Thyroid cancer is the most frequent endocrine tumor with a growing incidence worldwide. However, common diagnostic strategy for thyroid cancer classification is hardly to make a proper diagnosis in some cases. To assist classical approach, this study used metabolomics to screen and validate biomarkers from serum and urinary for papillary thyroid cancer (PTC). Overall, 124 untreated PTC, 76 untreated benign thyroid nodule (BTN), and 116 healthy control (HC) were collected in this study. Thirty-six differential metabolites were screened from non-targeted metabolomics with a discovery sample set in comparison with HC and BTN. Serum β-hydroxybutyrate (BHB), docosahexaenoic acid (DHA), 1-methyladenosine (1-MedA), pregnanediol-3-glucuronide (PdG), urinary nicotinic acid mononucleotide (NAM) and xanthosine (Xan) were validated to be significantly differential by targeted metabolomics in validation set. The logistic regression model incorporating six biomarkers had excellent discrimination from receiver-operating characteristics (ROC) analysis, with area under the receiver-operating characteristic curve (AUC) of 0.943 (95% CI 0.902 to 0.983) and 0.952 (95% CI 0.921 to 0.983) for female alone and female + male samples, respectively. The prediction accuracy and false-negative rate in the real setting of one PTC to ten suspicious nodules were 84.7 and 17.7% with the threshold at probablity of 0.5. Results of a double-blind study for PTC and BTN had true positive value of 100% and true negative value of 91.7%. To conclude, BHB, DHA, 1-MedA, PdG, NAM and Xan are suitable biomarkers for PTC, and logistic regression models with the six biomarkers can be potentially used as clinical diagnosis.
Keywords: biomarker; double-blind; logistic regression model; metabolomics; papillary thyroid carcinoma.