Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2018 Jun;33(2):164-174.
doi: 10.3803/EnM.2018.33.2.164.

Genetic Polymorphism Predisposing to Differentiated Thyroid Cancer: A Review of Major Findings of the Genome-Wide Association Studies

Affiliations
Review

Genetic Polymorphism Predisposing to Differentiated Thyroid Cancer: A Review of Major Findings of the Genome-Wide Association Studies

Vladimir A Saenko et al. Endocrinol Metab (Seoul). 2018 Jun.

Abstract

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.

Keywords: Genetic loci; Genetic predisposition to disease; Genetic testing; Genome-wide association study; Polymorphism, single nucleotide; Thyroid neoplasms.

PubMed Disclaimer

Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1
Fig. 1. A Manhattan plot of the combined thyroid cancer genome-wide association study (GWAS) results in the populations of European ancestry according to [24]. The vertical axis is the negative log10-transformed P values for association signals of single-nucleotide polymorphisms across 22 autosomal chromosomes (horizontal axis). Annotated in black color are the loci discovered in earlier studies and replicated by different groups. Red color correspond to six novel loci associated with thyroid cancer detected by meta-analysis of GWAS data from 3,001 patients and 287,550 controls of the European descent. Note that statistical significance of association of the newly detected loci is generally lower than of those discovered earlier. The figure is derived from the open access article [24] according to a Creative Commons Attribution 4.0 International License and is presented here with minor modifications. FOXE1, forkhead box E1; PTCSC2, papillary thyroid carcinoma susceptibility candidate 2; PCNXL2, pecanex homolog 2; DIRC3, disrupted in renal carcinoma 3; LRRC34, leucine rich repeat containing 34; TERT, telomerase reverse transcriptase; EPB41L4A, erythrocyte membrane protein band 4.1 like 4A; NRG1, neuregulin 1; OBFC1, STN1, CST complex subunit; PCNXL3, pecanex homolog 3; MBIP1, MAP3K12 binding inhibitory protein 1; SMAD3, SMAD family member 3.
Fig. 2
Fig. 2. A Manhattan plot of the genome-wide association study of differentiated thyroid cancer (DTC) in the Korean population according to [40]. The vertical axis is the negative log10-transformed P values for association signals of single-nucleotide polymorphisms across 22 autosomal chromosomes (horizontal axis). Annotated in black color are the loci described earlier in the populations of European ancestry. Red color correspond to seven novel loci associated with DTC and specific to the Korean population based on gene scans of 470 patients and 8,279 controls. The red horizontal line represents the genome-wide significance threshold P=5E-08, and the blue horizontal line represents the genome-wide suggestiveness threshold P=1E-05. The figure is derived from the open access article [40] according to a Creative Commons Attribution 4.0 International License and is presented here with minor modifications. VAV3, vav guanine nucleotide exchange factor 3; PCNXL2, pecanex homolog 2; DIRC3, disrupted in renal carcinoma 3; FHIT, fragile histidine triad; SEPT11, septin 11; NRG1, neuregulin 1; FOXE1, forkhead box E1; SLC24A6, solute carrier family 8 member B1; MSRB3, methionine sulfoxide reductase B3; NKX2-1, NK2 homeobox 1; INSR, insulin receptor.

References

    1. Statistics Korea. Cancer incident cases and incidence rates by site (24 items) and sex [Internet] Daejeon: Statistics Korea; c2014. [cited 2018 May 3]. Available from: http://kosis.kr/eng/statisticsList/statisticsList_01List.jsp?vwcd=MT_ETI....
    1. Center for Cancer Control and Information Services, National Cancer Center. Projected cancer statistics, 2017 [Internet] Tokyo: National Cancer Center; 2017. [updated 2017 Oct 27]. [cited 2018 May 3]. Available from: https://ganjoho.jp/en/public/statistics/short_pred.html.
    1. Saika K, Matsuda T, Sobue T. Incidence rate of thyroid cancer by histological type in Japan. Jpn J Clin Oncol. 2014;44:1131–1132. - PubMed
    1. Sueta A, Ito H, Kawase T, Hirose K, Hosono S, Yatabe Y, et al. A genetic risk predictor for breast cancer using a combination of low-penetrance polymorphisms in a Japanese population. Breast Cancer Res Treat. 2012;132:711–721. - PubMed
    1. Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359:2208–2219. - PMC - PubMed

LinkOut - more resources