Skip to main content
  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
    • Progress and Priorities
    • Collections
      • COVID-19 & Cancer Resource Center
      • Disparities Collection
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Informing Public Health Policy
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
    • Progress and Priorities
    • Collections
      • COVID-19 & Cancer Resource Center
      • Disparities Collection
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Informing Public Health Policy
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Research Articles

A Comprehensive Meta-analysis of Case–Control Association Studies to Evaluate Polymorphisms Associated with the Risk of Differentiated Thyroid Carcinoma

Gisella Figlioli, Rossella Elisei, Cristina Romei, Ombretta Melaiu, Monica Cipollini, Franco Bambi, Bowang Chen, Aleksandra Köhler, Alfonso Cristaudo, Kari Hemminki, Federica Gemignani, Asta Försti and Stefano Landi
Gisella Figlioli
1Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
2Department of Biology, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rossella Elisei
3Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cristina Romei
3Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ombretta Melaiu
2Department of Biology, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Monica Cipollini
2Department of Biology, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Franco Bambi
4Blood Centre, Azienda Ospedaliero Universitaria A. Meyer, Firenze, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bowang Chen
1Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aleksandra Köhler
1Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
5II Medizinische Klinik, Gastrologie, Onkologie und Palliativmedizin, St.Agnes-Hospital Bocholt, Bocholt, Germany.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alfonso Cristaudo
3Department of Endocrinology and Metabolism, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kari Hemminki
1Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
6Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö, Sweden.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Federica Gemignani
2Department of Biology, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Asta Försti
1Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
6Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö, Sweden.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: stefano.landi@unipi.it a.foersti@dkfz-heidelberg.de
Stefano Landi
2Department of Biology, University of Pisa, Pisa, Italy.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: stefano.landi@unipi.it a.foersti@dkfz-heidelberg.de
DOI: 10.1158/1055-9965.EPI-15-0652 Published April 2016
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Background: Linkage analyses and association studies suggested that inherited genetic variations play a role in the development of differentiated thyroid carcinoma (DTC).

Methods: We combined the results from a genome-wide association study (GWAS) performed by our group and from published studies on DTC. With a first approach, we evaluated whether a SNP published as associated with the risk of DTC could replicate in our GWAS (using FDR as adjustment for multiple comparisons). With the second approach, meta-analyses were performed between literature and GWAS when both sources suggested an association, increasing the statistical power of the analysis.

Results: rs1799814 (CYP1A1), rs1121980 (FTO), and 3 SNPs within 9q22 (rs965513, rs7048394, and rs894673) replicated the associations described in the literature. In addition, the meta-analyses between literature and GWAS revealed 10 more SNPs within 9q22, six within FTO, two within SOD1, and single variations within HUS1, WDR3, UGT2B7, ALOX12, TICAM1, ATG16L1, HDAC4, PIK3CA, SULF1, IL11RA, VEGFA, and 1p31.3, 2q35, 8p12, and 14q13.

Conclusion: This analysis confirmed several published risk loci that could be involved in DTC predisposition.

Impact: These findings provide evidence for the role of germline variants in DTC etiology and are consistent with a polygenic model of the disease. Cancer Epidemiol Biomarkers Prev; 25(4); 700–13. ©2016 AACR.

Introduction

Thyroid carcinoma is the most common malignancy of the endocrine system showing increasing incidences over the years (1–3), with age-standardized rates (ASR) of about 6/100,000 in the developed countries. Particularly elevated ASRs were observed in Lithuania (ASR = 15.5/100,000), Italy (ASR = 13.5/100,000), Austria (ASR = 12.4/100,000), and in the United States (ASR = 9.9/100,000; refs. 4, 5). Two main thyroid carcinoma histological types can be distinguished: the “medullary” and the “non-medullary” thyroid carcinoma, the former (MTC) originating from the para-follicular cells, the latter (NMTC) from the follicular cells. NMTC comprises the most frequent subtypes, papillary (PTC) and follicular (FTC) thyroid carcinomas (defined overall as “differentiated thyroid carcinomas”, DTC), accounting for 80% and 15% of the cases, respectively. Hürthle cells (or oxyphilic cells, 5%) and poorly differentiated carcinomas (1%–6%) are considered as not common entities (6, 7).

The great majority of DTCs behaves as a sporadic form, featured by somatic mutations within RET, RAS, BRAF, or NTRK1 genes and affecting the MAPK signaling pathway (8–11). However, approximately 5% of cases, mostly PTC, have a family history (12). Inherited genetic variations play an important role in both the familial and the sporadic forms, as supported by data from linkage analyses or case–control association studies (CCASs). In particular, CCASs, when carried out with an appropriate sample size, constitute the state-of-the-art for the identification of common genetic variants associated with complex traits (13). In the literature, a large number of gene-based studies have been published in which specific a priori hypotheses have been examined. Thus, SNPs within genes encoding for proteins involved in the DNA repair, cell-cycle control, thyroid physiology, or playing a role in other types of human cancer have been investigated (14). The major weakness of these studies is that unknown genes playing a relevant role in the etiology of the disease could have been missed. This limitation is solved by genome-wide association studies (GWASs) in which the whole genome is analyzed without formulating any a priori hypothesis. GWASs on DTC allowed discovering novel variants, including those near FOXE1, DIRC3, NKX2-1 (15–17) and, more recently, those near IMMP2L, RARRES1, SNAPC4/CARD9, ARSB, BATF, DHX35, SPATA13, GALNTL4, and FOXA2 (18–20). However, to ensure a high quality and to prevent false-positive findings, highly stringent criteria are applied in the GWASs with the disadvantage of excluding SNPs truly associated with the risk. In the present work, we investigated whether SNPs associated with the susceptibility to DTC in previous CCASs could replicate in an independent GWAS carried out by our research group. Moreover, we investigated whether SNPs showing a sub-threshold genome-wide statistical significance in our GWAS could improve their association following a meta-analysis with previously published data.

Materials and Methods

Ethics statement

Study participants were recruited according to the protocols approved by the institutional review boards in accordance with the Declaration of Helsinki. All subjects provided written informed consent to participate in the study and allowed the use of their biological samples.

Study participants of the GWAS

The group of cases comprised 701 histologically confirmed DTC patients from central and southern Italy, recruited at the Cisanello Hospital in Pisa, an important Italian referral center for thyroid diseases. The control group comprised 499 healthy individuals from the Meyer Hospital in Florence without known thyroid disease, of which 390 were blood donor volunteers and 109 were healthy individuals recruited during a routine health screening. Cases and controls were frequency matched by sex, age, body mass index (BMI), and smoking habits. The patient group consisted of 22.3% males and 77.7% females with a median age of 46; the control group consisted of 23.2% males and 76.8% females with a median age of 50. The median BMI was 24.5 in cases and 24.4 in controls. The proportion of smokers was 37% in cases and 40% in controls. All cases and controls were of Caucasian origin.

Genome-wide association study

Full details of the GWAS, including the genotyping process, quality control and statistical analysis were previously described (20). Briefly, samples were genotyped using Illumina HumanOmni1-Quad_v1-0_B 1M BeadChips and Illumina HumanOmniExpress-12v1_A 730K BeadChips. Genotype calling was performed using Illumina GenomeStudio 2010 (Illumina Inc.). After applying strict quality control criteria, the analysis was restricted to the subset of genotyped SNPs common to both Illumina arrays used. Hence, 572 042 SNPs were analyzed for association with DTC risk in 690 cases and 497 controls. The adequacy of the case–control matching and the possibility of differential genotyping of cases and controls were assessed using Q-Q plots of test statistics. The genomic control inflation factor λ was calculated using the standard method by the CRAN R package GAP (Genetic Analysis Package; https://cran.r-project.org/web/packages/gap/index.html; http://www.inside-r.org/packages/cran/gap/docs/gcontrol2).

