Skip to main content

01.12.2014 | Research | Ausgabe 1/2014 Open Access

Journal of Translational Medicine 1/2014

Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer

Journal of Translational Medicine > Ausgabe 1/2014
Wenyu Zhang, Jin Zang, Xinhua Jing, Zhandong Sun, Wenying Yan, Dongrong Yang, Feng Guo, Bairong Shen
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1479-5876-12-66) contains supplementary material, which is available to authorized users.
Wenyu Zhang, Jin Zang contributed equally to this work.

Competing interests

The authors declare they have no competing interests.

Authors’ contributions

WZ designed the miRNA biomarker prediction pipeline, performed the statistical analysis and drafted the manuscript. FG and JZ carried out the in vitro validation experiments. XJ performed the data collection process. WY, ZS and DY participated in the functional enrichment analysis. BS conceived and coordinated the overall study and revised the manuscript. All authors read and approved the final manuscript.



MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.


The human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.


Using this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.


Our analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.
Über diesen Artikel

Weitere Artikel der Ausgabe 1/2014

Journal of Translational Medicine 1/2014 Zur Ausgabe