背景:结直肠癌肝转移的发生显著影响预后。现有研究表明,原发肿瘤的位置,血管浸润,淋巴结转移,术前肿瘤标志物异常是结直肠癌肝转移的危险因素。PD-L1(程序性细胞死亡1配体1)的阳性表达可能是鼻咽癌和胃癌的有利预后指标。其中CPS(联合阳性评分)量化PD-L1表达水平。根据以前的研究,探讨CPS作为结直肠癌肝转移的潜在危险因素,并综合其他独立危险因素,建立结直肠癌肝转移的新预测模型。
方法:对湘雅二医院经病理确诊的437例结直肠癌患者进行回顾性分析。中南大学,从2019年1月1日至2021年12月31日。收集数据,包括CPS,年龄,性别,原发肿瘤位置,Ki-67表达,病理分化,神经入侵,血管浸润,淋巴结转移,和术前肿瘤标志物。使用Youden指数确定了连续变量CPS的最佳截止点,所有的CPS都根据这个阈值分为高风险和低风险组(低于阈值的分数被认为是高风险,而高于它的被认为是低风险的)。单因素logistic回归分析结直肠癌肝转移的危险因素,然后应用多因素logistic回归分析整合所选择的危险因素。通过构建受试者工作特性(ROC)曲线对所建立的预测模型进行了验证,校正曲线,和DCA(决策曲线分析)。为了可视化目的,构建了一个列线图。
结果:确定的PD-L1CPS的临界点为4.5,分数低于该阈值表明结直肠癌肝转移的高风险。此外,除直肠以外的原发肿瘤,存在结肠周围淋巴结转移,肿瘤标志物CEA和CA199水平异常是结直肠癌肝转移的独立危险因素。构建的临床预测模型具有良好的预测能力和准确性,ROC曲线下面积为0.871(95%CI0.838-0.904)。
结论:对CPS作为结直肠癌肝转移的新预测因子进行了探索和验证。基于此,通过整合其他独立危险因素,建立了结直肠癌肝转移的新临床预测模型。DCA,临床影响曲线,基于该模型构建的列线图具有重要的临床意义,为临床实践提供指导。
BACKGROUND: The occurrence of liver metastasis significantly affects the prognosis of colorectal cancer (CRC). Existing research indicates that primary tumor location, vascular invasion, lymph node metastasis, and abnormal preoperative tumor markers are risk factors for CRC liver metastasis. Positive expression of programmed cell death ligand 1 (PD-L1) may serve as a favorable prognostic marker for nasopharyngeal and gastric cancers, in which combined positive score (CPS) quantifies the level of PD-L1 expression. This study aimed to explore CPS as a potential risk factor for CRC liver metastasis and integrate other independent risk factors to establish a novel predictive model for CRC liver metastasis.
METHODS: A retrospective analysis was conducted on 437 patients with CRC pathologically diagnosed at The Second Xiangya Hospital of Central South University from January 1, 2019, to December 31, 2021. Data were collected, including CPS, age, gender (male and female), primary tumor location, Ki-67 expression, pathologic differentiation, neural invasion, vascular invasion, lymph node metastasis, and preoperative tumor markers. The optimal cutoff point for the continuous variable CPS was determined using the Youden index, and all CPSs were dichotomized into high- and low-risk groups based on this threshold (scores below the threshold were considered high risk, and score above the threshold were considered low risk). Univariate logistic regression analysis was employed to identify risk factors for CRC liver metastasis, followed by
multivariate logistic regression analysis to integrate the selected risk factors. The predictive model was validated through the construction of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A nomogram was constructed for visualization.
RESULTS: The determined cutoff point for PD-L1 CPS was 4.5, with scores below this threshold indicating a high risk of CRC liver metastasis. In addition, primary tumor origin other than the rectum, presence of pericolonic lymph node metastasis, and abnormal levels of tumor markers carcinoembryonic antigen and cancer antigen 19-9 were identified as independent risk factors for CRC liver metastasis. The constructed clinical prediction model demonstrated good predictive ability and accuracy, with an area under the ROC curve of 0.871 (95% CI, 0.838-0.904).
CONCLUSIONS: The exploration and validation of CPS as a novel predictor of CRC liver metastasis were performed. Based on these findings, a new clinical prediction model for CRC liver metastasis was developed by integrating other independent risk factors. The DCA, clinical impact curve, and nomogram graph constructed on the basis of this model have significant clinical implications and guide clinical practice.