The inflation factor λ was 1.0, excluding the possibility of hidden population substructure, relatedness among subjects or differential genotype calling. Statistical analysis was conducted using PLINK version 1.06 (21).

Search strategy and selection criteria

PubMed was searched from database inception until September 2013 to collect case–control studies investigating the association between SNPs and DTC. We used the keywords polymorph* AND (papillary OR follicular OR non-medullary OR “non medullary”) AND thyroid AND (cancer OR carcinoma) AND (susceptibility OR risk OR predisposition) to collect studies carried out on DTC or PTC. The major reasons for exclusion of the studies were (i) studies not in English language; (ii) studies without odds ratio (OR) and 95% confidence interval (95% CI); (iii) case–case studies; (iv) studies on benign thyroid disease. A total of 100 original articles and five meta-analyses met our criteria and were assessed. The list of citations is reported, for brevity, in the Supplementary data. The SNPs reported in these studies were recorded and searched in the present GWAS, allowing a direct comparison between the results published in the literature with the results from the GWAS. When an SNP was not found in the GWAS, the linkage disequilibrium (LD) block around the SNP was checked using the CEU data of the 1000 Genome Project (22), and the results of SNPs in high LD (r2 ≥ 0.8) were reported. All the collected data are reported in Supplementary Tables S1 and S2.

Statistical approaches

Two statistical approaches were used to reduce the number of false positives and to increase the power of the study. With the first approach, we performed a meta-analysis of published data when more than one study was carried out on a given SNP. Then, we evaluated whether SNPs previously associated with the risk of DTC (positive, at a nominal significance level of Pass < 0.05, either in a single study or in meta-analysis) were associated also with the risk of DTC in our GWAS. These SNPs were evaluated by calculating their allelic Pass in the GWAS. In order to adjust for multiple comparisons, the false-positive discovery rate correction (FDR; ref. 23) was applied to the list of Pass obtained in the GWAS and the associations with q < 0.05 were considered as statistically significant, i.e., considered as replicating the literature data.

With the second approach, results from SNPs positive in the literature (either in a single study or in the meta-analysis of the literature) were meta-analyzed with those of GWAS. Moreover, the meta-analyses were performed also when, for a given SNP, a suggestive evidence of association (Pass < 0.20, taken arbitrarily) was observed both in the literature and in the GWAS. Because the GWAS was performed on Caucasians, the meta-analysis first was carried out in Caucasians. When literature data were not available for Caucasians, the GWAS was meta-analyzed using literature data for the available population(s).

The pooled ORs were calculated for allelic model (a vs. A) and additive model (Aa vs. AA and aa vs. AA). In case only dominant or recessive model was reported in the literature, the same model was applied for the GWAS data.

The statistics are based on the absolute counts of variant and common alleles/genotypes among cases and controls. The χ2 based Q-test was used to assess heterogeneity across studies (Phet < 0.05) and I2 statistics was calculated to quantify the proportion of the total variation across studies due to heterogeneity. In case of no significant heterogeneity, OR and 95% CI were assessed using the fixed-effect model (the Mantel–Haenszel method); otherwise the random-effects model (DerSimonian–Laird method) was used. Meta-analyses were performed by MIX 1.7 freeware software. Also in this case, adjustment for multiple comparisons was performed by applying the FDR correction and q < 0.05 were considered as significant. A SNP associated with the risk of DTC in the literature was considered replicated when found with a q < 0.05 also in the GWAS. Moreover, an SNP was considered positively associated with the risk of DTC when found with a q < 0.05 in the meta-analysis.

Results

One hundred published articles, reporting results for 316 SNPs belonging to 127 genes, met the selection criteria (see the reference list in the Supplementary data). Data collected included the reference of the literature, the gene name, the dbSNP identification number, the number of cases and controls investigated, and the OR with its 95% CI, of the allelic and additive models. In the first type of evaluation, the corresponding ORs and 95% CIs were also calculated for these SNPs based on the GWAS. In case only dominant or recessive model was reported in the literature, the same model was applied for the GWAS data. The results are reported side-by-side in Supplementary Tables S1 and S2, respectively, to allow a direct comparison. Among the 316 SNPs, 91 were associated with the risk of DTC in a statistically significant way according to the literature (Pass < 0.05). The meta-analysis of the literature data alone was performed on 46 SNPs and 13 were statistically significant at the 0.05 level (Supplementary Table S3). Fifteen of the 91 SNPs associated in any study were replicated in the GWAS at the same significance level, and the side-by-side comparison is shown in Table 1. However, only five SNPs, including CYP1A1 rs1799814, FTO rs1121980, and the GWAS identified SNPs on 9q22 (rs965513, rs7048394, and rs894673), were statistically significant after the application of FDR correction. Only one SNP (rs965513) showed to be associated in a statistically significant way in the meta-analysis of literature data and in the present GWAS (Table 1). In addition to these analyses, we adopted another approach. In order to ascertain whether an increase of statistical power could allow reaching a statistical significance, we selected SNPs showing a Pass < 0.2 (arbitrarily chosen, in any inheritance model) both in the literature and in the GWAS and we performed a meta-analysis. Moreover, we added the results of the present GWAS to meta-analyses from literature data, when these latter showed SNPs significantly associated with the risk of DTC.

View this table:
  • View inline
  • View popup
Table 1.

List of SNPs associated with the risk of DTC in previous studies published in the literature

Results from the meta-analyses are shown in Tables 2 and 3. Below, we summarize the results according to the biological processes.

View this table:
  • View inline
  • View popup
Table 2.

Meta-analyses of published data on Caucasians with data from present GWAS

View this table:
  • View inline
  • View popup
Table 3.

Meta-analyses between the non-Caucasian population(s) from the literature and present GWAS

SNPs within DNA repair genes

A total of 64 SNPs located within 27 genes involved in DNA repair pathways were investigated so far in the context of DTC. Of them, 10 were associated with the disease in the literature with at least one genetic model but none was replicated in the present GWAS under the allelic model (Table 1). However, a statistical significance was observed for rs25487 within XRCC1 (OR = 0.76, 95% CI, 0.59–0.99 for heterozygotes in the additive model). When this result was combined in meta-analysis with seven previous studies carried out on Caucasians, an OR of 0.92 (95% CI, 0.85–0.99) was found in the allelic model; however, this result was not significant after FDR correction (q = 0.06). Moreover, the meta-analyses revealed an increased risk for rs2708906, at 5′ region near HUS1 (OR = 1.34; 95% CI, 1.08–1.64; q = 0.04 for heterozygotes and OR = 1.52; 95% CI, 1.16–2.00; q = 0.01 for homozygotes; Table 2).

SNPs within cell-cycle regulation and apoptosis genes

Thirty-three common SNPs in 15 genes involved in the cell-cycle regulation or in apoptosis were collected. A total of nine significance associations were published so far, but only rs4658973 (WDR3) was replicated in the GWAS (allelic model: OR = 0.83; 95% CI, 0.70–1.00). Meta-analysis on Caucasians again suggested the role of this variant in DTC etiology with OR = 0.71 (95% CI, 0.61–0.82, allelic model), remaining significant after FDR correction (q = 1.8 × 10−6; Tables 1 and 2). Moreover, when the GWAS association on Caucasians was combined with a mixed population from a previous study, a statistical significance was found for rs2279744 (MDM2, OR = 1.40, 95% CI, 1.12–1.74; only recessive model was available for meta-analysis; Table 3).

