Database Commons

a catalog of worldwide biological databases

LncACTdb 3.0 is a comprehensive database of experimentally supported interactions among competing endogenous RNA (ceRNA) and the corresponding personalized networks contributing to precision medicine. LncACTdb 3.0 is freely available at http://bio-bigdata.hrbmu.edu.cn/LncACTdb or http://www.bio-bigdata.net/LncACTdb. We have updated the LncACTdb 3.0 database with several new features, including (i) 5669 experimentally validated ceRNA interactions across 25 species and 537 diseases/phenotypes through manual curation of published literature, (ii) personalized ceRNA interactions and networks for 16 228 patients from 62 datasets in TCGA and GEO, (iii) sub-cellular and extracellular vesicle locations of ceRNA manually curated from literature and data sources, (iv) more than 10 000 experimentally supported long noncoding RNA (lncRNA) biomarkers associated with tumor diagnosis and therapy, and (v) lncRNA/mRNA/miRNA expression profiles with clinical and pathological information of thousands of cancer patients. A panel of improved tools has been developed to explore the effects of ceRNA on individuals with specific pathological backgrounds. For example, a network tool provides a comprehensive view of lncRNA-related, patient-specific, and custom-designed ceRNA networks. LncACTdb 3.0 will provide novel insights for further studies of complex diseases at the individual level and will facilitate the development of precision medicine to treat such diseases.

We describe LncACTdb 2.0 (http://www.bio-bigdata.net/LncACTdb/), an updated and significantly expanded database which provides comprehensive information of competing endogenous RNAs (ceRNAs) in different species and diseases. We have updated LncACTdb 2.0 with more data and several new features, including (i) manually curating 2663 experimentally supported ceRNA interactions from >5000 published literatures; (ii) expanding the scope of the database up to 23 species and 213 diseases/phenotypes; (iii) curating more ceRNA types such as circular RNAs and pseudogenes; (iv) identifying and scoring candidate lncRNA-associated ceRNA interactions across 33 cancer types from TCGA data; (v) providing illustration of survival, network and cancer hallmark information for ceRNAs. Furthermore, several flexible online tools including LncACT-Get, LncACT-Function, LncACT-Survival, LncACT-Network and LncACTBrowser have been developed to perform customized analysis, functional analysis, survival analysis, network illustration and genomic visualization. LncACTdb 2.0 also provides newly designed, user-friendly web interfaces to search, browse and download all the data. The BLAST interface is convenient for users to query dataset by inputting custom sequences. The Hot points interface provides users the most studied items by others. LncACTdb 2.0 is a continually updated database and will serve as an important resource to explore ceRNAs in physiological and pathological processes.

Recent studies have suggested that long non-coding RNAs (lncRNAs) can interact with microRNAs (miRNAs) and indirectly regulate miRNA targets though competing interactions. However, the molecular mechanisms underlying these interactions are still largely unknown. In this study, these lncRNA-miRNA-gene interactions were defined as lncRNA-associated competing triplets (LncACTs), and an integrated pipeline was developed to identify lncACTs that are active in cancer. Competing lncRNAs had sponge features distinct from non-competing lncRNAs. In the lncACT cross-talk network, disease-associated lncRNAs, miRNAs and coding-genes showed specific topological patterns indicative of their competence and control of communication within the network. The construction of global competing activity profiles revealed that lncACTs had high activity specific to cancers. Analyses of clustered lncACTs revealed that they were enriched in various cancer-related biological processes. Based on the global cross-talk network and cluster analyses, nine cancer-specific sub-networks were constructed. H19- and BRCA1/2-associated lncACTs were able to discriminate between two groups of patients with different clinical outcomes. Disease-associated lncACTs also showed variable competing patterns across normal and cancer patient samples. In summary, this study uncovered and systematically characterized global properties of human lncACTs that may have prognostic value for predicting clinical outcome in cancer patients.