Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China.
College of life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China.
BMC Cancer. 2020 Nov 12;20(1):1099. doi: 10.1186/s12885-020-07551-4.
Identifying the mutation status of KRAS is important for optimizing treatment in patients with colorectal cancer (CRC). The aim of this study was to investigate the predictive value of haematological parameters and serum tumour markers (STMs) for KRAS gene mutations.
The clinical data of patients with colorectal cancer from January 2014 to December 2018 were retrospectively collected, and the associations between KRAS mutations and other indicators were analysed. Receiver operating characteristic (ROC) curve analysis was performed to quantify the predictive value of these factors. Univariate and multivariate logistic regression models were applied to identify predictors of KRAS mutations by calculating the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs).
KRAS mutations were identified in 276 patients (35.2%). ROC analysis revealed that age, CA12-5, AFP, SCC, CA72-4, CA15-3, FERR, CYFRA21-1, MCHC, and tumor location could not predict KRAS mutations (P = 0.154, 0.177, 0.277, 0.350, 0.864, 0.941, 0.066, 0.279, 0.293, and 0.053 respectively), although CEA, CA19-9, NSE and haematological parameter values showed significant predictive value (P = 0.001, < 0.001, 0.043 and P = 0.003, < 0.001, 0.001, 0.031, 0.030, 0.016, 0.015, 0.019, and 0.006, respectively) but without large areas under the curve. Multivariate logistic regression analysis showed that CA19-9 was significantly associated with KRAS mutations and was the only independent predictor of KRAS positivity (P = 0.016).
Haematological parameters and STMs were related to KRAS mutation status, and CA19-9 was an independent predictive factor for KRAS gene mutations. The combination of these clinical factors can improve the ability to identify KRAS mutations in CRC patients.
确定 KRAS 基因突变状态对优化结直肠癌（CRC）患者的治疗至关重要。本研究旨在探讨血液学参数和血清肿瘤标志物（STM）对 KRAS 基因突变的预测价值。
回顾性收集 2014 年 1 月至 2018 年 12 月间的 CRC 患者临床资料，分析 KRAS 基因突变与其他指标的相关性。采用受试者工作特征（ROC）曲线分析量化这些因素的预测价值。通过计算比值比（OR）及其相应的 95%置信区间（CI），应用单因素和多因素 logistic 回归模型来识别 KRAS 突变的预测因子。
在 276 例患者中发现 KRAS 基因突变（35.2%）。ROC 分析显示，年龄、CA12-5、AFP、SCC、CA72-4、CA15-3、FERR、CYFRA21-1、MCHC 和肿瘤位置不能预测 KRAS 基因突变（P=0.154、0.177、0.277、0.350、0.864、0.941、0.066、0.279、0.293 和 0.053），尽管 CEA、CA19-9、NSE 和血液学参数值具有显著的预测价值（P=0.001、<0.001、0.043 和 P=0.003、<0.001、0.001、0.031、0.030、0.016、0.015、0.019 和 0.006），但曲线下面积均不大。多因素 logistic 回归分析显示，CA19-9 与 KRAS 突变显著相关，是 KRAS 阳性的唯一独立预测因子（P=0.016）。
血液学参数和 STM 与 KRAS 突变状态相关，CA19-9 是 KRAS 基因突变的独立预测因子。这些临床因素的组合可以提高识别 CRC 患者 KRAS 突变的能力。
