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Long noncoding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and biological functions. With the increasing study of human diseases and biological processes, information in human H3K27ac ChIP-seq, ATAC-seq and DNase-seq datasets is accumulating rapidly, resulting in an urgent need to collect and process data to identify transcriptional regulatory regions of lncRNAs. We therefore developed a comprehensive database for human regulatory information of lncRNAs (TRlnc, http://bio.licpathway.net/TRlnc), which aimed to collect available resources of transcriptional regulatory regions of lncRNAs and to annotate and illustrate their potential roles in the regulation of lncRNAs in a cell type-specific manner. The current version of TRlnc contains 8 683 028 typical enhancers/super-enhancers and 32 348 244 chromatin accessibility regions associated with 91 906 human lncRNAs. These regions are identified from over 900 human H3K27ac ChIP-seq, ATAC-seq and DNase-seq samples. Furthermore, TRlnc provides the detailed genetic and epigenetic annotation information within transcriptional regulatory regions (promoter, enhancer/super-enhancer and chromatin accessibility regions) of lncRNAs, including common SNPs, risk SNPs, eQTLs, linkage disequilibrium SNPs, transcription factors, methylation sites, histone modifications and 3D chromatin interactions. It is anticipated that the use of TRlnc will help users to gain in-depth and useful insights into the transcriptional regulatory mechanisms of lncRNAs. [ABSTRACT FROM AUTHOR]

With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P  = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP. [ABSTRACT FROM AUTHOR]

In recent years, high-throughput genomic technologies like chromatin immunoprecipitation sequencing (ChIp-seq) and transcriptome sequencing (RNA-seq) have been becoming both more refined and less expensive, making them more accessible. Many circular RNAs (circRNAs) that originate from back-spliced exons have been identified in various cell lines across different species. However, the regulatory mechanism for transcription of circRNAs remains unclear. Therefore, there is an urgent need to construct a database detailing the transcriptional regulation of circRNAs. TRCirc (http://www.licpathway.net/TRCirc) provides a resource for efficient retrieval, browsing and visualization of transcriptional regulation information of circRNAs. The current version of TRCirc documents 92 375 circRNAs and 161 transcription factors (TFs) from more than 100 cell types and together represent more than 765 000 TF–circRNA regulatory relationships. Furthermore, TRCirc provides other regulatory information about transcription of circRNAs, including their expression, methylation levels, H3K27ac signals in regulation regions and super-enhancers associated with circRNAs. TRCirc provides a convenient, user-friendly interface to search, browse and visualize detailed information about these circRNAs. [ABSTRACT FROM AUTHOR]