title: Evaluation and identification of microRNA-106 in the diagnosis of cancer: a meta-analysis.
abstract: Recently, extensive research has identified the non-invasive and cost-effective biomarker microRNA-106 (miR-106) in cancer detection. However, inconsistent results have prevented its usage in clinical. Therefore, we conducted this meta-analysis aimed to systematically determine diagnostic accuracy of miR-106 in distinguishing patients with cancer from cancer-free controls and further evaluate its value serving as a biomarker in clinical. We conducted a systematically literature search in databases (PubMed, web of science, Embase and the Cochrane Library) collecting relevant articles up to July 22th, 2014. The overall diagnostic accuracy of miR-106 was assessed by the following indexes: sensitivity, specificity, PLR, NLR and DOR. The SROC curve with AUC value was also generated for the assessment. Due to the significant heterogeneity, the random effects approach was chosen in our analysis and meta-regression was performed to explore the potential source of it. We also tested potential presence of publication bias using Deeks' funnel plots test. Stata 12.0 statistical software was used for analysis in the present study. Overall, the 11 studies involving 756 cancer patients and 834 controls were considered eligible in our analysis. The results in our work showed that sensitivity of 0.57 (95% CI: 0.44-0.68) and specificity of 0.85 (95% CI: 0.72-0.92), with the under area AUC value of 0.75 (95% CI: 0.71-0.79) for miR-106 assay. Additionally, the combined PLR, NLR and DOR describing the discriminatory ability were 3.7 (95% CI: 2.2-6.2), 0.51 (95% CI: 0.42-0.62) and 7 (95% CI: 4-12) in the present analysis. The results in our meta-analysis showed that miR-106 had moderate accuracy in identifying cancer patients. Thus, further larger-scale prospective studies are needed to improve the diagnostic efficiency and explore the combination of miR-106 and other biomarkers with more pronounced accuracy.
authors.affiliation: Department of Pathology, Affiliated Hospital of Academy of Military Medical Sciences Beijing 100071, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.affiliation: Department of Pathology, Affiliated Hospital of Academy of Military Medical Sciences Beijing 100071, China.
authors.forename: Chun-Wei
authors.initials: CW
authors.lastname: Xu
authors.affiliation: Department of Pathology, Affiliated Hospital of Academy of Military Medical Sciences Beijing 100071, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Shao
authors.affiliation: Department of Pathology, Affiliated Hospital of Academy of Military Medical Sciences Beijing 100071, China.
authors.forename: Huai-Tao
authors.initials: HT
authors.lastname: Wang
authors.affiliation: Department of Pathology, Affiliated Hospital of Academy of Military Medical Sciences Beijing 100071, China.
authors.forename: Yong-Fang
authors.initials: YF
authors.lastname: Wu
authors.affiliation: Department of Pathology, Affiliated Hospital of Academy of Military Medical Sciences Beijing 100071, China.
authors.forename: Ye-Ying
authors.initials: YY
authors.lastname: Song
authors.affiliation: Department of Pathology, Affiliated Hospital of Academy of Military Medical Sciences Beijing 100071, China.
authors.forename: Xiao-Bing
authors.initials: XB
authors.lastname: Li
authors.affiliation: Department of Oncology, Affiliated Zhongshan Hospital of Dalian University Dalian, Liaoning 116001, China.
authors.forename: Wen-Bin
authors.initials: WB
authors.lastname: Gao
authors.affiliation: Department of Medical, Dalian University Dalian, Liaoning 116622, China.
authors.forename: Wen-Bo
authors.initials: WB
authors.lastname: Liang
keywords: MicroRNA-106; accuracy; cancer; meta-analysis