Emerging evidences demonstrate that circular RNAs (circRNAs) are abnormally expressed in tumors and could serve as prognostic markers for cancers. However, the expression patterns and clinical implications of circRNAs in nonsmall cell lung cancer (NSCLC) remain obscure. In this study, we profiled circRNA expressions in 10 pairs of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) after ribosomal RNA-depletion and RNase R digestion to enrich circRNAs. Combining five circRNA computational programs, we found that LUAD and LUSC not only share common expression patterns, but also exhibit distinct circRNA expression signatures. Moreover, the Receiver Operating Characteristic (ROC) curve analysis indicated that hsa_circ_0077837 and hsa_circ_0001821 could serve as potential biomarkers for both LUAD and LUSC, while hsa_circ_0001073 and hsa_circ_0001495 could be diagnostic/ subtyping marker for LUAD and LUSC, respectively. Therefore, our findings highlight the important diagnostic potential of circRNAs in NSCLC.

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tRNA-derived small RNAs (tsRNAs) are a class of novel small RNAs, ubiquitously present in prokaryotes and eukaryotes. It has been reported that tsRNAs exhibit spatiotemporal expression patterns and can function as regulatory molecules in many biological processes. Current tsRNA databases only cover limited organisms and ignore tsRNA functional characteristics. Thus, integrating more relevant tsRNA information is helpful for further exploration. Here, we present a tsRNA database, named tsRBase, which integrates the expression pattern and functional information of tsRNAs in multiple species. In tsRBase, we identified 121 942 tsRNAs by analyzing more than 14 000 publicly available small RNA-seq data covering 20 species. This database collects samples from different tissues/cell-lines, or under different treatments and genetic backgrounds, thus helps depict specific expression patterns of tsRNAs under different conditions. Importantly, to enrich our understanding of biological significance, we collected tsRNAs experimentally validated from published literatures, obtained protein-binding tsRNAs from CLIP/RIP-seq data, and identified targets of tsRNAs from CLASH and CLEAR-CLIP data. Taken together, tsRBase is the most comprehensive and systematic tsRNA repository, exhibiting all-inclusive information of tsRNAs from diverse data sources of multiple species. tsRBase is freely available at http://www.tsrbase.org.

Background: Enhancers can act as cis-regulatory elements (CREs) to control development and cellular function by regulating gene expression in a tissue-specific and ubiquitous manner. However, the regulatory network and characteristic of different types of enhancers (e.g., transcribed/non-transcribed enhancers, tissue-specific/ubiquitous enhancers) across multiple tissues are still unclear.Results: Here, a total of 53,924 active enhancers and 10,307 enhancer-associated RNAs (eRNAs) in 10 tissues (adrenal, brain, breast, heart, liver, lung, ovary, placenta, skeletal muscle and kidney) were identified through the integration of histone modifications (H3K4me1, H3K27ac and H3K4me3) and DNase I hypersensitive sites (DHSs) data. Moreover, 40,101 tissue-specific enhancers (TS-Enh), 1,241 ubiquitously expressed enhancers (UE-Enh) as well as transcribed enhancers (T-Enh), including 7,727 unidirectionally transcribed enhancers (1D-Enh) and 1,215 bidirectionally transcribed enhancers (2D-Enh) were defined in 10 tissues. The results show that enhancers exhibited high GC content, genomic variants and transcription factor binding sites (TFBS) enrichment in all tissues. These characteristics were significantly different between TS-Enh and UE-Enh, T-Enh and NT-Enh, 2D-Enh and 1D-Enh. Furt hermore, the results showed that enhancers obviously upregulate the expression of adjacent target genes which were remarkably correlated with the functions of corresponding tissues. Finally, a free user-friendly tissue-specific enhancer database, TiED (http://lcbb.swjtu.edu.cn/TiED), has been built to store, visualize, and confer these results.Conclusion: Genome-wide analysis of the regulatory network and characteristic of various types of enhancers showed that enhancers associated with TFs, eRNAs and target genes appeared in tissue specificity and function across different tissues.

Recent studies have indicated that long non-coding RNAs (lncRNAs) and mRNA function as competing endogenous RNAs (ceRNAs) that compete to bind to shared microRNA (miRNA) recognition elements (MREs) to perform specific biological functions during tumorigenesis. The tumor suppressor p53 is a master regulator of cancer-related biological processes by acting as a transcription factor to regulate target genes including miRNA and lncRNA. However, the mechanism in human hepatocellular carcinoma and whether p53-mediated RNA targets could form ceRNA network remain unclear. Here, we identified a series of differential expressed miRNAs, lncRNA and mRNA which were potentially regulated by p53 using RNA sequencing in HepG2. Genomic characteristics comparative analysis showed significant differences between mRNAs and lncRNAs. By integrating experimentally confirmed Ago2 and p53 binding sites, we constructed a highly reliable p53-mediated ceRNA network using hypergeometric test. The KEGG pathway enrichment analysis showed that the ceRNA network highly enriched in the cancer or p53-associated signaling pathways. Finally, using betweenness centrality analysis, we identified five master miRNAs (hsa-miR-3620-5p, hsa-miR-3613-3p, hsa-miR-6881-3p, hsa-miR-6087 and hsa-miR-18a-3p) that regulated most of the target RNAs, suggesting these miRNAs play central roles in the whole p53-mediated ceRNAs network. Taken together, our results provide a new regulatory mechanism of p53 networks for future studies in cancer therapeutics.