Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach

Abstract

To identify a peculiar genetic combination predisposing to diffe rentiated 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 individual s 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 fif th quintile had a seven-fold increased risk of DTC than those in the first. Bayesian inf erence 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 on ly 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.

Keywords

• differentiated thyroid cancer
• machine learning
• single nucleotide polymorphism