Department of Oncology, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Department of Gastrointestinal Surgery, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Saudi J Gastroenterol. 2019 Sep-Oct;25(5):293-301. doi: 10.4103/sjg.SJG_502_18.
BACKGROUND/AIM: To construct quantitative prognostic models for colorectal cancer (CRC) based on COX-2/C-MET/KRAS expression status in clinical practice.
Clinical factors and COX-2/C-MET/KRAS expression status of 578 eligible patients from two Chinese hospitals were included. The patients were randomly allocated into training and validation datasets. We created several models using Cox proportional hazard models: Signature contained clinical factors, Signature contained COX-2/C-MET/KRAS expression status, and Signature contained both. After comparing their accuracy, nomograms for progression-free survival (PFS) and overall survival (OS) were built for the best signatures, with their concordance index and calibration tested. Further, patients were subgrouped by the median of the best signatures, and survival differences between the subgroups were compared.
For PFS, among the three signatures, Signature had the best area under the curve (AUC), with the 1-, 2- and 3-year AUCs being 0.70, 0.73 and 0.89 in the training dataset, respectively and 0.67, 0.73 and 0.87 in the validation dataset, respectively. For OS, the AUCs of Signature for 1-, 2- and 3-years were 0.63, 0.71 and 0.81 in the training dataset, respectively and 0.68, 0.71 and 0.76 in validation dataset, respectively. The nomograms based on Signature and Signature had good calibrations. Subsequent stratification analysis demonstrated that the subgroups were significantly different for both PFS (training:P < 0.001; validation:P< 0.001) and OS (training:P < 0.001; validation:P < 0.001).
Combining clinical factors and COX-2/C-MET/KRAS expression status, our models provided accurate prognostic information in CRC. They can be used to aid treatment decisions in clinical practice.
背景/目的：基于 COX-2/C-MET/KRAS 表达状态构建用于临床实践的结直肠癌（CRC）定量预后模型。
纳入了来自中国两家医院的 578 名合格患者的临床因素和 COX-2/C-MET/KRAS 表达状态。患者被随机分配到训练数据集和验证数据集。我们使用 Cox 比例风险模型创建了几种模型：Signature 包含临床因素，Signature 包含 COX-2/C-MET/KRAS 表达状态，以及 Signature 同时包含两者。在比较它们的准确性后，为最佳 Signature 构建了无进展生存期（PFS）和总生存期（OS）的列线图，并测试了它们的一致性指数和校准。此外，根据最佳 Signature 的中位数对患者进行亚组分组，并比较亚组之间的生存差异。
对于 PFS，在三个 Signature 中，Signature 的曲线下面积（AUC）最佳，在训练数据集中的 1、2 和 3 年 AUC 分别为 0.70、0.73 和 0.89，在验证数据集中的 1、2 和 3 年 AUC 分别为 0.67、0.73 和 0.87。对于 OS，Signature 在训练数据集中的 1、2 和 3 年的 AUC 分别为 0.63、0.71 和 0.81，在验证数据集中的 1、2 和 3 年的 AUC 分别为 0.68、0.71 和 0.76。基于 Signature 和 Signature 的列线图具有良好的校准度。随后的分层分析表明，PFS（训练：P<0.001；验证：P<0.001）和 OS（训练：P<0.001；验证：P<0.001）的亚组之间均存在显著差异。
结合临床因素和 COX-2/C-MET/KRAS 表达状态，我们的模型为 CRC 提供了准确的预后信息。它们可用于辅助临床实践中的治疗决策。
