甲状腺乳头状癌复发风险预测生物标志物的研究进展

Advances in biomarkers for predicting recurrence risk in papillary thyroid carcinoma

摘要:

甲状腺乳头状癌（PTC）是最常见的甲状腺恶性肿瘤，尽管总体预后较好，但仍有部分患者在术后出现局部复发或远处转移，严重影响长期治疗效果与生活质量。传统基于临床病理特征的风险分层体系在个体化复发预测方面存在一定局限，促使生物标志物逐渐成为精准评估PTC复发风险的重要研究方向。近年来，血清学指标、遗传学标志物、肿瘤微环境相关免疫标志物以及液体活检技术等不断取得进展，其中BRAF^V600E、TERT启动子突变、炎症相关指标及循环肿瘤成分等在复发风险评估中展现出较高潜力。同时，多组学整合与人工智能技术的发展进一步推动了PTC复发预测由单一指标向多维动态模型转变。本文围绕 PTC 复发风险预测相关生物标志物的研究现状，对其作用机制、临床价值及应用局限进行综述，并结合液体活检、多组学及人工智能等新兴方向，探讨未来精准风险分层与个体化管理的发展趋势，以期为 PTC 的临床决策与长期随访提供参考。

Abstract:

Papillary thyroid carcinoma (PTC) is the most common type of thyroid malignancy. Although most patients have a favorable prognosis, a proportion still develop locoregional recurrence or distant metastasis after initial treatment, which adversely affects long-term outcomes and quality of life. Conventional risk stratification systems based on clinicopathological features have limited ability in individualized recurrence prediction, prompting increasing interest in biomarker-based precision assessment. In recent years, substantial advances have been achieved in serological biomarkers, genetic alterations, tumor microenvironment-related immune markers, and liquid biopsy technologies. Among them, BRAF^V600E mutation, TERT promoter mutation, inflammation-related indicators, and circulating tumor-derived components have demonstrated promising value in recurrence risk evaluation. Meanwhile, the integration of multi-omics approaches and artificial intelligence has further promoted the transition from single-parameter prediction to multidimensional dynamic risk models. This review summarizes the current progress in biomarkers associated with PTC recurrence risk, discusses their underlying mechanisms, clinical applications, and existing limitations, and further explores emerging strategies, including liquid biopsy, multi-omics integration, and artificial intelligence. These advances may facilitate precise risk stratification and individualized management of patients with PTC.