Objective:This meta-analysis sought to assess the overall diagnostic efficacy of long-noncoding RNA (lncRNA) MALAT-1 in lung cancer. Methods:Relevant studies were searched and obtained through the online PubMed, EMbase, EBSCO, CNKI, and Wanfang databases. Data were extracted and study quality of the included studies was assessed. The pooled effect sizes were synthesized and the summary receiver operator characteristic (SROC) curve was plotted using a bivariate meta-analysis model.Sensitivity analysis and meta-regression test were undertaken to identify the potential causes of study heterogeneity. Publication bias was judged by Deeks' funnel plot asymmetry test. The Z test was used to analyze the difference among pooled AUC values. Results:Five studies comprised of 433 lung cancer patients and 384 paired controls were included. The SROC displayed that the pooled sensitivity, specificity, and area under curve (AUC) of MALAT-1 testing for diagnosing lung cancer were 0.71 (95％ CI:0.67-0.74), 0.82 (95％ CI:0.79-0.84), and 0.89, respectively. Stratified analyses based on pathological type showed that MALAT-1 testing yielded an AUC of 0.91 in identifying non-small-cell lung cancer (NSCLC), and the AUC was 0.93 in confirming lung squamous cell carcinoma, which was better than that in lung adenocarcinoma (AUC=0.84;Z=1.97, P ＜ 0.05);moreover, serum-based MALAT-1 testing achieved an efficacy better than plasma-based analysis (AUC:0.94 vs. 0.88;Z=8.96, P ＜ 0.01). Study based on ethnicity showed that MALAT-1 harbored an AUC of 0.89 in Asian-based analysis, which was equal to that in Caucasian-based analysis (AUC=0.82;Z=1.11, P＞ 0.05).Deeks' funnel plot asymmetry test manifested no clear publication bias among studies (P=0.865). Conclusion:Circulating MALAT-1 testing reveals promising diagnostic efficacy in identification of lung cancer and therefore might be popularized as auxiliary biomarkers for NSCLC, especially for lung squamous cell carcinoma detection.%目的:系统性评价循环长链非编码RNA (lncRNA) MALAT-1在肺癌综合诊断中的价值.方法:检索PubMed、EM-base、EBSCO、CNKI、万方等数据库收集相关文献, 提取资料及评价纳入研究的文献质量;通过双变量Meta分析模型合并诊断效应量, 绘制综合受试者工作特征 (SROC) 曲线;采用敏感性分析、Meta回归分析探讨异质性来源, 并通过Deeks'漏斗图评估发表偏倚.利用Z检验比较组间合并曲线下面积 (AUC) 的差异.结果:共纳入符合标准的研究5项, 含433例肺癌患者和384例对照者.循环MALAT-1诊断肺癌的合并灵敏度为0.71 (95％CI:0.67～0.74), 特异度为0.82 (95％CI:0.79～0.84), AUC为0.89.基于病理类型的亚组分析显示, MALAT-1检测非小细胞肺癌 (NSCLC) 整体合并的AUC为0.91, 且在肺鳞癌中的诊断效能优于腺癌 (AUC:0.93 vs. 0.84;Z=1.97, P ＜0.05);基于血清的MALAT-1检测综合诊断效能高于血浆 (AUC:0.94 vs. 0.88;Z=8.96, P ＜0.01);按人群分析显示, MALAT-1在黄种和白种人群中的效能无显著差异 (AUC:0.89 vs. 0.82;Z=1.11, P＞ 0.05) .Deeks'漏斗图分析提示研究间不存在发表偏倚 (P=0.865) .结论:MALAT-1在肺癌中具有较高的综合诊断效能, 可作为NSCLC尤其是肺鳞癌诊断较好的辅助指标之一.
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