Application of Metabolomics in Thyroid Cancer Research

Overview

Journal Int J Endocrinol

Publisher Wiley

Specialty Endocrinology

Date 2015 May 15

PMID 25972898

Citations 26

Authors

Anna Wojakowska

Mykola Chekan

Piotr Widlak

Monika Pietrowska

Affiliations

Soon will be listed here.

Abstract

Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput "omics" techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis.

Citing Articles

In Search of Relevant Urinary Biomarkers for Thyroid Papillary Carcinoma and Benign Thyroid Nodule Differentiation, Targeting Metabolic Profiles and Pathways via UHPLC-QTOF-ESI-MS Analysis.

Berinde G, Socaciu A, Socaciu M, Petre G, Rajnoveanu A, Barsan M Diagnostics (Basel). 2024; 14(21).

PMID: 39518388 PMC: 11544950. DOI: 10.3390/diagnostics14212421.

Metabolic Profiles and Blood Biomarkers to Discriminate between Benign Thyroid Nodules and Papillary Carcinoma, Based on UHPLC-QTOF-ESI-MS Analysis.

Berinde G, Socaciu A, Socaciu M, Petre G, Socaciu C, Piciu D Int J Mol Sci. 2024; 25(6).

PMID: 38542465 PMC: 10970441. DOI: 10.3390/ijms25063495.

Pilot Study on the Use of Untargeted Metabolomic Fingerprinting of Liquid-Cytology Fluids as a Diagnostic Tool of Malignancy for Thyroid Nodules.

Dandrea G, Jing L, Peyrottes I, Guigonis J, Graslin F, Lindenthal S Metabolites. 2023; 13(7).

PMID: 37512489 PMC: 10384948. DOI: 10.3390/metabo13070782.

Direct mass spectrometry analysis of biological tissue for diagnosis of thyroid cancer using wooden-tip electrospray ionization.

Liu D, Shen Y, Di D, Cai S, Huang X, Lin H Front Chem. 2023; 11:1134948.

PMID: 36846859 PMC: 9947238. DOI: 10.3389/fchem.2023.1134948.

The potential value of LC-MS non-targeted metabonomics in the diagnosis of follicular thyroid carcinoma.

Qin J, Yang Y, Du W, Li G, Wu Y, Luo R Front Oncol. 2023; 12:1076548.

PMID: 36620583 PMC: 9814718. DOI: 10.3389/fonc.2022.1076548.

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