SNPs within genes encoding for xenobiotic metabolism enzymes

Through PubMed search, 67 SNPs within 19 genes encoding for xenobiotic metabolism enzymes (XME) were collected. Overall, 19 positive associations were reported in the literature. Interestingly, rs1799814 (CYP1A1) showed a strong association in GWAS and it remained statistically significant after FDR correction (Table 1). In the meta-analysis, a high risk was found associated with the rare allele (OR = 1.86, 95% CI, 1.50–2.30, q = 4.4 × 10−8). Besides rs1799814, meta-analyses on Caucasians, after multiple testing correction, revealed a possible role also for rs1041740 (SOD1, q = 5.5 × 10−3; allelic model), rs12626475 (3′ region near SOD1, q = 0.02; allelic model), and rs3924194 (UGT2B7, q = 0.04; for heterozygotes; Table 2).

SNPs within genes involved in thyroid function

Seven genes playing a key role in thyroid function were assessed in DTC studies by genotyping 21 SNPs. Only five SNPs were reported as significantly associated with DTC, and none of them was significant in the GWAS. Thus, the meta-analyses did not confirm the role of these variants in DTC etiology (Tables 1 and 2).

SNPs within MAPK pathway genes

Of 17 SNPs within 8 genes of the MAPK pathway, four predisposing variants were reported. GWAS replicated the significant association found for rs12628 (HRAS, OR = 1.23, 95% CI, 1.02–1.48 in the allelic model), but a high heterogeneity was found between the study population previously analyzed and the present study (Phet < 0.0001). Thus, no significant evidence of association was identified in the meta-analysis using the random-effect model (Table 2).

SNPs within immune response and inflammation genes

Fifteen genes and 33 SNPs involved in immunity or in inflammation pathways were analyzed to identify susceptibility variants for DTC and eight significantly associated SNPs were published (Table 1). The present GWAS replicated the possible role of rs1126667 (ALOX12, OR = 1.34, 95% CI, 1.02–1.75; heterozygotes) and rs2292151 (TICAM1, OR = 1.69, 95% CI, 0.99–2.91; homozygotes; Table 2), as well as for rs1061758 (IL11RA, OR = 1.29, 95% CI, 1.02–1.65; allelic model; Table 3). The involvement of these SNPs in increasing the risk of DTC was further suggested by the meta-analyses. In particular, an OR = 1.74 (95% CI, 1.28–2.37, q = 8.9 × 10−4; allelic model) for rs1126667, and OR = 1.24 (95% CI, 1.06–1.45, q = 0.01; allelic model) for rs2292151 was observed in the meta-analysis with Caucasians studies, and an OR = 1.39 (95% CI, 1.14–1.70, q= 0.01; allelic model) for 1061758 was observed in the meta-analysis with an Asian study (Tables 2 and 3).

SNPs within other cancer genes

Fifty-five SNPs in 28 other genes related to cancer were investigated in relation to DTC risk and 37 SNPs were associated according to the literature. Of them, SNPs within ATG16L1 and FTO showed a strong association in GWAS under the allelic model and rs1121980 (within FTO) remained associated also after FDR correction (Table 1). According to the present meta-analysis of the published results and our GWAS data on Caucasians, SNPs rs2241880 (ATG16L1, OR = 0.81, 95% CI, 0.70–0.93, q = 7.6 × 10−3; allelic model), rs11642841 (FTO, OR = 0.76, 95% CI, 0.67–0.87, q = 1.2 × 10−4; allelic model), rs1121980 (FTO, OR = 0.75, 95% CI, 0.66–0.86, q = 5.7 × 10−5; allelic model), rs8050136 (FTO, OR = 0.76, 95% CI, 0.67–0.86, q = 4.8 × 10−5; allelic model), rs9939609 (FTO, OR = 0.77, 95% CI, 0.67–0.88, q = 3.9 × 10−4; allelic model), rs7202116 (FTO, OR = 0.76, 95% CI, 0.66–0.87, q = 2.5 × 10−4; allelic model), rs7584828 (HDAC4, OR = 0.68, 95% CI, 0.54–0.84, q = 4.2 × 10−3; heterozygotes), rs2132572 (5′ region near IGFBP3, OR = 0.77, 95% CI, 0.61–0.96, only dominant model was available for meta-analysis), and rs17849071 (PIK3CA, OR = 0.64, 95% CI, 0.46–0.90, q = 0.04; heterozygotes) were associated with a reduced risk of DTC, whereas SNPs rs17817288 (FTO, OR = 1.32, 95% CI, 1.15–1.51, q = 1.6 × 10−4; allelic model) and rs6472462 (5′ region near SULF1, OR = 1.17, 95% CI, 1.03–1.33, q = 0.03; allelic model) were associated with increased risks (Table 2). When the meta-analyses were extended to other available populations, four more SNPs showed an evidence of association, although not significantly after FDR correction: rs2229765 (IGF1R, OR = 0.77, 95% CI, 0.61–0.98, q = 0.09; heterozygotes), rs2230396 (ITGB1, OR = 0.75, 95% CI, 0.58–0.98, q = 0.09; heterozygotes), rs17524488 (5′ region near OPN, OR = 0.81, 95% CI, 0.67–0.99, q = 0.09; heterozygotes), and rs699947 (5′ region near VEGFA, OR = 1.22, 95% CI, 1.05–1.41, q = 0.05; allelic model; Table 3).

SNPs previously studied in relation to DTC risks from genome-wide association studies or studies focused on specific intergenic regions

Genetic variants on 1p31.3, 2q35, 8p12, 9q22, and 14q13.3 were associated with DTC risk by using genome-wide approaches. Three LD blocks (defined by rs965513, rs7048394, and rs894673) on chromosome 9q22 near FOXE1 were associated with DTC risk so far. These SNPs also showed a strong association in the present GWAS, where the allelic Pass remained statistically significant also after FDR correction (Table 1). Moreover, these associations were strengthened in the meta-analyses on Caucasians in the allelic model, with OR = 1.85 (95% CI, 1.76–1.95, q < 10−20) for rs965513, OR = 1.51 (95% CI, 1.31–1.73, q = 2.3 × 10−8) for rs7048394 and OR = 1.51 (95% CI, 1.33–1.71, q = 8.3 × 10−10) for rs894673. Moreover, the present meta-analysis points rs334725 (1p13.3, OR = 1.32, 95% CI, 1.10–1.59, q = 5.1 × 10−3), rs966423 (2q35, OR = 1.27, 95% CI, 1.19–1.35, q = 1.3 × 10−12), rs2439302 (8p12, OR = 1.30, 95% CI, 1.23–1.39, q = 1.2 × 10−15), and rs944289 (14q13, OR = 1.25, 95% CI, 1.17–1.33, q = 0.02) as associated with the risk of DTC (Table 2).

Discussion

Current scientists' knowledge on DTC genetic risk factors is based on a series of association studies on genes involved in different cellular mechanisms that could lead to malignant transformation of thyroid cells. Typically these studies were performed according to candidate-gene approaches, and rarely the findings were replicated using similar samples in terms of ethnicity and thyroid carcinoma histological type. Furthermore, to date, only few GWASs were performed, and a small number of genomic loci were associated with the risk of the disease by using this approach.

