Circular RNAs (circRNAs) are a novel class of non-coding RNA that assumes a covalently closed continuous conformation. CircRNAs were previously thought to be the byproducts of splicing errors caused by low abundance and the technological limitations. With the recent development of high-throughput sequencing technology, numerous circRNAs have been discovered in many species. Recent studies have revealed that circRNAs are stable and widely expressed, and often exhibit cell type-specific or tissue-specific expression. Most circRNAs can be generated from exons, introns, or both. Remarkably, emerging evidence indicates that some circRNAs can serve as microRNA (miRNA) sponges, regulate transcription or splicing, and can interact with RNA binding proteins (RBPs). Moreover, circRNAs have been reported to play essential roles in myriad life processes, such as aging, insulin secretion, tissue development, atherosclerotic vascular disease risk, cardiac hypertrophy and cancer. Although circRNAs are ancient molecules, they represent a newly appreciated form of noncoding RNA and as such have great potential implications in clinical and research fields. Here, we review the current understanding of circRNA classification, function and significance in physiological and pathological processes. We believe that future research will increase our understanding of the regulation and function of these novel molecules.

Identification of disease-associated circular RNAs (circRNAs) is of critical importance, especially with the dramatic increase in the amount of circRNAs. However, the availability of experimentally validated disease-associated circRNAs is limited, which restricts the development of effective computational methods. To our knowledge, systematic approaches for the prediction of disease-associated circRNAs are still lacking. In this study, we propose the use of deep forests combined with positive-unlabeled learning methods to predict potential disease-related circRNAs. In particular, a heterogeneous biological network involving 17 961 circRNAs, 469 miRNAs, and 248 diseases was constructed, and then 24 meta-path-based topological features were extracted. We applied 5-fold cross-validation on 15 disease data sets to benchmark the proposed approach and other competitive methods and used Recall@k and PRAUC@k to evaluate their performance. In general, our method performed better than the other methods. In addition, the performance of all methods improved with the accumulation of known positive labels. Our results provided a new framework to investigate the associations between circRNA and disease and might improve our understanding of its functions.

Long noncoding RNAs (lncRNAs) have been shown to play critical roles in the development and progression of diseases. lncRNA activated by transforming growth factor beta (TGF-β) (lncRNA-ATB) was discovered as a prognostic factor in hepatocellular carcinoma, gastric cancer, and colorectal cancer. However, little is known about the role of lncRNA-ATB in pancreatic cancer. This study aimed to assess lncRNA-ATB expression in pancreatic cancer and explore its role in pancreatic cancer pathogenesis. Quantitative real-time polymerase chain reaction was performed to detect lncRNA-ATB expression in 150 pancreatic cancer tissues and five pancreatic cancer cell lines compared to paired adjacent normal tissues and normal human pancreatic ductal epithelial cell line HPDE6c-7. The correlations between lncRNA-ATB expression and clinicopathological characteristics and prognosis were also analyzed. We found that lncRNA-ATB expression was decreased in pancreatic cancer tissues and pancreatic cancer cell lines. Low lncRNA-ATB expression levels were significantly correlated with lymph node metastases (yes vs. no, P = 0.009), neural invasion (positive vs. negative, P = 0.049), and clinical stage (early stage vs. advanced stage, P = 0.014). Moreover, patients with low lncRNA-ATB expression levels exhibited markedly worse overall survival prognoses (P < 0.001). Multivariate analysis indicated that decreased lncRNA-ATB expression was an independent predictor of poor prognosis in pancreatic cancer patients (P = 0.005). In conclusion, lncRNA-ATB may play a critical role in pancreatic cancer progression and prognosis and may serve as a potential prognostic biomarker in pancreatic cancer patients.