血清肿瘤标志物在结直肠癌（CRC）的早期诊断、疗效评估及预后判断中已展现出一定价值，但在预测患者术后远期复发方面的效能仍需深入探索。CRC术后复发是一个复杂的多因素过程，其机制主要涉及癌细胞的隐匿性转移与增殖、肿瘤微环境的改变以及免疫逃逸等，风险因素包括患者年龄、家族史、病理分化程度、手术方式、辅助治疗的有效性以及生活习惯等。癌胚抗原（CEA）、糖类抗原19-9（CA19-9）等常用血清肿瘤标志物虽与CRC的病理特征存在显著相关性，在术后复发预测中具有一定作用，但面临手术应激反应、敏感性、特异性限制等挑战。未来发展方向包括深度整合血清标志物、影像学和基因组学等多模态数据，利用人工智能和机器学习技术构建和优化预测模型，以及加强跨学科合作，为患者提供更全面有效的预后评估及诊治策略。

Serum tumor markers have shown certain value in the early diagnosis, efficacy evaluation,and prognosis assessment of colorectal cancer (CRC). However, their effectiveness in predicting the long-term postoperative recurrence of patients still needs to be explored in depth. Postoperative recurrence of CRC is a complex multi-factorial process, and its mechanisms mainly involve the occult metastasis and proliferation of cancer cells, changes in the tumor microenvironment, and immune escape. Risk factors include the patient's age, family history, degree of pathological differentiation, surgical approach, effectiveness of adjuvant therapy,and lifestyle habits, etc. Although commonly used serum tumor markers such as carcinoembryonic antigen (CEA)and carbohydrate antigen 19-9 (CA19-9) have a significant correlation with the pathological characteristics of CRC and play a certain role in predicting postoperative recurrence, they face challenges such as surgical stress response and limitations in sensitivity and specificity. Future development directions include the in-depth integration of multimodal data such as serum markers, imaging, and genomics, the use of artificial intelligence and machine learning technologies to construct and optimize prediction models, as well as strengthening interdisciplinary cooperation to provide patients with more comprehensive and effective prognosis assessment,diagnosis, and treatment strategies.