Stratification of follicular thyroid tumours using data-independent acquisition proteomics and a comprehensive thyroid tissue spectral library

Mol Oncol. 2022 Apr;16(8):1611-1624. doi: 10.1002/1878-0261.13198. Epub 2022 Mar 12.

Abstract

Thyroid nodules occur in about 60% of the population. A major challenge in thyroid nodule diagnosis is to distinguish between follicular adenoma (FA) and carcinoma (FTC). Here, we present a comprehensive thyroid spectral library covering five types of thyroid tissues. This library includes 121 960 peptides and 9941 protein groups. This spectral library can be used to quantify up to 7863 proteins from thyroid tissues, and can also be used to develop parallel reaction monitoring (PRM) assays for targeted protein quantification. Next, to stratify follicular thyroid tumours, we compared the proteomes of 24 FA and 22 FTC samples, and identified 204 differentially expressed proteins (DEPs). Our data suggest altered ferroptosis pathways in malignant follicular carcinoma. In all, 31 selected proteins effectively distinguished follicular tumours. Of those DEPs, nine proteins were further verified by PRM in an independent cohort of 18 FA and 19 FTC. Together, we present a comprehensive spectral library for DIA and targeted proteomics analysis of thyroid tissue specimens, and identified nine proteins that could potentially distinguish FA and FTC.

Keywords: data-independent acquisition; mass spectrometry; proteomics; spectral library; thyroid nodules.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenocarcinoma, Follicular* / diagnosis
  • Adenocarcinoma, Follicular* / metabolism
  • Adenocarcinoma, Follicular* / pathology
  • Adenoma* / diagnosis
  • Carcinoma*
  • Humans
  • Proteomics
  • Thyroid Neoplasms* / metabolism