software. Of the CNVs tested using univariate linear regression, 100 CNVs were significant for AST and 16 were significant for ALT (P < 0.05). We identified 39 genes located within the CNV regions. DKK1 and HS3ST3B1 were shown to play roles in heparan sulfate biosynthesis and the Wnt signaling pathway, respectively. NAF1 and NPY1R were associated with glycoprotein processes and neuropeptide Y receptor activity based on GO categories. PTER, SOX14 and TM7SF4 were expressed in liver. DPYS and CTSC were found to be associated with dihydropyrimidinuria and Papillon-Lefevre syndrome phenotypes using OMIM. NPY5R was found to be associated with dyslipidemia using the Genetic Association Database.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.991-998
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• 2004

In many practical teaming problems including bioinformatics area, there is a small amount of labeled data along with a large pool of unlabeled data. Labeled examples are fairly expensive to obtain because they require human efforts. In contrast, unlabeled examples can be inexpensively gathered without an expert. A common method with unlabeled data for data classification and analysis is co-training. This method uses a small set of labeled examples to learn a classifier in two views. Then each classifier is applied to all unlabeled examples, and co-training detects the examples on which each classifier makes the most confident predictions. After some iterations, new classifiers are learned in training data and the number of labeled examples is increased. In this paper, we propose a new co-training strategy using unlabeled data. And we evaluate our method with two classifiers and two experimental data: WebKB and BIND XML data. Our experimentation shows that the proposed co-training technique effectively improves the classification accuracy when the number of labeled examples are very small.

• Herbal Formula Science
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• v.28 no.4
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• pp.313-325
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• 2020

Objectives : To explore the associated potential pathways and molecular targets of Hwangryunhaedok-tang(HHT) by the approaches of network pharmacology and bioinformatics in traditional chinese medicine(TCM). Methods : Hwangryunhaedok-tang constituent drugs(Coptidis Rhizoma, CR; Scutellariae Radix, SR; Phellodendri Cortex, PC; Gardeniae Fructus, GF) and their processing types were searched from TCM systems pharmacology(TCMSP). The databases of TCMSP, Kyoto Encyclopedia of Genes and Genomes(KEGG), MCODE and STRING were used to gather information. The network of bioactive ingredients and target gene was constructed by Cytoscape software(version 3.8). Results : A total of 94 HHT active compounds(CR, 12; SR, 35; PC, 33; GF, 14, respectively) were found, and HHT were identified by TCMSP. Applications of KEGG and MCODE analysis indicates that total of 6 bioactive ingredients in the top 10% ranking were obtained and 32 diseases of HHT were screened. The molecular pathway analysis revealed that HHT exerts cancer, inflammation and cerebrovascular diseases effects by acting on several signaling pathway. In addition, HHT found that three genes(e.g. SPIN1, TRIM25, and APP) correlate with the aforementioned diseases. Conclusions : This study showed that network pharmacology analysis is useful to elucidate the complex mechanisms of action of HHT.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.11
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• pp.1532-1536
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• 2015

To verified the target genes of miR-34c, bioinformatics software was used to predict the targets of miR-34c. Three possible target genes of miR-34c related to spermatogenesis and male reproductive development: zinc finger protein 148 (ZNF148), kruppel-like factor 4 (KLF4), and platelet-derived growth factor receptor alpha (PDGFRA) were predicted. Then, the expression of miR-34c and its target genes were detected in swine testicular tissue at different developmental stages by quantitative polymerase chain reaction. The results suggested that the expression of PDGFRA has the highest negative correlation with miR-34c. Then immunohistochemical staining was done to observe the morphology of swine testicular tissue at 2-days and 3, 4, 5-months of age, which indicated that PDGFRA was mainly expressed in the support cells near the basement membrane during the early development stages of testicular tissue, but that the expression of PDGFRA was gradually reduced in later stages. Therefore, western blot analyzed that the highest expression of PDGFRA was generated in 2-days old testicular tissues and the expression levels reduced at 3 and 4-months old, which correlated with the results of immunohistochemical staining. In conclusion, PDGFRA is a target gene of miR-34c.