Nomograms Based on Serum N-glycome for Diagnosis of Papillary Thyroid Microcarcinoma and Prediction of Lymph Node Metastasis

Curr Oncol. 2022 Aug 23;29(9):6018-6034. doi: 10.3390/curroncol29090474.

Abstract

Non-invasive biomarkers for the diagnosis and prognosis of papillary thyroid microcarcinoma (PTMC) are still urgently needed. We aimed to characterize the N-glycome of PTMC, and establish nomograms for the diagnosis of PTMC and the prediction of lymph node metastasis (LNM). N-glycome of PTMC (LNM vs. non-LNM, capsular invasion (CI) vs. non-CI (NCI)) and matched healthy controls (HC) were quantitatively analyzed based on mass spectrometry. N-glycan traits associated with PTMC/LNM were used to create binomial logistic regression models and were visualized as nomograms. We found serum N-glycome differed between PTMC and HC in high-mannose, complexity, fucosylation, and bisection, of which, four N-glycan traits (TM, CA1, CA4, and A2Fa) were significantly associated with PTMC. The nomogram based on four traits achieved good performance for the identification of PTMC. Two N-glycan traits (CA4 and A2F0S0G) showed strong associations with LNM. The nomogram based on two traits showed relatively good performance in predicting LNM. We also found differences between CI and NCI in several N-glycan traits, which were not the same as that associated with LNM. This study reported serum N-glycosylation signatures of PTMC for the first time. Nomograms constructed from aberrant glycans could be useful tools for PTMC diagnosis and stratification.

Keywords: capsular invasion; lymph node metastasis; nomogram; papillary thyroid microcarcinoma; serum glycomics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinoma, Papillary* / pathology
  • Humans
  • Lymphatic Metastasis
  • Mannose
  • Nomograms*
  • Thyroid Neoplasms

Substances

  • Mannose

Supplementary concepts

  • Papillary Thyroid Microcarcinoma

Grants and funding

This research was funded by the National Natural Science Foundation of China, grant number 32071436, 82172727, 81572459, and 31901041; Beijing Municipal Natural Science Foundation, grant number 7202164 and 7222127; and the CAMS Innovation Fund for Medical Sciences (CIFMS), grant number 2021-I2M-1-002.