In order to gain further insights into the role of SNPs previously associated with DTC, in the present work we carefully analyzed the results of our GWAS and we performed meta-analyses with the previous studies. The associations between DTC and well-established GWAS-identified SNPs, including rs965513, rs7048394, and rs894673 near FOXE1 (9q22), were replicated using our GWAS data. Furthermore, rs944289 near NKX2-1 (14q13.3), rs966423 within DIRC3 (2q35), rs334725 within NFIA (1p31.3), and rs2439302 within NRG1 (8p12) showed an evidence of association in the meta-analysis of the GWAS results and previous published data. The role of these loci in DTC etiology was already discussed in previous works and will be not discussed here.

Although in the present work most of the SNPs assayed in previously published hypothesis-driven studies were not associated with the risk of DTC, it is noteworthy to observe that several of them actually did associate. In particular, rs1799814 within CYP1A1 and rs1121980 within FTO were replicated on our GWAS data after the application of multiple testing corrections. The meta-analysis–based approach provided an evidence of association of several additional variants, including SNPs in the DNA repair gene HUS1 (rs2708906), cell-cycle regulation gene WDR3 (rs4658973), xenobiotic metabolism genes SOD1 (rs1041740, rs12626475) and UGT2B7 (rs3924194), the immune response and inflammation genes ALOX12 (rs1126667), TICAM1 (rs2292151), and IL11RA (rs1061758), as well as other cancer genes ATG16L1 (rs2241880), FTO (rs17817288, rs11642841, rs9939609), HDAC4 (rs7584828), IGFBP3 (rs2132572), PIK3CA (rs17849071), SULF1 (rs6472462), IGF1R (rs2229765), OPN (rs17524488), and VEGFA (rs699947). All these SNPs were previously investigated in hypothesis-driven studies, underlying the importance of CCASs also in the era of GWAS. In particular, we highlighted the role of rs17849071 and rs17524488, whose association was not significant in previous studies but became statistically significant after increasing the sample size with the present meta-analysis. Overall, our in-depth analysis showed that some a priori hypotheses formulated in previous studies were confirmed and could have realistic bases for shedding some lights in the etiology of DTC. Our GWAS had an adequate statistical power to detect small size effects (>85% of power for SNPs with MAF>0.05, relative risk of 1.4 and type I error α = 0.05), that is reinforced with the data already published through the meta-analysis. However, we cannot exclude that other SNPs could be associated with DTC but failed to replicate in the present study. For example, it is worth mentioning that 13 SNPs (Supplementary Table S3) were found associated with the risk of DTC in the meta-analysis of literature data alone, although all but rs965513 were not significant in the present GWAS. Ideally, all these SNPs should be replicated in a large and independent series of cases and controls to further confirm their involvement in DTC predisposition (24). In conclusion, our findings provide additional evidence that common genetic variants have a role in DTC initiation and/or progression. Further cutting-edge studies, as novel GWASs, next-generation sequencing analysis, fine-mapping or genome-wide interactions studies, are needed to characterize all the predisposing risk factors for DTC.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Authors' Contributions

Conception and design: R. Elisei, C. Romei, K. Hemminki, F. Gemignani, A. Försti, S. Landi

Development of methodology: G. Figlioli, A. Försti

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): G. Figlioli, R. Elisei, F. Bambi, B. Chen, A. Cristaudo, K. Hemminki, A. Försti

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): G. Figlioli, R. Elisei, O. Melaiu, B. Chen, A. Köhler, K. Hemminki, A. Försti

Writing, review, and/or revision of the manuscript: G. Figlioli, C. Romei, M. Cipollini, A. Cristaudo, F. Gemignani, A. Försti, S. Landi

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): G. Figlioli, A. Köhler

Study supervision: R. Elisei, K. Hemminki, F. Gemignani, S. Landi

Grant Support

This work was funded by the Istituto Toscano Tumori, grant system 2010.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

  • Received June 29, 2015.
  • Revision received January 22, 2016.
  • Accepted January 23, 2016.
  • ©2016 American Association for Cancer Research.

