Yang Shici, Zhu Gaohong, He Rui, Fang Dong, Feng Jiaojiao
Department of Nuclear Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Oncol Lett. 2023 Jul 27;26(3):396. doi: 10.3892/ol.2023.13982. eCollection 2023 Sep.
Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.
甲状腺癌（TC）是一类广泛的肿瘤分类，其中分化型甲状腺癌（DTC）是常见的组织学亚型。DTC的特征是晚期死亡率增加，这导致了DTC总体死亡率较高。这种进展主要归因于分子驱动基因的改变，从而导致侵袭、转移和去分化等表型变化。由于诊断和治疗选择不足，DTC的临床管理具有挑战性。组学技术的出现为DTC的诊断和治疗提供了一条有前景的途径。识别能够预测DTC早期进展为晚期不良结局的分子标志物对于精确诊断和治疗至关重要。本综述旨在通过组学技术，特别是转录组学和蛋白质组学，将大数据与生物系统相结合，以加深我们对DTC的理解，这可以揭示致癌作用的分子机制。
