title: Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.forename: Wenyu
authors.initials: W
authors.lastname: Zhang
authors.forename: Jin
authors.initials: J
authors.lastname: Zang
authors.forename: Xinhua
authors.initials: X
authors.lastname: Jing
authors.forename: Zhandong
authors.initials: Z
authors.lastname: Sun
authors.forename: Wenying
authors.initials: W
authors.lastname: Yan
authors.forename: Dongrong
authors.initials: D
authors.lastname: Yang
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou 215006, China. bairong.shen@suda.edu.cn.
authors.forename: Bairong
authors.initials: B
authors.lastname: Shen
authors.forename: Feng
authors.initials: F
authors.lastname: Guo
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D030541:Databases, Genetic; D015972:Gene Expression Regulation, Neoplastic; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D058977:Molecular Sequence Annotation; D016133:Polymerase Chain Reaction; D011471:Prostatic Neoplasms / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs; D012333:RNA, Messenger