References

  1. 1.↵
    1. Kilfoy BA,
    2. Devesa SS,
    3. Ward MH,
    4. Zhang Y,
    5. Rosenberg PS,
    6. Holford TR,
    7. et al.
    Gender is an age-specific effect modifier for papillary cancers of the thyroid gland. Cancer Epidemiol Biomarkers Prev 2009;18:1092–100.
    OpenUrlAbstract/FREE Full Text
  2. 2.↵
    1. Aschebrook-Kilfoy B,
    2. Grogan RH,
    3. Ward MH,
    4. Kaplan E,
    5. Devesa SS
    . Follicular thyroid cancer incidence patterns in the United States, 1980–2009. Thyroid 2013;23:1015–21.
    OpenUrlPubMed
  3. 3.↵
    1. Yu GP,
    2. Li JC,
    3. Branovan D,
    4. McCormick S,
    5. Schantz SP
    . Thyroid cancer incidence and survival in the national cancer institute surveillance, epidemiology, and end results race/ethnicity groups. Thyroid 2010;20:465–73.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Steliarova-Foucher E,
    2. O'Callaghan M,
    3. Ferlay J,
    4. Masuyer E,
    5. Forman D,
    6. Comber H,
    7. et al.
    European Cancer Observatory: Cancer Incidence, Mortality, Prevalence and Survival in Europe. Version 1.0 (September 2012) European Network of Cancer Registries, International Agency for Research on Cancer. Available fromhttp://eco.iarc.fr, accessed on 03/03/2014.
  5. 5.↵
    1. Ferlay J,
    2. Soerjomataram I,
    3. Ervik M,
    4. Dikshit R,
    5. Eser S,
    6. Mathers C,
    7. et al.
    GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. Lyon, France: International Agency for Research on Cancer; 2013. Available from: http://globocan.iarc.fr, accessed on 03/03/2014.
  6. 6.↵
    1. Grande E,
    2. Diez JJ,
    3. Zafon C,
    4. Capdevila J
    . Thyroid cancer: molecular aspects and new therapeutic strategies. JThyroid Res 2012;2012:847108.
    OpenUrl
  7. 7.↵
    1. Jukkola A,
    2. Bloigu R,
    3. Ebeling T,
    4. Salmela P,
    5. Blanco G
    . Prognostic factors in differentiated thyroid carcinomas and their implications for current staging classifications. Endocr Relat Cancer 2004;11:571–9.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. DeLellis RA
    . Pathology and genetics of thyroid carcinoma. J Surg Oncol 2006;94:662–9.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Fagin JA
    . Genetics of papillary thyroid cancer initiation: implications for therapy. Trans Am Clin Climatol Assoc 2005;116:259–69.
    OpenUrlPubMed
  10. 10.↵
    1. Kimura ET,
    2. Nikiforova MN,
    3. Zhu Z,
    4. Knauf JA,
    5. Nikiforov YE,
    6. Fagin JA
    . High prevalence of BRAF mutations in thyroid cancer: genetic evidence for constitutive activation of the RET/PTC-RAS-BRAF signaling pathway in papillary thyroid carcinoma. Cancer Res 2003;63:1454–7.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Kondo T,
    2. Ezzat S,
    3. Asa SL
    . Pathogenetic mechanisms in thyroid follicular-cell neoplasia. Nat Rev Cancer 2006;6:292–306.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Malchoff CD,
    2. Malchoff DM
    . Familial nonmedullary thyroid carcinoma. Cancer Control 2006;13:106–10.
    OpenUrlPubMed
  13. 13.↵
    1. Healy DG
    . Case–control studies in the genomic era: a clinician's guide. Lancet Neurol 2006;5:701–7.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Landa I,
    2. Robledo M
    . Association studies in thyroid cancer susceptibility: are we on the right track? J Mol Endocrinol 2011;47:R43–58.
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    1. Gudmundsson J,
    2. Sulem P,
    3. Gudbjartsson DF,
    4. Jonasson JG,
    5. Masson G,
    6. He H,
    7. et al.
    Discovery of common variants associated with low TSH levels and thyroid cancer risk. Nat Genet 2012;44:319–22.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Gudmundsson J,
    2. Sulem P,
    3. Gudbjartsson DF,
    4. Jonasson JG,
    5. Sigurdsson A,
    6. Bergthorsson JT,
    7. et al.
    Common variants on 9q22.33 and 14q13.3 predispose to thyroid cancer in European populations. Nat Genet 2009;41:460–4.
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Takahashi M,
    2. Saenko VA,
    3. Rogounovitch TI,
    4. Kawaguchi T,
    5. Drozd VM,
    6. Takigawa-Imamura H,
    7. et al.
    The FOXE1 locus is a major genetic determinant for radiation-related thyroid carcinoma in Chernobyl. Hum Mol Genet 2010;19:2516–23.
    OpenUrlAbstract/FREE Full Text
  18. 18.↵
    1. Figlioli G,
    2. Chen B,
    3. Elisei R,
    4. Romei C,
    5. Campo C,
    6. Cipollini M,
    7. et al.
    Novel genetic variants in differentiated thyroid cancer and assessment of the cumulative risk. Sci Rep 2015;5:8922.
    OpenUrlPubMed
  19. 19.↵
    1. Figlioli G,
    2. Kohler A,
    3. Chen B,
    4. Elisei R,
    5. Romei C,
    6. Cipollini M,
    7. et al.
    Novel genome-wide association study-based candidate loci for differentiated thyroid cancer risk. J Clin Endocrinol Metab 2014:jc20141734.
  20. 20.↵
    1. Kohler A,
    2. Chen B,
    3. Gemignani F,
    4. Elisei R,
    5. Romei C,
    6. Figlioli G,
    7. et al.
    Genome-wide association study on differentiated thyroid cancer. J Clin Endocrinol Metab 2013;98:E1674–E81.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Purcell S,
    2. Neale B,
    3. Todd-Brown K,
    4. Thomas L,
    5. Ferreira MAR,
    6. Bender D,
    7. et al.
    PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007;8:559–75.
    OpenUrl
  22. 22.↵
    1. Abecasis GR,
    2. Auton A,
    3. Brooks LD,
    4. DePristo MA,
    5. Durbin RM,
    6. et al.
    1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012;49:56–65.
    OpenUrl
  23. 23.↵
    1. Benjamini Y,
    2. Drai D,
    3. Elmer G,
    4. Kafkafi N,
    5. Golani I
    . Controlling the false discovery rate in behavior genetics research. Behav Brain Res 2001;125:279–84.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Colhoun HM,
    2. McKeigue PM,
    3. Davey Smith G
    . Problems of reporting genetic associations with complex outcomes. Lancet 2003;361:865–72.
    OpenUrlCrossRefPubMed
  25. 25.
    1. Neta G,
    2. Brenner AV,
    3. Sturgis EM,
    4. Pfeiffer RM,
    5. Hutchinson AA,
    6. schebrook-Kilfoy B,
    7. et al.
    Common genetic variants related to genomic integrity and risk of papillary thyroid cancer. Carcinogenesis 2011;32:1231–7.
    OpenUrlAbstract/FREE Full Text
  26. 26.
    1. Xu L,
    2. Doan PC,
    3. Wei Q,
    4. Liu Y,
    5. Li G,
    6. Sturgis EM
    . Association of BRCA1 functional single nucleotide polymorphisms with risk of differentiated thyroid carcinoma. Thyroid 2012;22:35–43.
    OpenUrlCrossRefPubMed
  27. 27.
    1. Sigurdson AJ,
    2. Land CE,
    3. Bhatti P,
    4. Pineda M,
    5. Brenner A,
    6. Carr Z,
    7. et al.
    Thyroid nodules, polymorphic variants in DNA repair and RET-related genes, and interaction with ionizing radiation exposure from nuclear tests in Kazakhstan. Radiat Res 2009;171:77–88.
    OpenUrlCrossRefPubMed
  28. 28.
    1. Siraj AK,
    2. Al-Rasheed M,
    3. Ibrahim M,
    4. Siddiqui K,
    5. Al-Dayel F,
    6. Al-Sanea O,
    7. et al.
    RAD52 polymorphisms contribute to the development of papillary thyroid cancer susceptibility in Middle Eastern population. J Endocrinol Invest 2008;31:893–9.
    OpenUrlPubMed
  29. 29.
    1. Chiang FY,
    2. Wu CW,
    3. Hsiao PJ,
    4. Kuo WR,
    5. Lee KW,
    6. Lin JC,
    7. et al.
    Association between polymorphisms in DNA base excision repair genes XRCC1, APE1, and ADPRT and differentiated thyroid carcinoma. Clin Cancer Res 2008;14:5919–24.
    OpenUrlAbstract/FREE Full Text
  30. 30.
    1. Garcia-Quispes WA,
    2. Perez-Machado G,
    3. Akdi A,
    4. Pastor S,
    5. Galofre P,
    6. Biarnes F,
    7. et al.
    Association studies of OGG1, XRCC1, XRCC2 and XRCC3 polymorphisms with differentiated thyroid cancer. Mutat Res 2011;709–710:67–72.
    OpenUrl
  31. 31.
    1. Ho T,
    2. Li G,
    3. Lu J,
    4. Zhao C,
    5. Wei Q,
    6. Sturgis EM
    . Association of XRCC1 polymorphisms and risk of differentiated thyroid carcinoma: a case–control analysis. Thyroid 2009;19:129–35.
    OpenUrlCrossRefPubMed
  32. 32.
    1. Sturgis EM,
    2. Zhao C,
    3. Zheng R,
    4. Wei Q
