Related Experiment Video
Updated: Jan 21, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Biomarkers for detecting prostate cancer: Protocol for an umbrella review with integrated network meta-analysis
Junhai Jia1, Yue Sun2, Jingjie Ren2
1Gansu Province Hospital Rehabilitation Center.
Background:
Prostate cancer (PCa) is common, with it being the 2nd most prevalent cancer in men worldwide and the 6th leading cause of death in men. Screening for any type of cancer aims to increase the chances of successful treatment through early detection of the disease. There were some systematic reviews (SRs) evaluated the diagnostic value of biomarkers for the diagnosis of PCa and no studies have been conducted to analyze the quality of these SRs. We are not clear which kind of marker is the best choice. Thus, this study aims to assess the methodologic quality of the SRs and reanalyze the published data based on SRs for the biomarkers to find the optimal biomarker for the early diagnosis of PCa.
Methods:
We performed a systematic literature search of PubMed, Embase, Web of Science, and Cochrane Library and to identify relevant SRs from inception to April 2019. Diagnostic accuracy studies included any type of single biomarker or combined biomarkers aimed at evaluating the diagnostic value is considered eligible for this overview. The Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument will be used to evaluate the risk of bias of the included SRs. Standard pairwise meta-analysis and adjusted indirect comparison will be used to compare the diagnostic value of different biomarkers.
Results:
The results of this study will be submitted to a peer-reviewed journal for publication.
Conclusion:
This study will reanalyze the published data based on SRs. We hope that the results will help find a biomarker with the superior diagnostic performance for the diagnosis of PCa.
Prospero Registration Number:
CRD42019125880.
Related Concept Videos
Review and Preview
Percentiles are a type of fractile that partition data into...
Review and Preview
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks
Directing Effect of Substituents: meta-Directing Groups
Cancer Survival Analysis

