Identification of cancer-related lncRNAs through integrating genome, regulome and transcriptome features.

LncRNAs have become rising stars in biology and medicine, due to their versatile functions in a wide range of important biological processes and active roles in various human cancers. Here, we developed a computational method based on the naïve Bayesian classifier method to identify cancer-related lncRNAs by integrating genome, regulome and transcriptome data, and identified 707 potential cancer-related lncRNAs. We demonstrated the performance of the method by ten-fold cross-validation, and found that integration of multi-omic data was necessary to identify cancer-related lncRNAs. We identified 707 potential cancer-related lncRNAs and our results showed that these lncRNAs tend to exhibit significant differential expression and differential DNA methylation in multiple cancer types, and prognosis effects in prostate cancer. We also found that these lncRNAs were more likely to be direct targets of TP53 family members than others. Moreover, based on 147 lncRNA knockdown data in mice, we validated that four of six mouse orthologous lncRNAs were significantly involved in many cancer-related processes, such as cell differentiation and the Wnt signaling pathway. Notably, one lncRNA, lnc-SNURF-1, which was found to be associated with TNF-mediated signaling pathways, was up-regulated in prostate cancer and the protein-coding genes affected by knockdown of the lncRNA were also significantly aberrant in prostate cancer patients, suggesting its probable importance in tumorigenesis. Taken together, our method underlines the power of integrating multi-omic data to uncover cancer-related lncRNAs.