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Clinical Trial
. 2021 Oct 30;22(21):11794.
doi: 10.3390/ijms222111794.

Modifier Role of Common RET Variants in Sporadic Medullary Thyroid Carcinoma

Affiliations
Clinical Trial

Modifier Role of Common RET Variants in Sporadic Medullary Thyroid Carcinoma

Anna Skalniak et al. Int J Mol Sci. .

Abstract

Background: Although the disease-causing effect of pathogenic variants in the gene RET has been unambiguously identified, there is a lack of consensus regarding the possible impact of common variants in this gene. Our study aimed to test whether variants in exons 10, 11, and 13-16 that are commonly detected during routine diagnostic testing might have any modifying effect on MTC. Methods: In sporadic MTC patients with no pathogenic variants but with or without common variants in RET, the following variants were evaluated: rs1799939 (p.G691S), rs1800861 (p.L769=), rs1800862 (p.S836=), rs2472737 in intron 14, and rs1800863 (p.S904=). Results: After Bonferroni correction, none of the variants were statistically significantly associated with disease outcome when analysed independently. The MTC group was divided into three genetically different clusters by unsupervised k-means clustering. Those clusters differed significantly in the age at diagnosis. A trend towards the association of given clusters with metabolic disorders and with remission state was identified. Conclusions: Although common variants in RET are not responsible for the risk of MTC, their analysis might turn out useful in the prediction of a patient's clinical outcome. Importantly, this analysis would come with no additional cost in laboratories with a diagnostic procedure based on exon sequencing.

Keywords: RET variants; medullary thyroid carcinoma; modifier.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Graphic representation of clusters discriminated by unsupervised k−means clusterisation.
Figure 2
Figure 2
Graphic representation of age distributions in the obtained clusters.
Figure 3
Figure 3
Correspondence analysis plot−relationship between clusters and remission state.

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