• Genomics & Informatics
• /
• v.21 no.3
• /
• pp.38.1-38.8
• /
• 2023

Non-small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DEmiRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co- expression network. Next, four modules were selected based on the Z_summary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.8
• /
• pp.3189-3194
• /
• 2015

Background: To explore the expression of TS, RRM, ERCC1, TUBB3 and STMN1 genes in the tissues of patients with non-small cell lung cancer (NSCLC) and its significance in guiding the postoperative adjuvant chemotherapy. Materials and Methods: Real time polymerase chain reaction (PCR) was applied to detect the expression of TS, RRM, ERCC1, TUBB3 and STMN1 genes in the tissues of NSCLC patients so as to analyze the relationship between the expression of each gene and the clinical characteristics and to guide the postoperative individualized chemotherapy according to the detection results of NSCLC patients. Results: Expression of TS gene was evidently higher in patients with adenocarcinoma than those with non-adenocarcinoma (P=0.013) and so was the expression of ERCC1 (P=0.003). The expression of TUBB3 gene was obviously higher in NSCLC patients in phases I/II and IV than those in phase III ($P_1=0.021$; $P_2=0.004$), and it was also markedly higher in patients without lymph node metastasis than those with (P=0.008). The expression of STMN1 gene was apparently higher in patients in phase I/II than those in phase IV (P=0.002). There was no significant difference between the rest gene expression and the clinical characteristics of NSCLC patients (P>0.05). Additionally, the diseasefree survival (DFS) was significantly longer in patients receiving gene detections than those without (P=0.021). Conclusions: The selection of chemotherapeutic protocols based singly on patients' clinical characteristics has certain blindness. However, the detection of tumor-susceptible genes can guide the postoperative adjuvant chemotherapy and prolong the DFS of NSCLC patients.

• Korean Journal of Microbiology
• /
• v.27 no.2
• /
• pp.85-91
• /
• 1989

The effect of SV40 and murine cytomegalovirus (MCMV) enhancers on the general and tissue-specific gene expression was investigated. Recombinant plasmids containing these transcriptional engancers downstream of a structural gene for chloramphenicol acetyl transferase (CAT) were constructed. The transient expression of CAT gene from these plasmids was measured in monkey (CV1PD) and HeLa cells. Both SV40 and MCMV engancers activated the expression of CAT gene by more than 20 and 150 folds, respectively, compared with engancerless plasmids. When the SV40 promoter, upstream of CAT gene, was replaced with 2.2 kbp of promoter regulatory region of bovine growth hormone (bGH) gene, there was no expression of CAT even in the presence of enhancers, reflecting the tissue-specific expression of bGH genes. However, when the bGH regulatory region was shortened to 230 bp, the expression level increased dramatically in the presence of SV40 enhancers. In contrast, the expression from the shortened promoter was only marginally activated by the stronger MCMV enhancer.

• Genomics & Informatics
• /
• v.17 no.4
• /
• pp.45.1-45.3
• /
• 2019

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.29-34
• /
• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.

• BMB Reports
• /
• v.39 no.4
• /
• pp.457-463
• /
• 2006

Insulin resistance is commonly observed in patients prior to the development of type 2 diabetes and may predict the onset of the disease. We tested the hypothesis that impairment in insulin stimulated glucose-disposal in insulin resistant patients would be reflected in the gene expression profile of skeletal muscle. We performed gene expression profiling on skeletal muscle of insulin resistant and insulin sensitive subjects using microarrays. Microarray analysis of 19,000 genes in skeletal muscle did not display a significant difference between insulin resistant and insulin sensitive muscle. This was confirmed with real-time PCR. Our results suggest that insulin resistance is not reflected by changes in the gene expression profile in skeletal muscle.

• Journal of Microbiology
• /
• v.35 no.2
• /
• pp.149-151
• /
• 1997

A plasmid vector with combined features of yeast shuttle vector and mammalian expression vector was constructed to facilitate expression of cloned gene in both cell-types. All necessary elements required for plasmid maintenance and selection in E. coli, yeast and mammalian cells were size-economically arranged in this plasmid. The numan cytomegalovirus (CMV) immediate early promoter and yeast GAL1 promoter were sequentially placed in front of the gene to be expressed. The synthetic splicing donor and acceptor sequences were inserted into the immediate upstream and downstream of the GAL1 promotor, allowing the CMV promotor to direct the expression of a given gene in mammalian cell environment by splicing out the interfering GAL1 promotor sequence. When the resulting vector containing LacZ as a gene was introduced into yeast and mammalian cells, both cells efficiently produced .betha.-galactosidase, dimonstrating its dual host usage.

• BMB Reports
• /
• v.29 no.1
• /
• pp.88-91
• /
• 1996

The anaerobically expressed gene aeg-46.5, which had been identified by the operon fusion technique with a hybrid bacteriophage of ${\lambda}$ and Mu, ${\lambda}$placMu53, was studied for its expression pattern and growth. The expression of aeg-46.5 was studied in the wild-type cell and mutant cells that have mutation (s) in the control gene of anaerobic respiration (fnr) and nitrate response (narL and narP). The ${\beta}$-galactosidase reporter gene showed maximum expression in narL host after two hours of aerobic to anaerobic switch in M9-Glc-nitrate medium. Both 40 mM and 100 mM concentrations of nitrate ion in the medium had little effect on expression level. We propose that aeg-46.5 is subject to multiple regulations of anaerobic activation by Fnr, nitrate activation by NarP and repression mediated by NarL.

• BMB Reports
• /
• v.51 no.5
• /
• pp.211-218
• /
• 2018

Chromatin is an intelligent building block that can express either external or internal needs through structural changes. To date, three methods to change chromatin structure and regulate gene expression have been well-documented: histone modification, histone exchange, and ATP-dependent chromatin remodeling. Recently, a growing body of literature has suggested that histone tail cleavage is related to various cellular processes including stem cell differentiation, osteoclast differentiation, granulocyte differentiation, mammary gland differentiation, viral infection, aging, and yeast sporulation. Although the underlying mechanisms suggesting how histone cleavage affects gene expression in view of chromatin structure are only beginning to be understood, it is clear that this process is a novel transcriptional epigenetic mechanism involving chromatin dynamics. In this review, we describe the functional properties of the known histone tail cleavage with its proteolytic enzymes, discuss how histone cleavage impacts gene expression, and present future directions for this area of study.

• Genomics & Informatics
• /
• v.17 no.3
• /
• pp.30.1-30.4
• /
• 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.