Unveiling a novel biomarker panel for diagnosis and classification of well-differentiated thyroid carcinomas

Abstract

Thyroid cancer is the most common human endocrine malignancy with increasing global incidence. Papillary thyroid carcinomas (PTC) and follicular thyroid carcinomas (FTC) are well-differentiated thyroid cancers (WDTC) accounting for 95% of all thyroid cancer cases, with survival rates of almost 100% when diagnosed early. Since PTC and FTC have different modes of metastasis, they require different treatment strategies. Standard diagnosis by fine needle aspiration with cytopathological examination can be inaccurate in approximately 10-30% of all cases and difficult to definitively classify as WDTC. Currently, there is no single or panel of biomarkers available for thyroid cancer diagnosis and classification. This study identified novel biomarkers for thyroid cancer diagnosis and classification using proteomics, which may be translated into a biomarker panel for clinical application. Two-dimensional SDS-PAGE and mass spectrometry were used to identify potential biomarkers in papillary and follicular thyroid carcinoma cell lines, and the biomarkers were validated in five PTC and five FTC tissues, with their adjacent normal tissues from Thai patients. Eight biomarkers could distinguish PTC from normal tissues, namely enolase 1, triose phosphate isomerase, cathepsin D, annexin A2, cofilin 1, proliferating cell nuclear antigen (PCNA), copine 1 and heat shock protein 27 kDa (HSP27). These biomarkers can also discriminate FTC from normal tissues, except for annexin A2. On the contrary, annexin A2, cofilin 1, PCNA and HSP27 can be used to classify the types of WDTC. These findings have potential for use as a novel multi-marker panel for more accurate diagnosis and classification to better guide physicians on thyroid cancer treatment. Moreover, our results suggest the involvement of proteins in cell growth and proliferation, and the p53 pathway in the carcinogenesis of WDTC, which may lead to targeted therapy for thyroid cancer.