Integrating Tumor Genomics and Urinary Exosomal Proteomics to Establish a Multi-Layer Biomarker Framework for Early Risk Stratification and Post-Treatment Surveillance in Fresh Thyroid Cancer Patients

Thyroid cancer is the most common endocrine malignancy, and although differentiated thyroid cancer (DTC) generally confers favorable outcomes, 10-20% of patients still face substantial postoperative risks, including local recurrence, distant metastasis, and inadequate response to radioactive iodine therapy. Current risk stratification, largely based on tumor size, lymph node involvement, and histopathology, fails to adequately represent tumor heterogeneity and evolutionary changes, potentially resulting in both overtreatment and undertreatment. Next-generation sequencing (NGS) has revealed a stepwise accumulation of genomic alterations from early driver mutations (e.g., BRAF, RAS, RET/PTC, PAX8-PPARG) to late-stage progression events (e.g., TERT promoter, TP53, PI3K/AKT/mTOR), while metastatic lesions often harbor high-risk mutations absent in primary tumors, underscoring the limitations of single-time-point tissue sampling. Furthermore, serum thyroglobulin (Tg) surveillance is hindered in patients with anti-Tg antibodies. Extracellular vesicles (EVs), particularly those obtained from urine, provide a compelling liquid biopsy modality due to their non-invasiveness, repeatability, and reduced interference by abundant serum proteins. The investigators' previous findings demonstrate that urinary exosomal peptides-including U-Ex Tg, ANXA2, TIMP, and Angiopoietin-1-correlate with malignancy, capsular invasion, and nodal metastasis, and exhibit dynamic postoperative variation, suggesting their utility in detecting molecular residual disease.

This prospective study will recruit 100 fresh thyroid cancer cases and integrate tumor genomic profiling, urinary exosomal proteomics via LC-MRM/MS, and clinical phenotype assessment-including nodal involvement, subsequent therapies, and long-term outcomes-to delineate causal links between genomic drivers, proteomic execution signals, and clinical progression. The overarching aim is to establish an early risk-stratification and molecular recurrence-alerting model capable of identifying high-risk trajectories earlier than conventional approaches, thereby enhancing surveillance precision and enabling timely intervention. This multi-layered biomarker framework holds strong potential to redefine postoperative monitoring standards and advance the clinical and policy implementation of precision medicine in thyroid cancer.