Systematic Review and Meta-Analysis of Diagnostic Accuracy of miRNAs in Patients with Pancreatic Cancer.
Ontology highlight
ABSTRACT: Background
It is reported that miRNAs are aberrantly expressed in patients with pancreatic cancer. However, the diagnostic value of miRNAs in pancreatic cancer remains controversial. The meta-analysis was to access diagnostic accuracy of miRNAs in pancreatic cancer.Methods
PubMed, Scopus, Web of Science, Chinese National Knowledge Infrastructure (CNKI), WANFANG Data, China Biomedical Literature Database (CBM), and VIP databases were retrieved up to June 30, 2016, to collect articles concerning the diagnosis of miRNAs in pancreatic cancer. The methodological quality of each study was assessed by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). This meta-analysis was conducted using RevMan5.0, MetaDiSc 1.4, and Stata 12.0 software.Results
There are 40 articles including 109 studies. The pooled SEN was 0.81 (95% CI, 0.80-0.82), the pooled SPE was 0.78 (95% CI, 0.77-0.79), the pooled +LR was 3.32 (95% CI, 2.92-3.80), the pooled -LR was 0.27 (95% CI, 0.24-0.31), the pooled DOR was 14.56 (95% CI, 11.55-18.34), and pooled AUC was 0.86 (95% CI, 0.84-0.88).Discussion
This meta-analysis demonstrated that miRNA makes a significant impact in the pancreatic cancer diagnosis with a high SEN and SPE, particularly using multiple miRNAs.
SUBMITTER: Sun X
PROVIDER: S-EPMC5977035 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Publications
Systematic Review and Meta-Analysis of Diagnostic Accuracy of miRNAs in Patients with Pancreatic Cancer.
Sun Xiao X Zhou Xiaobin X Zhang Yuan Y Zhu Xiaoyan X Liu Haihua H
Disease markers 20180515
<h4>Background</h4>It is reported that miRNAs are aberrantly expressed in patients with pancreatic cancer. However, the diagnostic value of miRNAs in pancreatic cancer remains controversial. The meta-analysis was to access diagnostic accuracy of miRNAs in pancreatic cancer.<h4>Methods</h4>PubMed, Scopus, Web of Science, Chinese National Knowledge Infrastructure (CNKI), WANFANG Data, China Biomedical Literature Database (CBM), and VIP databases were retrieved up to June 30, 2016, to collect artic ...[more]
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