结直肠癌肺转移患者行肺转移瘤切除术的 KRAS 突变的预后价值：系统评价和荟萃分析。
Prognostic value of KRAS mutation in patients undergoing pulmonary metastasectomy for colorectal cancer: A systematic review and meta-analysis.
机构信息
State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China; Nanshan School of Medicine, Guangzhou Medical University, Guangzhou, Guangdong, China.
Nanshan School of Medicine, Guangzhou Medical University, Guangzhou, Guangdong, China.
出版信息
Crit Rev Oncol Hematol. 2021 Apr;160:103308. doi: 10.1016/j.critrevonc.2021.103308. Epub 2021 Mar 19.
BACKGROUND
The purpose of the study was to evaluate whether KRAS mutation could be an independent prognostic biomarker in patients undergoing pulmonary metastasectomy (PM) for colorectal cancer (CRC).
METHODS
A systemic review was performed by searching online databases to identify studies reporting overall survival (OS) and recurrence free survival (RFS) of CRC patients undergoing PM. Pooled HRs were calculated for OS and RFS.
RESULTS
A total of 15233 patients from 60 studies were included. Pooled analysis showed that KRAS mutation was associated with worse OS (HR: 1.86, 95 % Cl: 1.35-2.57) and RFS (HR: 1.68, 95 % Cl: 1.38-2.04). A significant effect on OS and/or RFS was also shown by other 18 factors.
CONCLUSIONS
This meta-analysis found that KRAS mutation is an important prognostic predictor for OS and RFS in CRC patients undergoing PM, supporting a comprehensive model including clinicopathological and biological factors for optimal patients selection and prognosis for surgical treatment.
背景
本研究旨在评估 KRAS 突变是否可以作为结直肠癌（CRC）患者接受肺转移瘤切除术（PM）的独立预后生物标志物。
方法
通过在线数据库检索，对报告 CRC 患者接受 PM 后总生存率（OS）和无复发生存率（RFS）的研究进行系统评价。计算 OS 和 RFS 的合并 HR。
结果
共有 60 项研究的 15233 名患者纳入研究。荟萃分析显示，KRAS 突变与较差的 OS（HR：1.86，95%Cl：1.35-2.57）和 RFS（HR：1.68，95%Cl：1.38-2.04）相关。其他 18 个因素也对 OS 和/或 RFS 有显著影响。
结论
本荟萃分析发现，KRAS 突变是 CRC 患者接受 PM 后 OS 和 RFS 的重要预后预测因素，支持包括临床病理和生物学因素的综合模型，以实现最佳患者选择和手术治疗预后。
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