Xu Longwen, Liu Mengjie, Lian Jie, Li Enmeng, Dongmin Chang, Li Xuqi, Wang Wenjuan
Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Department of General Surgery, The First Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China.
J Cancer Res Clin Oncol. 2023 Nov;149(15):13523-13543. doi: 10.1007/s00432-023-05187-y. Epub 2023 Jul 27.
A high postoperative recurrence rate seriously impedes colon cancer (CC) patients from achieving long-term survival. Here, we aimed to develop a Treg-related classifier that can help predict recurrence-free survival (RFS) and therapy benefits of stage I-III colon cancer.
A Treg-related prognostic classifier was built through a variety of bioinformatic methods, whose performance was assessed by KM survival curves, time-dependent receiver operating characteristic (tROC), and Harrell's concordance index (C-index). A prognostic nomogram was generated using this classifier and other traditional clinical parameters. Moreover, the predictive values of this classifier for immunotherapy and chemotherapy therapeutic efficacy were tested using multiple immunotherapy sets and R package "pRRophetic".
A nine Treg-related classifier categorized CC patients into high- and low-risk groups with distinct RFS in the multiple datasets (all p < 0.05). The AUC values of 5-year RFS were 0.712, 0.588, 0.669, and 0.662 in the training, 1st, 2nd, and entire validation sets, respectively. Furthermore, this classifier was identified as an independent predictor of RFS. Finally, a nomogram combining this classifier and three clinical variables was generated, the analysis of tROC, C-index, calibration curves, and the comparative analysis with other signatures confirmed its predictive performance. Moreover, KM analysis exhibited an obvious discrepancy in the subgroups, especially in different TNM stages and with adjuvant chemotherapy. We detected the difference between the two risk subsets of immune cell sub-population and the response to immunotherapy and chemotherapy.
We built a robust Treg-related classifier and generated a prognostic nomogram that predicts recurrence-free survival in stage I-III colon cancer that can identify high-risk patients for more personalized and effective therapy.
高术后复发率严重阻碍结肠癌（CC）患者实现长期生存。在此，我们旨在开发一种与调节性T细胞（Treg）相关的分类器，以帮助预测I-III期结肠癌的无复发生存期（RFS）和治疗获益。
通过多种生物信息学方法构建了一个与Treg相关的预后分类器，其性能通过Kaplan-Meier（KM）生存曲线、时间依赖的受试者工作特征（tROC）和Harrell一致性指数（C指数）进行评估。使用该分类器和其他传统临床参数生成了一个预后列线图。此外，使用多个免疫治疗数据集和R包“pRRophetic”测试了该分类器对免疫治疗和化疗疗效的预测价值。
一个包含九个与Treg相关的分类器将CC患者分为高风险和低风险组，在多个数据集中具有明显不同的RFS（所有p<0.05）。训练集、第一验证集、第二验证集和整个验证集中5年RFS的AUC值分别为0.712、0.588、0.669和0.662。此外，该分类器被确定为RFS的独立预测因子。最后，生成了一个结合该分类器和三个临床变量的列线图，tROC分析、C指数、校准曲线以及与其他特征的比较分析证实了其预测性能。此外，KM分析在亚组中表现出明显差异，特别是在不同的TNM分期和辅助化疗中。我们检测了免疫细胞亚群的两个风险亚组之间的差异以及对免疫治疗和化疗的反应。
我们构建了一个强大的与Treg相关的分类器，并生成了一个预后列线图，可预测I-III期结肠癌的无复发生存期，能够识别高风险患者，以便进行更个性化和有效的治疗。
