One of the challenges in the area of diagnostics of human thyroid cancer is a preoperative diagnosis of thyroid nodules with indeterminate cytology. Herein, we report an untargeted metabolomics analysis to identify circulating thyroid nodule metabolic signatures, to find new novel metabolic biomarkers. Untargeted gas chromatographyquadrupole-mass spectrometry was used to ascertain the specific plasma metabolic changes of thyroid nodule patients, which consisted of papillary thyroid carcinoma (PTC; n = 19), and multinodular goiter (MNG; n = 16), as compared to healthy subjects (n = 20). Diagnostic models were constructed using multivariate analyses such as principal component analysis, orthogonal partial least squares-discriminant analysis, and univariate analysis including One-way ANOVA and volcano plot by MetaboAnalyst and SIMCA software. Because of the multiple-testing issue, false discovery rate p-values were also computed for these functions. A total of 60 structurally annotated metabolites were subjected to statistical analysis. A combination of univariate and multivariate statistical analyses revealed a panel of metabolites responsible for the discrimination between thyroid nodules and healthy subjects, with variable importance in the projection (VIP) value greater than 0.8 and p-value less than 0.05. Significantly altered metabolites between thyroid nodules versus healthy persons are those associated with amino acids metabolism, the tricarboxylic acid cycle, fatty acids, and purine and pyrimidine metabolism, including cysteine, cystine, glutamic acid, α-ketoglutarate, 3-hydroxybutyric acid, adenosine-5-monophosphate, and uracil, respectively. Further, sucrose metabolism differed profoundly between thyroid nodule patients and healthy subjects. Moreover, according to the receiver operating characteristic (ROC) curve analysis, sucrose could discriminate PTC from MNG (area under ROC curve value = 0.92). This study enhanced our understanding of the distinct metabolic pathways associated with thyroid nodules, which enabled us to distinguish between patients and healthy subjects. In addition, our study showed extensive

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) in thyroid tumors require accurate data normalization, however, there are no sufficient studies addressing the suitable reference genes for gene expression analysis in malignant and normal thyroid tissue specimens. The purpose of this study was to identify valid internal control genes for normalization of relative qRT-PCR studies in human papillary thyroid carcinoma tissue samples. The expression characteristics of 12 candidate reference genes (GAPDH, ACTB, HPRT1, TBP, B2M, PPIA, 18SrRNA, HMBS, GUSB, PGK1, RPLP0, and PGM1) were assessed by qRT-PCR in 45 thyroid tissue samples (15 papillary thyroid carcinoma, 15 paired normal tissues and 15 multinodular goiters). These twelve candidate reference genes were selected by a systematic literature search. GeNorm, NormFinder, and BestKeeper statistical algorithms were applied to determine the most stable reference genes. The three algorithms were in agreement in identifying GUSB and HPRT1 as the most stably expressed genes in all thyroid tumors investigated. According to the NormFinder software, the pair of genes including ‘GUSB and HPRT1’ or ‘GUSB and HMBS’ or ‘GUSB and PGM1’ were the best combinations for selection of pair reference genes. The optimal number of genes required for reliable normalization of qPCR data in thyroid tissues would be three according to calculations made by GeNorm algorithm. These results suggest that GUSB and HPRT1 are promising reference genes for normalization of relative qRT-PCR studies in papillary thyroid carcinoma.