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Anaplastic thyroid cancer (ATC) is a lethal human cancer with a 5-year survival rate of less than 10%. Recently, its genomic and transcriptomic characteristics have been extensively elucidated over 5 years owing to advance in high throughput sequencing. These efforts have extended molecular understandings into the progression mechanisms and therapeutic vulnerabilities of aggressive thyroid cancers. In this review, we provide an overview of genomic and transcriptomic alterations in ATC and poorly-differentiated thyroid cancer, which are distinguished from differentiated thyroid cancers. Clinically relevant genomic alterations and deregulated signaling pathways will be able to shed light on more effective prevention and stratified therapeutic interventions for affected patients.
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The aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Study (KoGES).
We obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested more than 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence of metabolic syndrome according to GGT quartiles in both genders.
The GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20 mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in both male and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to 1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95% CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predicting risk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women.
We suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome.
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Since the release of The Cancer Genome Atlas study of papillary thyroid carcinoma (PTC) in 2014, additional genomic studies of differentiated thyroid carcinoma (DTC) using massively-parallel sequencing (MPS) have been published. Recent advances in MPS technology have started to provide important insights into the molecular pathogenesis of DTC. In the genomic landscape, the most recurrently altered genes in DTC, which has a low mutational burden relative to other cancers, are
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Thyroid diseases, including autoimmune thyroid diseases and thyroid cancer, are known to have high heritability. Family and twin studies have indicated that genetics plays a major role in the development of thyroid diseases. Thyroid function, represented by thyroid stimulating hormone (TSH) and free thyroxine (T4), is also known to be partly genetically determined. Before the era of genome-wide association studies (GWAS), the ability to identify genes responsible for susceptibility to thyroid disease was limited. Over the past decade, GWAS have been used to identify genes involved in many complex diseases, including various phenotypes of the thyroid gland. In GWAS of autoimmune thyroid diseases, many susceptibility loci associated with autoimmunity (human leukocyte antigen [
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Thyroid cancer has one of the highest hereditary component among human malignancies as seen in medical epidemiology investigations, suggesting the potential meaningfulness of genetic studies. Here we review researches into genetic variations that influence the chance of developing non-familial differentiated thyroid cancer (DTC), focusing on the major findings of the genome-wide association studies (GWASs) of common single-nucleotide polymorphisms (SNPs). To date, eight GWAS have been performed, and the association of a number of SNPs have been reproduced in dozens of replication investigations across different ethnicities, including Korea and Japan. Despite the cumulative effect of the strongest SNPs demonstrates gradual increase in the risk for cancer and their association signals are statistically quite significant, the overall prediction ability for DTC appears to be very limited. Thus, genotyping of common SNPs only would be insufficient for evidence-based counseling in clinical setting at present. Further studies to include less significant and rare SNPs, non-SNP genetic information, gene-gene interactions, ethnicity, non-genetic and environmental factors, and development of more advanced computational algorithms are warranted to approach to personalized disease risk prediction and prognostication.
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Diabetes is a common metabolic disorder with a worldwide prevalence of 8.3% and is the leading cause of visual loss, end-stage renal disease and amputation. Recently, genome-wide association studies (GWASs) have identified genetic risk factors for diabetic microvascular complications of retinopathy, nephropathy, and neuropathy. We summarized the recent findings of GWASs on diabetic microvascular complications and highlighted the challenges and our opinion on future directives. Five GWASs were conducted on diabetic retinopathy, nine on nephropathy, and one on neuropathic pain. The majority of recent GWASs were underpowered and heterogeneous in terms of study design, inclusion criteria and phenotype definition. Therefore, few reached the genome-wide significance threshold and the findings were inconsistent across the studies. Recent GWASs provided novel information on genetic risk factors and the possible pathophysiology of diabetic microvascular complications. However, further collaborative efforts to standardize phenotype definition and increase sample size are necessary for successful genetic studies on diabetic microvascular complications.
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