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March 31, 2026Frontiers in Endocrinology0 citationsOpen Access

Emerging liquid biopsy markers in thyroid cancer: from isolation technologies to clinical translation

YGYe GaoQDQiyue DuanYWYufei Wei

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Abstract

Diagnosis and treatment of thyroid cancer (TC) are undergoing radical changes, shifting from traditional tissue studies to liquid biopsies (LB). LB technologies provide effective, non-invasive solutions to these challenges. This paper reviews recent advances in the analysis of three important biomarker systems: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) and exosomes. Technically, microfluidics and bioaffinity-based physical capture strategies have significantly improved the efficiency with which CTCs can be separated and analysed at an individual cell level. Ultra-sensitive digital polymerase chain reaction (dPCR) and targeted sequencing enable accurate characterization of rare mutations and methylation alterations in ctDNA. Additionally, innovations in ultracentrifugation and new microfluidic chips have improved the efficiency with which exosomes and the information molecules they contain (e.g., miRNA and lncRNA) are extracted and detected. These abundant liquid biomarkers demonstrate significant value in clinical translation. CTCs provide a unique opportunity to study the mechanisms of metastasis and tumor heterogeneity, while ctDNA plays a central role in preoperative risk classification, monitoring minimal residual disease and tracking the dynamics of drug-resistant mutations. Exosomes, as stable carriers of nucleic acids, have significant potential for early diagnosis and assessment of treatment efficacy. Nevertheless, this field still faces significant challenges, including a lack of technical standardization and integration of different biomarker results, insufficient sensitivity in patients with low tumor burden and a lack of broad preclinical validation. In the future, joint analysis of multigenomic biomarkers and deep integration of the latest technologies, such as microfluidics and artificial intelligence, promise to create a more comprehensive ecosystem for precise TC diagnosis and treatment.

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Cite This Study

Gao et al. (2026) studied this question.

synapsesocial.com/papers/6a0cdf7f3b13d695db1f59d2https://doi.org/10.3389/fendo.2026.1757547
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