PURPOSE: To identify prostate cancer lncRNAs using a pipeline proposed in this study, which is applicable for the identification of lncRNAs that are differentially expressed in prostate cancer tissues but have a negligible potential to encode proteins. MATERIALS AND METHODS: We used two publicly available RNA-Seq datasets from normal 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-expression network was constructed by the weighted gene co-expression network analysis. RESULTS: A total of 1,080 lncRNA transcripts 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 prostate cancer specific lncRNAs. Brown and black modules had significant negative and positive correlations with prostate cancer, respectively. CONCLUSIONS: The pipeline proposed in this study is useful for the prediction of prostate cancer specific lncRNAs. Three genes (PCA3, C20orf166-AS1, and RP11-267A15.1) were identified to have a significant differential expression in prostate cancer tissues. However, there have been no published studies to demonstrate the specificity of RP11-267A15.1 in prostate cancer tissues. Thus, the results of this study can provide a new theoretic insight into the identification of prostate cancer specific genes.
PURPOSE: To identify prostate cancer lncRNAs using a pipeline proposed in this study, which is applicable for the identification of lncRNAs that are differentially expressed in prostate cancer tissues but have a negligible potential to encode proteins. MATERIALS AND METHODS: We used two publicly available RNA-Seq datasets from normal 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-expression network was constructed by the weighted gene co-expression network analysis. RESULTS: A total of 1,080 lncRNA transcripts 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 prostate cancer specific lncRNAs. Brown and black modules had significant negative and positive correlations with prostate cancer, respectively. CONCLUSIONS: The pipeline proposed in this study is useful for the prediction of prostate cancer specific lncRNAs. Three genes (PCA3, C20orf166-AS1, and RP11-267A15.1) were identified to have a significant differential expression in prostate cancer tissues. However, there have been no published studies to demonstrate the specificity of RP11-267A15.1 in prostate cancer tissues. Thus, the results of this study can provide a new theoretic insight into the identification of prostate cancer specific genes.
Authors: Amita Kashyap; Adelaide Rhodes; Brent Kronmiller; Josie Berger; Ashley Champagne; Edward W Davis; Mitchell V Finnegan; Matthew Geniza; David A Hendrix; Christiane V Löhr; Vanessa M Petro; Thomas J Sharpton; Jackson Wells; Clinton W Epps; Pankaj Jaiswal; Brett M Tyler; Stephen A Ramsey Journal: BMC Genomics Date: 2020-02-12 Impact factor: 3.969