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// Yan Chen 1 , Zhenzhou Xiao 1 , Minhua Hu 1 , Xiaoli Luo 1 and Zhaolei Cui 1 1 Laboratory of Biochemistry and Molecular Biology Research, Fujian Provincial Key Laboratory of Tumor Biotherapy, Department of Clinical Laboratory, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China Correspondence to: Zhaolei Cui, email: cuileidizi@fjmu.edu.cn Keywords: MALAT-1, cancer, diagnosis, meta-analysis Received: June 19, 2017 Accepted: August 26, 2017 Published: September 18, 2017 ABSTRACT Metastasis-associated lung adenocarcinoma transcript 1 ( MALAT-1 ) is one kind of long non-coding RNAs (lncRNAs) that has been recognized as a hallmark of the onset and development of several carcinomas. This study seek to meta-analyze the overall diagnostic efficacy of elevated MALAT-1 expression profile for human cancers. Studies on the diagnostic performance of MALAT-1 in cancers were retrieved by searching the online databases. The combined effect sizes were summarized using a bivariate meta-analysis model. Impacts of publication bias on the pooled effect sizes were assessed using “Duval and Tweedie nonparametric trim and fill method”. Sensitivity analysis and meta-regression test were applied to deeply trace the heterogeneity sources among eligible studies. A total of 14 studies with 1342 cancer cases were included. The combined effect sizes showed that MALAT-1 expression profiling conferred an estimated sensitivity of 0.69 (95% CI: 0.62–0.75) ( I 2 = 84.01%, P < 0.001), specificity of 0.85 (95% CI: 0.79–0.90) ( I 2 = 87.95%, P < 0.001) and AUC (area under curve) of 0.83 in distinguishing cancer patients from noncancerous contrasts. Moreover, stratified analysis depending on cancer type manifested that elevated MALAT-1 harbored a promising efficacy in the diagnosis of pulmonary tumors (AUC = 0.90), digestive system tumors (AUC = 0.84), gynecologic cancers (AUC = 0.84) and nasopharyngeal carcinoma (AUC = 0.84), particularly in confirming the subtype of squamous carcinoma (AUC = 0.91) and non-small cell lung carcinoma (AUC = 0.88) in lung cancer. Other analyses based on test matrix and ethnicity also presented robust results. Collectively, elevated MALAT-1 could be developed as an auxiliary molecular marker to aid in cancer diagnosis.

// Zhaolei Cui 1 , Yan Chen 1 , Zhenzhou Xiao 1 , Minhua Hu 1 , Yingying Lin 1 , Yansong Chen 1 , Yuhong Zheng 1 1 Department of Clinical Laboratory, Fujian Provincial Key Laboratory of Tumor Biotherapy, Fujian Provincial Cancer Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, China Correspondence to: Yan Chen, e-mail: yanc2015@163.com Keywords: lncRNA, gastric cancer, biomarker, diagnostic accuracy, meta-analysis Received: November 09, 2015 Accepted: March 10, 2016 Published: March 22, 2016 ABSTRACT Background: Long non-coding RNAs (lncRNAs) are highlighted as novel cancer biomarkers with great promise. Herein, we focused on summarizing the overall diagnostic performance of lncRNAs for gastric cancer (GC). Methods: Publications fulfilling the search criteria were selected from the online databases. Study quality was assessed according to the Quality Assessment for Studies of Diagnostic Accuracy (QUADAS) checklist. The summary receiver operator characteristic (SROC) curve was plotted using a bivariate meta-analysis model. Statistical analysis was performed based on the platforms of STATA 12.0 and Meta-Disc 1.4 software. Results: Fifteen studies with 1252 patients and 1283 matched controls were included. The pooled sensitivity and specificity for lncRNA expression profile in differentiating GC patients from cancer-free individuals were 0.68 (95%CI: 0.61-0.74) and 0.79 (95%CI: 0.72-0.84), respectively, corresponding to an area under curve (AUC) of 0.80. Moreover, the stratified analyses demonstrated that plasma-based lncRNA profiling harbored higher accuracy than that tissue-based assay (specificity: 0.80 versus 0.75; AUC: 0.84 versus 0.77). Conclusions: LncRNA profiling hallmarks a moderate diagnostic value in the management of GC and that lncRNA expression patterns may potentially be utilized as auxiliary biomarkers in confirming GC.