    . Radiation response genotype and risk of differentiated thyroid cancer: a case–control analysis. Laryngoscope 2005;115:938–45.
    OpenUrlCrossRefPubMed
  33. 33.
    1. Rahimi M,
    2. Fayaz S,
    3. Fard-Esfahani A,
    4. Modarressi MH,
    5. Akrami SM,
    6. Fard-Esfahani P
    . The role of Ile3434Thr XRCC7 gene polymorphism in differentiated thyroid cancer risk in an Iranian population. Iran Biomed J 2012;16:218–22.
    OpenUrlPubMed
  34. 34.
    1. Eun YG,
    2. Hong IK,
    3. Kim SK,
    4. Park HK,
    5. Kwon S,
    6. Chung DH,
    7. et al.
    A Polymorphism (rs1801018, Thr7Thr) of BCL2 is associated with papillary thyroid cancer in Korean population. Clin Exp Otorhinolaryngol 2011;4:149–54.
    OpenUrlPubMed
  35. 35.
    1. Wang YX,
    2. Zhao L,
    3. Wang XY,
    4. Liu CM,
    5. Yu SG
    . Role of Caspase 8, Caspase 9 and Bcl-2 polymorphisms in papillary thyroid carcinoma risk in Han Chinese population. Med Oncol 2012;29:2445–51.
    OpenUrlPubMed
  36. 36.
    1. Zhang F,
    2. Xu L,
    3. Wei Q,
    4. Song X,
    5. Sturgis EM,
    6. Li G
    . Significance of MDM2 and P14 ARF polymorphisms in susceptibility to differentiated thyroid carcinoma. Surgery 2013;153:711–7.
    OpenUrlPubMed
  37. 37.
    1. Granja F,
    2. Morari J,
    3. Morari EC,
    4. Correa LA,
    5. Assumpcao LV,
    6. Ward LS
    . Proline homozygosity in codon 72 of p53 is a factor of susceptibility for thyroid cancer. Cancer Lett 2004;210:151–7.
    OpenUrlCrossRefPubMed
  38. 38.
    1. Baida A,
    2. Akdi M,
    3. Gonzalez-Flores E,
    4. Galofre P,
    5. Marcos R,
    6. Velazquez A
    . Strong association of chromosome 1p12 loci with thyroid cancer susceptibility. Cancer Epidemiol Biomarkers Prev 2008;17:1499–504.
    OpenUrlAbstract/FREE Full Text
  39. 39.
    1. Bufalo NE,
    2. Leite JL,
    3. Guilhen AC,
    4. Morari EC,
    5. Granja F,
    6. Assumpcao LV,
    7. et al.
    Smoking and susceptibility to thyroid cancer: an inverse association with CYP1A1 allelic variants. Endocr Relat Cancer 2006;13:1185–93.
    OpenUrlAbstract/FREE Full Text
  40. 40.
    1. Siraj AK,
    2. Ibrahim M,
    3. Al-Rasheed M,
    4. Abubaker J,
    5. Bu R,
    6. Siddiqui SU,
    7. et al.
    Polymorphisms of selected xenobiotic genes contribute to the development of papillary thyroid cancer susceptibility in Middle Eastern population. BMC Med Genet 2008;9:61.
    OpenUrlCrossRefPubMed
  41. 41.
    1. Schonfeld SJ,
    2. Neta G,
    3. Sturgis EM,
    4. Pfeiffer RM,
    5. Hutchinson AA,
    6. Xu L,
    7. et al.
    Common genetic variants in sex hormone pathway genes and papillary thyroid cancer risk. Thyroid 2012;22:151–6.
    OpenUrlCrossRefPubMed
  42. 42.
    1. Aschebrook-Kilfoy B,
    2. Neta G,
    3. Brenner AV,
    4. Hutchinson A,
    5. Pfeiffer RM,
    6. Sturgis EM,
    7. et al.
    Common genetic variants in metabolism and detoxification pathways and the risk of papillary thyroid cancer. Endocr Relat Cancer 2012;19:333–44.
    OpenUrlAbstract/FREE Full Text
  43. 43.
    1. Granja F,
    2. Morari J,
    3. Morari EC,
    4. Correa LA,
    5. Assumpcao LV,
    6. Ward LS
    . GST profiling may be useful in the screening for thyroid nodule malignancy. Cancer Lett 2004;209:129–37.
    OpenUrlCrossRefPubMed
  44. 44.
    1. Prasad VV,
    2. Wilkhoo H
    . Association of the functional polymorphism C677T in the methylenetetrahydrofolate reductase gene with colorectal, thyroid, breast, ovarian, and cervical cancers. Onkologie 2011;34:422–6.
    OpenUrlPubMed
  45. 45.
    1. Hernandez A,
    2. Xamena N,
    3. Surralles J,
    4. Galofre P,
    5. Velazquez A,
    6. Creus A,
    7. et al.
    Role of GST and NAT2 polymorphisms in thyroid cancer. J Endocrinol Invest 2008;31:1025–31.
    OpenUrlPubMed
  46. 46.
    1. Guilhen AC,
    2. Bufalo NE,
    3. Morari EC,
    4. Leite JL,
    5. Assumpcao LV,
    6. Tincani AJ,
    7. et al.
    Role of the N-acetyltransferase 2 detoxification system in thyroid cancer susceptibility. Clin Cancer Res 2009;15:406–12.
    OpenUrlAbstract/FREE Full Text
  47. 47.
    1. Akdi A,
    2. Perez G,
    3. Pastor S,
    4. Castell J,
    5. Biarnes J,
    6. Marcos R,
    7. et al.
    Common variants of the thyroglobulin gene are associated with differentiated thyroid cancer risk. Thyroid 2011;21:519–25.
    OpenUrlCrossRefPubMed
  48. 48.
    1. Pastor S,
    2. Akdi A,
    3. Gonzalez ER,
    4. Castell J,
    5. Biarnes J,
    6. Marcos R,
    7. et al.
    Common genetic variants in pituitary-thyroid axis genes and the risk of differentiated thyroid cancer. Endocr Connect 2012;1:68–77.
    OpenUrlAbstract/FREE Full Text
  49. 49.
    1. Cipollini M,
    2. Pastor S,
    3. Gemignani F,
    4. Castell J,
    5. Garritano S,
    6. Bonotti A,
    7. et al.
    TPO genetic variants and risk of differentiated thyroid carcinoma in two European populations. Int J Cancer 2013;133:2843–51.
    OpenUrlPubMed
  50. 50.
    1. Khan MS,
    2. Pandith AA,
    3. Ul HM,
    4. Iqbal M,
    5. Khan NP,
    6. Wani KA,
    7. et al.
    Lack of mutational events of RAS genes in sporadic thyroid cancer but high risk associated with HRAS T81C single nucleotide polymorphism (case–control study). Tumour Biol 2013;34:521–9.
    OpenUrlPubMed
  51. 51.
    1. Kim MJ,
    2. Kim SK,
    3. Park HJ,
    4. Chung DH,
    5. Park HK,
    6. Lee JS,
    7. et al.
    PDGFRA promoter polymorphisms are associated with the risk of papillary thyroid cancer. Mol Med Rep 2012;5:1267–70.
    OpenUrlPubMed
  52. 52.
    1. Ho T,
    2. Li G,
    3. Zhao C,
    4. Wei Q,
    5. Sturgis EM
    . RET polymorphisms and haplotypes and risk of differentiated thyroid cancer. Laryngoscope 2005;115:1035–41.
    OpenUrlCrossRefPubMed
  53. 53.
    1. Prasad VV,
    2. Padma K
    . Non-synonymous polymorphism (Gln261Arg) of 12-lipoxygenase in colorectal and thyroid cancers. Fam Cancer 2012;11:615–21.
    OpenUrlPubMed
  54. 54.
    1. Ban JY,
    2. Kim MK,
    3. Park SW,
    4. Kwon KH
    . Interleukin-1 beta polymorphisms are associated with lymph node metastasis in Korean patients with papillary thyroid carcinoma. Immunol Invest 2012;41:888–905.
    OpenUrlPubMed
  55. 55.
    1. Eun YG,
    2. Shin IH,
    3. Kim MJ,
    4. Chung JH,
    5. Song JY,
    6. Kwon KH
    . Associations between promoter polymorphism -106A/G of interleukin-11 receptor alpha and papillary thyroid cancer in Korean population. Surgery 2012;151:323–9.
    OpenUrlPubMed
  56. 56.
    1. Brenner AV,
    2. Neta G,
    3. Sturgis EM,
    4. Pfeiffer RM,
    5. Hutchinson A,
    6. Yeager M,
    7. et al.
    Common single nucleotide polymorphisms in genes related to immune function and risk of papillary thyroid cancer. PLoS One 2013;8:e57243.
    OpenUrlCrossRefPubMed
  57. 57.
    1. Huijbers A,
    2. Plantinga TS,
    3. Joosten LA,
    4. Aben KK,
    5. Gudmundsson J,
    6. den HM,
    7. et al.
    The effect of the ATG16L1 Thr300Ala polymorphism on susceptibility and outcome of patients with epithelial cell-derived thyroid carcinoma. Endocr Relat Cancer 2012;19:L15–L8.
    OpenUrlFREE Full Text
  58. 58.
    1. Kim YO,
    2. Hong IK,
    3. Eun YG,
    4. Nah SS,
    5. Lee S,
    6. Heo SH,
    7. et al.
    Polymorphisms in bone morphogenetic protein 3 and the risk of papillary thyroid cancer. Oncol Lett 2013;5:336–40.
    OpenUrlPubMed
  59. 59.
    1. Wang YX,
    2. Zhao L,
    3. Wang XY,
    4. Liu CM,
    5. Yu SG
    . Association between E-cadherin (CDH1) polymorphisms and papillary thyroid carcinoma risk in Han Chinese population. Endocrine 2012;41:526–31.
    OpenUrlPubMed
  60. 60.
    1. Park HJ,
    2. Choe BK,
    3. Kim SK,
    4. Park HK,
    5. Kim JW,
    6. Chung JH,
    7. et al.
    Association between collagen type XI alpha1 gene polymorphisms and papillary thyroid cancer in a Korean population. Exp Ther Med 2011;2:1111–6.
    OpenUrlPubMed
  61. 61.
    1. Rebai M,
    2. Kallel I,
    3. Charfeddine S,
    4. Hamza F,
    5. Guermazi F,
    6. Rebai A
    . Association of polymorphisms in estrogen and thyroid hormone receptors with thyroid cancer risk. J Recept Signal Transduct Res 2009;29:113–8.
    OpenUrlCrossRefPubMed
  62. 62.
