Zhou Zhengran, Mortensen Richard, Hu Peishan, Jin Bo, Zheng Xiao-Bin, Li Xiao, Schilling James, Chou C James, Sylvester Karl G, Cong Yanghai, Wu Xiaojian, Gao Feng, Ling Xuefeng B, Liu Xuanhui
Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
iScience. 2025 Jul 21;28(9):113138. doi: 10.1016/j.isci.2025.113138. eCollection 2025 Sep 19.
Colorectal cancer (CRC) is among the leading causes of cancer-related deaths worldwide, predominantly caused by recurrence, underscoring the need for novel biomarkers for the early detection of relapse. We hypothesize that patients' response to cancer is closely linked to metabolic changes and can be detected in blood as an indicator for recurrence. Full metabolomics analysis was performed on the longitudinal samples collected from 160 patients with CRC before and after surgery for 24 months or more. Key blood metabolic biomarkers that distinguish relapse and non-relapse patients were identified, and a minimal residual disease (MRD) detection machine-learning model was constructed based on the discovered signatures. The model diagnosed CRC relapse with a sensitivity of 62% and specificity of 80%, with median and maximum lead times of 471 and 1056 days before diagnosed clinical relapse, suggesting it could be used as a novel diagnostic tool for the earlier detection of cancer relapse.
结直肠癌（CRC）是全球癌症相关死亡的主要原因之一，主要由复发引起，这突出表明需要新的生物标志物来早期检测复发。我们假设患者对癌症的反应与代谢变化密切相关，并且可以在血液中检测到作为复发的指标。对160例CRC患者术前及术后24个月或更长时间采集的纵向样本进行了全代谢组学分析。确定了区分复发和未复发患者的关键血液代谢生物标志物，并基于发现的特征构建了一个微小残留病（MRD）检测机器学习模型。该模型诊断CRC复发的灵敏度为62%，特异度为80%，在临床复发诊断前的中位和最长提前期分别为471天和1056天，表明它可作为早期检测癌症复发的新型诊断工具。
