Metabolite analysis-aided diagnosis of papillary thyroid cancer

in Endocrine-Related Cancer
Correspondence should be addressed to H Hou or A Wang or Q Hu: qsfctc@163.com or wangan@aiofm.ac.cn or huqy1965@163.com
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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.

Downloadable materials

  • Figure S1. ESI product ion mass spectra of the precursor ions of biomarkers (left) and total ion chromatogram (right): BHB (A), DHA (B), 1-MedA (C), PDG (D), NAM (E), Xan (F).
  • Table S1. Chromatographic Conditions of RP-HUPLC and HILIC-UHPLC in positive and negative mode.

 

      Society for Endocrinology

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    Orthogonal projections to latent structure-discriminant analysis (OPLS-DA) models (green for control and red for PTC): (A1) serum HPLC-ESI(+)-QTOF-MS, (A2) urinary HPLC-ESI(+)-QTOF-MS, (B1) serum HPLC-ESI(-)-QTOF-MS, (B2) urinary HPLC-ESI(-)-QTOF-MS, (C1) serum HILIC-ESI(+)-QTOF-MS. (C2) urinary HILIC-ESI(+)-QTOF-MS, (D1) serum HILIC-ESI(-)-QTOF-MS, (D2) urinary HILIC-ESI(-)-QTOF-MS, (E1) serum GC-MS. A full colour version of this figure is available at https://doi.org/10.1530/ERC-19-0344.

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    Heat map of identified differential metabolites screened by untargeted metabolomics in discovery set samples. A full colour version of this figure is available at https://doi.org/10.1530/ERC-19-0344.

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    Score scatter plots of principal component analysis (PCA) based on 36 differential metabolites: QC samples were closely assembled in the center. A full colour version of this figure is available at https://doi.org/10.1530/ERC-19-0344.

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    Differential biomarker in serum or urine of validation set samples: (A) serum BHB levels in female PTC patients, BTN patients and Healthy; (B) serum DHA levels in female PTC patients, BTN patients and Healthy; (C) serum 1-MedA levels in female PTC patients, BTN patients and Healthy; (D) serum PdG levels in female PTC patients, BTN patients and Healthy; (E) urine NAM/Cre levels in female PTC patients, BTN patients and Healthy; (F) urine xanthosine levels in female PTC patients, BTN patients and Healthy; (G) serum BHB levels in whole PTC patients, BTN patients and Healthy; (H) serum DHA levels in whole PTC patients, BTN patients and Healthy; (I) serum 1-MedA levels in whole PTC patients, BTN patients and Healthy; (J) serum PdG levels in whole PTC patients, BTN patients and Healthy; (K) urine NAM/Cre levels in whole PTC patients, BTN patients and Healthy; (L) urine xanthosine levels in whole PTC patients, BTN patients and Healthy; (I) Spearman’s rank correlation of BHB with thyroid status; (J) Spearman’s rank correlation of DHA with thyroid status; (K) Spearman’s rank correlation of 1-MedA with thyroid status; (L) Spearman’s rank correlation of PdG with thyroid status; (M) Spearman’s rank correlation of NAM with thyroid status; (N) Spearman’s rank correlation of Xan with thyroid status; (thyroid status: HC, BTN and PTC were defined as 1, 2 and 3, respectively.) (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). A full colour version of this figure is available at https://doi.org/10.1530/ERC-19-0344.

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    (A) Risk score and probability for female of non-cancer with logistic regression model; (B) Risk score and probability for female PTC with logistic regression model; (C) ROC curve plotted with false-positive rate (FPR) and true-positive rate (TPR) of logistic regression probability for female PTC classification. (D) Risk score and probability for non-cancer in female + male group with logistic regression model; (E) risk score and probability for whole PTC in logistic regression model; (F) ROC curve plotted with FPR and TPR of logistic regression probability for female + male PTC classification. A full colour version of this figure is available at https://doi.org/10.1530/ERC-19-0344.

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