    1. Kitahara CM,
    2. Neta G,
    3. Pfeiffer RM,
    4. Kwon D,
    5. Xu L,
    6. Freedman ND,
    7. et al.
    Common obesity-related genetic variants and papillary thyroid cancer risk. Cancer Epidemiol Biomarkers Prev 2012;21:2268–71.
    OpenUrlAbstract/FREE Full Text
  63. 63.
    1. Sheu SY,
    2. Handke S,
    3. Brocker-Preuss M,
    4. Gorges R,
    5. Frey UH,
    6. Ensinger C,
    7. et al.
    The C allele of the GNB3 C825T polymorphism of the G protein beta3-subunit is associated with an increased risk for the development of oncocytic thyroid tumours. JPathol 2007;211:60–6.
    OpenUrlPubMed
  64. 64.
    1. Rebai M,
    2. Kallel I,
    3. Hamza F,
    4. Charfeddine S,
    5. Kaffel R,
    6. Guermazi F,
    7. et al.
    Association of EGFR and HER2 polymorphisms with risk and clinical features of thyroid cancer. Genet Test Mol Biomarkers 2009;13:779–84.
    OpenUrlPubMed
  65. 65.
    1. Cho SH,
    2. Kim SK,
    3. Kwon E,
    4. Park HJ,
    5. Kwon KH,
    6. Chung JH
    . Polymorphism of IGF1R is associated with papillary thyroid carcinoma in a Korean population. JInterferon Cytokine Res 2012;32:401–6.
    OpenUrl
  66. 66.
    1. Xu L,
    2. Mugartegui L,
    3. Li G,
    4. Sarlis NJ,
    5. Wei Q,
    6. Zafereo ME,
    7. et al.
    Functional polymorphisms in the insulin-like binding protein-3 gene may modulate susceptibility to differentiated thyroid carcinoma in Caucasian Americans. Mol Carcinog 2012;51 Suppl 1:E158–E67.
    OpenUrlPubMed
  67. 67.
    1. Kim SK,
    2. Kim DK,
    3. Oh IH,
    4. Song JY,
    5. Kwon KH,
    6. Choe BK,
    7. et al.
    A missense polymorphism (rs11895564, Ala380Thr) of integrin alpha 6 is associated with the development and progression of papillary thyroid carcinoma in Korean population. JKorean Surg Soc 2011;81:308–15.
    OpenUrl
  68. 68.
    1. Eun YG,
    2. Kim SK,
    3. Chung JH,
    4. Kwon KH
    . Association study of integrins beta 1 and beta 2 gene polymorphism and papillary thyroid cancer. Am J Surg 2013;205:631–5.
    OpenUrlCrossRefPubMed
  69. 69.
    1. Ozdemir S,
    2. Uludag A,
    3. Silan F,
    4. Atik SY,
    5. Turgut B,
    6. Ozdemir O
    . Possible roles of the xenobiotic transporter P-glycoproteins encoded by the MDR1 3435 C>T gene polymorphism in differentiated thyroid cancers. Asian Pac J Cancer Prev 2013;14:3213–7.
    OpenUrlPubMed
  70. 70.
    1. Mu G,
    2. Wang H,
    3. Cai Z,
    4. Ji H
    . OPN -443C>T genetic polymorphism and tumor OPN expression are associated with the risk and clinical features of papillary thyroid cancer in a Chinese cohort. Cell Physiol Biochem 2013;32:171–9.
    OpenUrlPubMed
  71. 71.
    1. Iuliano R,
    2. Palmieri D,
    3. He H,
    4. Iervolino A,
    5. Borbone E,
    6. Pallante P,
    7. et al.
    Role of PTPRJ genotype in papillary thyroid carcinoma risk. Endocr Relat Cancer 2010;17:1001–6.
    OpenUrlAbstract/FREE Full Text
  72. 72.
    1. Hsiao PJ,
    2. Lu MY,
    3. Chiang FY,
    4. Shin SJ,
    5. Tai YD,
    6. Juo SH
    . Vascular endothelial growth factor gene polymorphisms in thyroid cancer. JEndocrinol 2007;195:265–70.
    OpenUrlAbstract/FREE Full Text
  73. 73.
    1. Cancemi L,
    2. Romei C,
    3. Bertocchi S,
    4. Tarrini G,
    5. Spitaleri I,
    6. Cipollini M,
    7. et al.
    Evidences that the polymorphism Pro-282-Ala within the tumor suppressor gene WWOX is a new risk factor for differentiated thyroid carcinoma. Int J Cancer 2011;129:2816–24.
    OpenUrlCrossRefPubMed
  74. 74.
    1. Jazdzewski K,
    2. Murray EL,
    3. Franssila K,
    4. Jarzab B,
    5. Schoenberg DR,
    6. de la CA
    . Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A 2008;105:7269–74.
    OpenUrlAbstract/FREE Full Text
  75. 75.
    1. Jones AM,
    2. Howarth KM,
    3. Martin L,
    4. Gorman M,
    5. Mihai R,
    6. Moss L,
    7. et al.
    Thyroid cancer susceptibility polymorphisms: confirmation of loci on chromosomes 9q22 and 14q13, validation of a recessive 8q24 locus and failure to replicate a locus on 5q24. J Med Genet 2012;49:158–63.
    OpenUrlAbstract/FREE Full Text
  76. 76.
    1. Landa I,
    2. Ruiz-Llorente S,
    3. Montero-Conde C,
    4. Inglada-Perez L,
    5. Schiavi F,
    6. Leskela S,
    7. et al.
    The variant rs1867277 in FOXE1 gene confers thyroid cancer susceptibility through the recruitment of USF1/USF2 transcription factors. PLoS Genet 2009;5:e1000637.
    OpenUrlCrossRefPubMed
  77. 77.
    1. Akulevich NM,
    2. Saenko VA,
    3. Rogounovitch TI,
    4. Drozd VM,
    5. Lushnikov EF,
    6. Ivanov VK,
    7. et al.
    Polymorphisms of DNA damage response genes in radiation-related and sporadic papillary thyroid carcinoma. Endocr Relat Cancer 2009;16:491–503.
    OpenUrlAbstract/FREE Full Text
  78. 78.
    1. Fard-Esfahani P,
    2. Fard-Esfahani A,
    3. Fayaz S,
    4. Ghanbarzadeh B,
    5. Saidi P,
    6. Mohabati R,
    7. et al.
    Association of Arg194Trp, Arg280His and Arg399Gln polymorphisms in X-ray repair cross-complementing group 1 gene and risk of differentiated thyroid carcinoma in Iran. Iran Biomed J 2011;15:73–8.
    OpenUrlPubMed
  79. 79.
    1. Santos LS,
    2. Branco SC,
    3. Silva SN,
    4. Azevedo AP,
    5. Gil OM,
    6. Manita I,
    7. et al.
    Polymorphisms in base excision repair genes and thyroid cancer risk. Oncol Rep 2012;28:1859–68.
    OpenUrlPubMed
  80. 80.
    1. Akdi A,
    2. Gimenez EM,
    3. Garcia-Quispes W,
    4. Pastor S,
    5. Castell J,
    6. Biarnes J,
    7. et al.
    WDR3 gene haplotype is associated with thyroid cancer risk in a Spanish population. Thyroid 2010;20:803–9.
    OpenUrlPubMed
  81. 81.
    1. Xing JC,
    2. Tufano RP,
    3. Murugan AK,
    4. Liu D,
    5. Wand G,
    6. Ladenson PW,
    7. et al.
    Single nucleotide polymorphism rs17849071 G/T in the PIK3CA gene is inversely associated with follicular thyroid cancer and PIK3CA amplification. PLoS One 2012;7:e49192.
    OpenUrlPubMed
  82. 82.
    1. Liyanarachchi S,
    2. Wojcicka A,
    3. Li W,
    4. Czetwertynska M,
    5. Stachlewska E,
    6. Nagy R,
    7. et al.
    Cumulative risk impact of five genetic variants associated with papillary thyroid carcinoma. Thyroid 2013; 23:1532–40.
    OpenUrlCrossRefPubMed
  83. 83.
    1. Wei WJ,
    2. Wang YL,
    3. Li DS,
    4. Wang Y,
    5. Wang XF,
    6. Zhu YX,
    7. et al.
    Association between the rs2910164 polymorphism in pre-Mir-146a sequence and thyroid carcinogenesis. PLoS One 2013;8:e56638.
    OpenUrlCrossRefPubMed
  84. 84.
    1. Wang YL,
    2. Feng SH,
    3. Guo SC,
    4. Wei WJ,
    5. Li DS,
    6. Wang Y,
    7. et al.
    Confirmation of papillary thyroid cancer susceptibility loci identified by genome-wide association studies of chromosomes 14q13, 9q22, 2q35 and 8p12 in a Chinese population. J Med Genet 2013;50:689–95.
    OpenUrlAbstract/FREE Full Text
  85. 85.
    1. Tomaz RA,
    2. Sousa I,
    3. Silva JG,
    4. Santos C,
    5. Teixeira MR,
    6. Leite V,
    7. et al.
    FOXE1 polymorphisms are associated with familial and sporadic nonmedullary thyroid cancer susceptibility. Clin Endocrinol(Oxf) 2012;77:926–33.
    OpenUrlCrossRefPubMed
  86. 86.
    1. Lin JC,
    2. Kuo WR,
    3. Chiang FY,
    4. Hsiao PJ,
    5. Lee KW,
    6. Wu CW,
    7. et al.
    Glutathione peroxidase 3 gene polymorphisms and risk of differentiated thyroid cancer. Surgery 2009;145:508–13.
    OpenUrlCrossRefPubMed
  87. 87.
    1. Kim SK,
    2. Park HJ,
    3. Hong IK,
    4. Chung JH,
    5. Eun YG
    . A missense polymorphism (rs11466653, Met326Thr) of toll-like receptor 10 (TLR10) is associated with tumor size of papillary thyroid carcinoma in the Korean population. Endocrine 2013;43:161–9.
    OpenUrlCrossRefPubMed
  88. 88.
    1. Han SA,
    2. Song JY,
    3. Min SY,
    4. Park WS,
    5. Kim MJ,
    6. Chung JH,
    7. et al.
    A genetic association analysis of polymorphisms, rs2282695 and rs12373539, in the FOSB gene and papillary thyroid cancer. Exp Ther Med 2012;4:519–23.
    OpenUrlPubMed
PreviousNext
Back to top
Cancer Epidemiology Biomarkers & Prevention: 25 (4)
April 2016
Volume 25, Issue 4
  • Table of Contents
  • Table of Contents (PDF)

