Purpose: To identify prostate cancer lncRNAs using a pipeline proposed in this study, which is applicable forthe identification of lncRNAs that are differentially expressed in prostate cancer tissues but have a negligiblepotential to encode proteins. Materials and Methods: We used two publicly available RNA-Seq datasets fromnormal prostate tissue and prostate cancer. Putative lncRNAs were predicted using the biological technology,then specific lncRNAs of prostate cancer were found by differential expression analysis and co-expressionnetwork was constructed by the weighted gene co-expression network analysis. Results: A total of 1,080 lncRNAtranscripts were obtained in the RNA-Seq datasets. Three genes (PCA3, C20orf166-AS1 and RP11-267A15.1)showed a significant differential expression in the prostate cancer tissues, and were thus identified as prostatecancer specific lncRNAs. Brown and black modules had significant negative and positive correlations withprostate cancer, respectively. Conclusions: The pipeline proposed in this study is useful for the prediction ofprostate cancer specific lncRNAs. Three genes (PCA3, C20orf166-AS1, and RP11-267A15.1) were identifiedto have a significant differential expression in prostate cancer tissues. However, there have been no publishedstudies to demonstrate the specificity of RP11-267A15.1 in prostate cancer tissues. Thus, the results of this studycan provide a new theoretic insight into the identification of prostate cancer specific genes.
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