• Journal of Microbiology and Biotechnology
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• 제32권3호
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• pp.365-377
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• 2022

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

• Genomics & Informatics
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• 제9권4호
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• pp.143-151
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• 2011

In this study, we propose a novel, intuitive method of constructing an expression quantitative trait (eQT) network that is related to the metabolic syndrome using LOD scores and peak loci for selected eQTs, based on the concept of gene-gene interactions. We selected 49 eQTs that were related to insulin resistance. A variance component linkage analysis was performed to explore the expression loci of each of the eQTs. The linkage peak loci were investigated, and the "support zone" was defined within boundaries of an LOD score of 0.5 from the peak. If one gene was located within the "support zone" of the peak loci for the eQT of another gene, the relationship was considered as a potential "directed causal pathway" from the former to the latter gene. SNP markers under the linkage peaks or within the support zone were searched for in the database to identify the genes at the loci. Two groups of gene networks were formed separately around the genes IRS2 and UGCGL2. The findings indicated evidence of networks between genes that were related to the metabolic syndrome. The use of linkage analysis enabled the construction of directed causal networks. This methodology showed that characterizing and locating eQTs can provide an effective means of constructing a genetic network.

• Asian Pacific Journal of Cancer Prevention
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• 제13권8호
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• pp.3681-3685
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• 2012

Background: In breast cancer, estrogen receptors have been demonstrated to interact with transcription factors to regulate target gene expression. However, high-throughput identification of the transcription regulation relationship between transcription factors and their target genes in response to estradiol is still in its infancy. Purpose: Thus, the objective of our study was to interpret the transcription regulation network of MCF7 breast cancer cells exposed to estradiol. Methods: In this work, GSE11352 microarray data were used to identify differentially expressed genes (DEGs). Results: Our results showed that the MYB (v-myb myeloblastosis viral oncogene homolog [avian]), PGR (progesterone receptor), and MYC (v-myc myelocytomatosis viral oncogene homolog [avian]) were hub nodes in our transcriptome network, which may interact with ER and, in turn, regulate target gene expression. MYB can up-regulate MCM3 (minichromosome maintenance 3) and MCM7 expression; PGR can suppress BCL2 (B-cell lymphoma 2) expression; MYC can inhibit TGFB2 (transforming growth factor, beta 2) expression. These genes are associated with breast cancer progression via cell cycling and the $TGF{\beta}$ signaling pathway. Conclusion: Analysis of transcriptional regulation may provide a better understanding of molecular mechanisms and clues to potential therapeutic targets in the treatment of breast cancer.

• 한국컴퓨터정보학회논문지
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• 제12권6호
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• pp.105-113
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• 2007

유전자 조절망은 유전자의 발현이 다른 유전자에게 영향을 주는 것을 표현하는 유전자 망이다. 오늘날 마이크로 어레이 실험으로부터 유전자의 발현량을 측정한 대용량의 데이터가 이용 가능하다. 전형적인 데이터중의 하나는 특정 유전자를 제거한 후 다른 유전자의 발현량을 측정한 steady-state data이다. 본 논문은 이런 측정 데이터를 이용하여 중복 정보를 최소화하는 유전자 조절망을 재구성하는 방법을 제시한다. 제시한 모델은 기존 연구에서는 고려되지 않았던 사이클 형태로 나타나는 자동 조절 기능을 고려하였고, 또한 유전자의 억제자 또는 촉진자 역할을 고려하였다.

• Genomics & Informatics
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• 제17권3호
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• pp.30.1-30.4
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• 2019

Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.

• 정보처리학회논문지D
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• 제14D권1호
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• pp.9-20
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• 2007

유전자들의 그룹은 복잡한 상호작용들을 통해 세포의 기능이 조절되며 이러한 상호작용을 하는 유전자 그룹들을 유전자 조절 네트워크 (GRNs: Gene Regulatory Networks)라고 한다. 이전의 유전자 발현 분석 기법인 군집화와 분류는 단지 상동성에 의한 유전자들 사이의 소속을 결정하는 데에는 유용하나 분자 활동에서의 같은 클래스에서 발견되어지는 유전자들 사이의 조절 관계를 식별할 수 없다. 더욱이 유전자들이 어떻게 연관되는 지와 유전자들이 서로 어떻게 조절하는지에 대한 매커니즘의 이해가 필요하다. 따라서 이 논문에서는 시계열 마이크로어레이 데이터로부터의 유전자들의 조절 관계를 발견하기 위해서 빈발 패턴 마이닝과 연쇄 규칙을 이용한 새로운 접근법을 제안하였다. 이 기법에서는 먼저, 빈발 패턴 마이닝 적용을 위한 적절한 데이터 변환 방법을 제안하였고 FP-growth을 이용하여 유전자 발현 패턴들을 발견한다. 그런 다음, 연쇄 규칙을 이용하여 빈발한 유전자 패턴들로부터 유전자 조절 네트워크를 구축하였다. 마지막으로 제안된 기법의 검증은 공개된 유전자들의 조절 관계와 실험 결과의 일치함을 보임으로써 평가하였다.

• Genomics & Informatics
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• 제18권3호
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• pp.26.1-26.6
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• 2020

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type-specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• Bioinformatics and Biosystems
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• 제2권1호
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• pp.24-27
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• 2007

Human cell regulatory mechanism is one of suspicious problems among biologists. Here we tried to uncover the human breast cancer cell regulatory mechanism from gene expression data (Marc J. Van de vijver, et. al., 2002) using a module network algorithm which is suggested by Segal, et. al.(2003) Finally, we derived a module network which consists of 50 modules and 10 tree depths. Moreover, to validate this candidate network, we applied a GO enrichment test and known transcription factor-target relationships from Transfac(R) (V. Matys, et. al, 2006) and HPRD database (Peri, S. et al., 2003).

• Journal of Veterinary Science
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• 제21권6호
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• pp.91.1-91.15
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• 2020

Background: Sulforaphane (SFN) is an isothiocyanate compound present in cruciferous vegetables. Although the anti-inflammatory effects of SFN have been reported, the precise mechanism related to the inflammatory genes is poorly understood. Objectives: This study examined the relationship between the anti-inflammatory effects of SFN and the differential gene expression pattern in SFN treated ob/ob mice. Methods: Nitric oxide (NO) level was measured using a Griess assay. The inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) expression levels were analyzed by Western blot analysis. Pro-inflammatory cytokines (tumor necrosis factor [TNF]-α, interleukin [IL]-1β, and IL-6) were measured by enzyme-linked immunosorbent assay (ELISA). RNA sequencing analysis was performed to evaluate the differential gene expression in the liver of ob/ob mice. Results: The SFN treatment significantly attenuated the iNOS and COX-2 expression levels and inhibited NO, TNF-α, IL-1β, and IL-6 production in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. RNA sequencing analysis showed that the expression levels of 28 genes related to inflammation were up-regulated (> 2-fold), and six genes were down-regulated (< 0.6-fold) in the control ob/ob mice compared to normal mice. In contrast, the gene expression levels were restored to the normal level by SFN. The protein-protein interaction (PPI) network showed that chemokine ligand (Cxcl14, Ccl1, Ccl3, Ccl4, Ccl17) and chemokine receptor (Ccr3, Cxcr1, Ccr10) were located in close proximity and formed a "functional cluster" in the middle of the network. Conclusions: The overall results suggest that SFN has a potent anti-inflammatory effect by normalizing the expression levels of the genes related to inflammation that were perturbed in ob/ob mice.