After the metabolic syndrome and its components, thyroid disorders represent the most common endocrine disorders, with increasing prevalence in the last two decades. Thyroid dysfunctions are distinguished by hyperthyroidism, hypothyroidism, or inflammation (thyroiditis) of the thyroid gland, in addition to the presence of thyroid nodules that can be benign or malignant. Thyroid cancer is typically detected via an ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) and cytological examination of the specimen. This approach has significant limitations due to the small sample size and inability to characterize follicular lesions adequately. Due to the rapid advancement of high-throughput molecular biology techniques, it is now possible to identify new biomarkers for thyroid neoplasms that can supplement traditional imaging modalities in postoperative surveillance and aid in the preoperative cytology examination of indeterminate or follicular lesions. Here, we review current knowledge regarding biomarkers that have been reliable in detecting thyroid neoplasms, making them valuable tools for assessing the efficacy of surgical procedures or adjunctive treatment after surgery. We are particularly interested in providing an up-to-date and systematic review of emerging biomarkers, such as mRNA and non-coding RNAs, that can potentially detect thyroid neoplasms in clinical settings. We discuss evidence for miRNA, lncRNA and circRNA dysregulation in several thyroid neoplasms and assess their potential for use as diagnostic and prognostic biomarkers.
Bibliographical noteKAUST Repository Item: Exported on 2023-07-24
Acknowledged KAUST grant number(s): BAS/1/1624-01-01, FCC/1/1976-47-01, OSR#4129, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/3450-01-01, URF/1/4098-01-01, FCC/1/1976- 26-01
Acknowledgements: This work is part of the collaboration between the Laboratory of Radiobiology and Molecular Genetics, Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia and King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia. This work was supported by the Republic of Serbia’s Ministry of Education, Science, and Technological Development under Contract No#451-03-1/2023-01/200017 and by the KAUST grant OSR#4129 (to E.R.I.), and King Abdullah University of Science and Technology (KAUST) through grant awards Nos. BAS/1/1624-01-01, FCC/1/1976-47-01, FCC/1/1976- 26-01, URF/1/3450-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, and URF/1/4098-01-01. The authors acknowledge the generous assistance from Dragan Todorovic, the graphic designer, for his excellent assistance in preparing the figures in this article.
ASJC Scopus subject areas
- Endocrinology, Diabetes and Metabolism