• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.205-216
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• 2015

Background: Many developing countries are lagging behind in reporting epidemiological data for individual central nervous system (CNS) tumors. This paper aimed to elicit patterns for the epidemiology of individual World Health Organization (WHO) classified CNS tumors in countries registered by WHO as "developing". Materials and Methods: Cyber search was carried out through 66 cancer networks/registries and 181 PubMed published papers that reported counts of CNS tumors for the period of 2009-2012. The relationship between the natural log of incidence Age Standardized Rate (ASR) reported by Globocan and Latitude/ Longitude was investigated. Results: Registries for 21 countries displayed information related to CNS tumors. In contrast tends for classified CNS tumor cases were identified for 38 countries via 181 PubMed publications. Extracted data showed a majority of unclassified reported cases [PubMed (38 countries, 45.7%), registries (21 countries, 96.1%)]. For classified tumors, astrocytic tumors were the most frequently reported type [PubMed (38 countries, 1,245 cases, 15.7%), registries (21 countries, 627 cases, 1.99%]. A significant linear regression relationship emerged between latitudes and reported cases of CNS tumors. Conclusions: Previously unreported trends of frequencies for individually classified CNS tumors were elucidated and a possible link of CNS tumors occurrence with geographical location emerged.

• Journal of Genetic Medicine
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• v.19 no.1
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• pp.1-6
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• 2022

Radiation therapy (RT) is a very important treatment for cancer that irradiates a large amount of radiation to lead cancer cells and tissues to death. The progression of RT in the aspect of personalized medicine has greatly advanced over the past few decades in the field of technical precision responding anatomical characteristics of each patient. However, the consideration of biological heterogeneity that makes different effect in individual patients has not actually applied to clinical practice. There have been numerous discovery and validation of biomarkers that can be applied to improve the efficiency of radiotherapy, among which those related to genomic information are very promising developments. These genome-based biomarkers can be applied to identify patients who can benefit most from altering their therapeutic dose and to select the best chemotherapy improving sensitivity to radiotherapy. The genomics-based biomarkers in radiation oncology focus on mutational changes, particularly oncogenes and DNA damage response pathways. Although few have translated into clinically viable tools, there are many promising candidates in this field. In this review the prominent mutation-based biomarkers and their potential for clinical translation will be discussed.

• Journal of Interdisciplinary Genomics
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• v.5 no.2
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• pp.32-34
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• 2023

Resistance to thyroid hormone syndrome (RTH) is a genetic disease caused by the mutation of either the thyroid hormone receptor-β (THRB) gene or the thyroid hormone receptor-α (THRA) gene. RTH caused by THRB mutations (RTH-β) is characterized by the target tissue's response to thyroid hormone, high levels of triiodothyronine and/or thyroxine, and inappropriate secretion of thyroid-stimulating hormone (TSH). THRA mutation is characterized by hypothyroidism that affects gastrointestinal, neurological, skeletal, and myocardial functions. Most patients do not require treatment, and some patients may benefit from medication therapy. These syndromes are characterized by decreased tissue sensitivity to thyroid hormones, generating various clinical manifestations. Thus, clinical changes of resistance to thyroid hormones must be recognized and differentiated, and an approach to the practice of personalized medicine through an interdisciplinary approach is needed.

• Genomics & Informatics
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• v.10 no.4
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• pp.239-243
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• 2012

Gestational diabetes mellitus (GDM) is a complex metabolic disorder of pregnancy that is suspected to have a strong genetic predisposition. It is associated with poor perinatal outcome, and both GDM women and their offspring are at increased risk of future development of type 2 diabetes mellitus (T2DM). During the past several years, there has been progress in finding the genetic risk factors of GDM in relation to T2DM. Some of the genetic variants that were proven to be significantly associated with T2DM are also genetic risk factors of GDM. Recently, a genome-wide association study of GDM was performed and reported that genetic variants in CDKAL1 and MTNR1B were associated with GDM at a genome-wide significance level. Current investigations using next-generation sequencing will improve our insight into the pathophysiology of GDM. It would be important to know whether genetic information revealed from these studies could improve our prediction of GDM and the future development of T2DM. We hope further research on the genetics of GDM would ultimately lead us to personalized genomic medicine and improved patient care.

