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J Transl Med. 2018 May 21;16(1):134. doi: 10.1186/s12967-018-1506-7.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

Lin Y1, Chen F1, Shen L1,2, Tang X1,3, Du C1, Sun Z1,4, Ding H1,5, Chen J6, Shen B7,8,9.

Author information

Center for Systems Biology, Soochow University, Suzhou, 215006, China.
Department of Genetics & Systems Biology Institute, Yale University School of Medicine, West Haven, CT, 06516, USA.
Department of Statistics and Data Science, Yale University, New Haven, CT, 06511, USA.
Jiangsu Health Vocational College, Nanjing, 211800, China.
Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, 215123, China.
School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, Suzhou, 215011, China.
Center for Systems Biology, Soochow University, Suzhou, 215006, China.
Center for Translational Biomedical Informatics, Guizhou University School of Medicine, Guiyang, 550025, China.
Institute for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041, China.



Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases.


In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets.


With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis.


Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.


Bioinformatics model; MicroRNA biomarkers; Network vulnerability analysis; Prostate cancer metastasis

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