Chen Wen-Jie, He De-Shen, Tang Rui-Xue, Ren Fang-Hui, Chen Gang
Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, China E-mail :
Asian Pac J Cancer Prev. 2015;16(2):411-20. doi: 10.7314/apjcp.2015.16.2.411.
Ki-67 has been widely used as an indicator of cell proliferation in gliomas. However, the role of Ki-67 as a prognostic marker is still undefined. Thus, we conducted a meta-analysis of the published literatures in order to clarify the impact of Ki-67 on survival in glioma cases. Eligible studies were identified in PubMed, EMBASE, ISI Web of Science, Cochrane Central Register of Controlled Trials, Science Direct and Wiley Online Library with the last search updated on August 31, 2014. The clinical characteristics, overall survival (OS) and progression- free survival (PFS) together with Ki-67 expression at different time points were extracted. A total of 51 studies, covering 4,307 patients, were included in the current meta-analysis. The results showed that overexpression of Ki-67 can predict poor OS (HR=1.66, 95%CI: 1.53-1.80; Z=11.87; p=0.000) and poor PFS (HR=1.67, 95%CI: 1.47-1.91; Z=7.67; p=0.000) in gliomas. Moreover, subgroup analyses also indicated that high level of Ki-67 expression was related to poor OS and PFS in glioma patients regardless of region, pathology type, cut-off value and statistical method. In conclusion, the current meta-analysis revealed that Ki-67 expression might be a predicative factor for poor prognosis of glioma patients, emphasizing its importance as a predictor.
Ki-67已被广泛用作神经胶质瘤细胞增殖的指标。然而,Ki-67作为一种预后标志物的作用仍不明确。因此,我们对已发表的文献进行了荟萃分析,以阐明Ki-67对神经胶质瘤患者生存的影响。通过在PubMed、EMBASE、ISI科学网、Cochrane对照试验中央注册库、Science Direct和Wiley Online Library中检索,确定符合条件的研究,最后一次检索更新于2014年8月31日。提取临床特征、总生存期(OS)和无进展生存期(PFS)以及不同时间点的Ki-67表达情况。本荟萃分析共纳入51项研究,涵盖4307例患者。结果显示,Ki-67过表达可预测神经胶质瘤患者的OS较差(HR=1.66,95%CI:1.53-1.80;Z=11.87;p=0.000)和PFS较差(HR=1.67,95%CI:1.47-1.91;Z=7.67;p=0.000)。此外,亚组分析还表明,无论地区、病理类型、临界值和统计方法如何,Ki-67高表达均与神经胶质瘤患者较差的OS和PFS相关。总之,当前的荟萃分析表明,Ki-67表达可能是神经胶质瘤患者预后不良的预测因素,强调了其作为预测指标的重要性。