• Genomics & Informatics
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• v.11 no.4
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• pp.186-190
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• 2013

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

• Genomics & Informatics
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• v.17 no.4
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• pp.38.1-38.6
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• 2019

DNA methylation is a relatively stable epigenetic modification that can regulate and stabilize gene expression patterns and hence establish cell identity. Because metabolic intermediates are key factors of DNA methylation and demethylation, perturbations in metabolic homeostasis can trigger alterations in cell-specific patterns of DNA methylation and contribute to disease development, including type 2 diabetes (T2D). During the past decade, genome-wide DNA methylation studies of T2D have expanded our knowledge of the molecular mechanisms underlying T2D. This review summarizes case-control studies of the DNA methylome of T2D and discusses DNA methylation as both a cause and consequence of T2D. Therefore, DNA methylation has potential as a promising T2D biomarker that can be applied to the development of therapeutic strategies for T2D.

• Molecules and Cells
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• v.41 no.4
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• pp.264-270
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• 2018

Cells cope with diverse intrinsic and extrinsic stimuli in order to make adaptations for survival. The epigenetic landscape plays a crucial role in cellular adaptation, as it integrates the information generated from stimuli. Signaling pathways induced by stimuli communicate with chromatin to change the epigenetic landscape through regulation of epigenetic modifiers. Metabolic dynamics altered by these stimuli also affect the activity of epigenetic modifiers. Here, I review the current understanding of epigenetic regulation via signaling and metabolic pathways. In addition, I will discuss possible ways to achieve specificity of epigenetic modifications through the cooperation of stimuli-induced signal transduction and metabolic reprogramming.

• Genomics & Informatics
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• v.11 no.1
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• pp.24-33
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• 2013

We had analyzed 10 exome sequencing data and single nucleotide polymorphism chips for blood cancer provided by the PGM21 (The National Project for Personalized Genomic Medicine) Award program. We had removed sample G06 because the pair is not correct and G10 because of possible contamination. In-house software somatic copy-number and heterozygosity alteration estimation (SCHALE) was used to detect one loss of heterozygosity region in G05. We had discovered 27 functionally important mutations. Network and pathway analyses gave us clues that NPM1, GATA2, and CEBPA were major driver genes. By comparing with previous somatic mutation profiles, we had concluded that the provided data originated from acute myeloid leukemia. Protein structure modeling showed that somatic mutations in IDH2, RASGEF1B, and MSH4 can affect protein structures.

• Biomolecules & Therapeutics
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• v.26 no.1
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• pp.69-80
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• 2018

Cancer cells reprogram cellular metabolism to support the malignant features of tumors, such as rapid growth and proliferation. The cancer promoting effects of metabolic reprogramming are found in many aspects: generating additional energy, providing more anabolic molecules for biosynthesis, and rebalancing cellular redox states in cancer cells. Metabolic pathways are considered the pipelines to supply metabolic cofactors of epigenetic modifiers. In this regard, cancer metabolism, whereby cellular metabolite levels are greatly altered compared to normal levels, is closely associated with cancer epigenetics, which is implicated in many stages of tumorigenesis. In this review, we provide an overview of cancer metabolism and its involvement in epigenetic modifications and suggest that the metabolic adaptation leading to epigenetic changes in cancer cells is an important non-genetic factor for tumor progression, which cooperates with genetic causes. Understanding the interaction of metabolic reprogramming with epigenetics in cancers may help to develop novel or highly improved therapeutic strategies that target cancer metabolism.

• BioChip Journal
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• v.12 no.4
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• pp.304-308
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• 2018

Atopic disease is caused by a complex combination of environmental factors and genetic factors, and studies on influence of exposure to various environmental factors on atopic diseases are continuously reported. However, the exact cause of atopic dermatitis is not yet known. Our study was conducted to analyse the association of SNPs with the development of atopic disease in a small cohort. Samples were collected from the Mothers' and Children's Environmental Health (MOCEH) study and 192 cord blood DNA samples were used to identify incidence of atopy due to influence of exposure to environmental factors. Genetic elements were analysed using a precision medicine research (PMR) array designed with various SNPs for personalized medicine. Case-control analysis of atopy disease revealed 253 significant variants (p<0.0001) and SNPs on five genes (CARD11, ZNF365, KIF3A, DMRTA1, and SFMBT1) were variants identified in previous atopic studies. These results are important to confirm the genetic mutation that may lead to the onset of foetal atopy due to maternal exposure to harmful environmental factors. Our results also suggest that a small-scale genome-wide association analysis is beneficial to confirm specific variants as direct factors in the development of atopy.