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Cancer Epidemiology, Biomarkers & Prevention article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
A Comprehensive Meta-analysis of Case–Control Association Studies to Evaluate Polymorphisms Associated with the Risk of Differentiated Thyroid Carcinoma
(Your Name) has forwarded a page to you from Cancer Epidemiology, Biomarkers & Prevention
(Your Name) thought you would be interested in this article in Cancer Epidemiology, Biomarkers & Prevention.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
A Comprehensive Meta-analysis of Case–Control Association Studies to Evaluate Polymorphisms Associated with the Risk of Differentiated Thyroid Carcinoma
Gisella Figlioli, Rossella Elisei, Cristina Romei, Ombretta Melaiu, Monica Cipollini, Franco Bambi, Bowang Chen, Aleksandra Köhler, Alfonso Cristaudo, Kari Hemminki, Federica Gemignani, Asta Försti and Stefano Landi
Cancer Epidemiol Biomarkers Prev April 1 2016 (25) (4) 700-713; DOI: 10.1158/1055-9965.EPI-15-0652

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
A Comprehensive Meta-analysis of Case–Control Association Studies to Evaluate Polymorphisms Associated with the Risk of Differentiated Thyroid Carcinoma
Gisella Figlioli, Rossella Elisei, Cristina Romei, Ombretta Melaiu, Monica Cipollini, Franco Bambi, Bowang Chen, Aleksandra Köhler, Alfonso Cristaudo, Kari Hemminki, Federica Gemignani, Asta Försti and Stefano Landi
Cancer Epidemiol Biomarkers Prev April 1 2016 (25) (4) 700-713; DOI: 10.1158/1055-9965.EPI-15-0652
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosure of Potential Conflicts of Interest
    • Authors' Contributions
    • Grant Support
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • Estimated Effects of Multi-Cancer Screening
  • Gene–Environment Interaction Portfolio Analysis
  • Risk Prediction for Renal Cell Carcinoma
Show more Research Articles
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook   Twitter   LinkedIn   YouTube   RSS

Articles

  • Online First
  • Current Issue
  • Past Issues

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians

About Cancer Epidemiology, Biomarkers & Prevention

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2021 by the American Association for Cancer Research.

Cancer Epidemiology, Biomarkers & Prevention
eISSN: 1538-7755
ISSN: 1055-9965

Advertisement