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000182090 041__ $$aEnglish
000182090 082__ $$a610
000182090 1001_ $$aBrigante, Giulia$$b0
000182090 245__ $$aGenetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach.
000182090 260__ $$aBasel$$bKarger$$c2022
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000182090 520__ $$aTo identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics and a machine learning (ML) classifier to describe cases and controls in three different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3 (404 DTC, 392 controls) were genotyped. Associations of 171 SNPs reported to predispose to DTC in candidate studies were extracted from the GWAS of dataset 1, followed by replication of SNPs associated with DTC risk (P < 0.05) in dataset 2. The reliability of the identified SNPs was confirmed by PRS and Bayesian statistics after merging the three datasets. SNPs were used to describe the case/control state of individuals by ML classifier. Starting from 171 SNPs associated with DTC, 15 were positive in both datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a seven-fold increased risk of DTC than those in the first. Bayesian inference confirmed that the selected 15 SNPs differentiate cases from controls. Results were corroborated by ML, finding a maximum AUC of about 0.7. A restricted selection of only 15 DTC-associated SNPs is able to describe the inner genetic structure of Italian individuals, and ML allows a fair prediction of case or control status based solely on the individual genetic background.
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000182090 650_7 $$2Other$$adifferentiated thyroid cancer
000182090 650_7 $$2Other$$amachine learning
000182090 650_7 $$2Other$$asingle nucleotide polymorphism
000182090 7001_ $$aLazzaretti, Clara$$b1
000182090 7001_ $$aParadiso, Elia$$b2
000182090 7001_ $$aNuzzo, Federico$$b3
000182090 7001_ $$aSitti, Martina$$b4
000182090 7001_ $$aTüttelmann, Frank$$b5
000182090 7001_ $$aMoretti, Gabriele$$b6
000182090 7001_ $$aSilvestri, Roberto$$b7
000182090 7001_ $$aGemignani, Federica$$b8
000182090 7001_ $$0P:(DE-He78)f26164c08f2f14abcf31e52e13ee3696$$aFörsti, Asta$$b9$$udkfz
000182090 7001_ $$0P:(DE-He78)19b0ec1cea271419d9fa8680e6ed6865$$aHemminki, Kari$$b10$$udkfz
000182090 7001_ $$aElisei, Rossella$$b11
000182090 7001_ $$aRomei, Cristina$$b12
000182090 7001_ $$aZizzi, Eric Adriano$$b13
000182090 7001_ $$aDeriu, Marco Agostino$$b14
000182090 7001_ $$aSimoni, Manuela$$b15
000182090 7001_ $$00000-0001-8364-6357$$aLandi, Stefano$$b16
000182090 7001_ $$00000-0001-5571-392X$$aCasarini, Livio$$b17
000182090 773__ $$0PERI:(DE-600)2659767-6$$a10.1530/ETJ-22-0058$$gVol. 11, no. 5, p. e220058$$n5$$pe220058$$tEuropean thyroid journal$$v11$$x2235-0640$$y2022
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