Shangwei Ning

Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers

Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.

Identification of lncRNA-associated competing triplets reveals global patterns and prognostic markers for cancer

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.

In silico detection and characteristics of novel microRNA genes in the Equus caballus genome using an integrated ab initio and comparative genomic approach.

The importance of microRNAs at the post-transcriptional regulation level has recently been recognized in both animals and plants. We used the simple but effective sequential method of first Blasting known animal miRNAs against the horse genome and then using the located candidates to search for novel miRNAs by RNA folding method in the vicinity (+ -500 bp) of the candidates. Here, a total of 407 novel horse miRNA genes including 354 mature miRNAs were identified, of these, 75 miRNAs were grouped into 32 families based on seed sequence identity. MiRNA genes tend to be present as clusters in some chromosomes, and 146 miRNA genes accounted for 36% of the total were observed as part of polycistronic transcripts. Detailed analysis of sequence characteristics in novel horse and all previous known animal miRNAs were carried out. Our study will provide a reference point for further study on miRNAs identification in animals and improve the understanding of genome in horse.

LincSNP: a database of linking disease-associated SNPs to human large intergenic non-coding RNAs

BackgroundGenome-wide association studies (GWAS) have successfully identified a large number of single nucleotide polymorphisms (SNPs) that are associated with a wide range of human diseases. However, many of these disease-associated SNPs are located in non-coding regions and have remained largely unexplained. Recent findings indicate that disease-associated SNPs in human large intergenic non-coding RNA (lincRNA) may lead to susceptibility to diseases through their effects on lincRNA expression. There is, therefore, a need to specifically record these SNPs and annotate them as potential candidates for disease.DescriptionWe have built LincSNP, an integrated database, to identify and annotate disease-associated SNPs in human lincRNAs. The current release of LincSNP contains approximately 140,000 disease-associated SNPs (or linkage disequilibrium SNPs), which can be mapped to around 5,000 human lincRNAs, together with their comprehensive functional annotations. The database also contains annotated, experimentally supported SNP-lincRNA-disease associations and disease-associated lincRNAs. It provides flexible search options for data extraction and searches can be performed by disease/phenotype name, SNP ID, lincRNA name and chromosome region. In addition, we provide users with a link to download all the data from LincSNP and have developed a web interface for the submission of novel identified SNP-lincRNA-disease associations.ConclusionsThe LincSNP database aims to integrate disease-associated SNPs and human lincRNAs, which will be an important resource for the investigation of the functions and mechanisms of lincRNAs in human disease. The database is available at http://bioinfo.hrbmu.edu.cn/LincSNP.

Prioritizing human cancer microRNAs based on genes' functional consistency between microRNA and cancer.

The identification of human cancer-related microRNAs (miRNAs) is important for cancer biology research. Although several identification methods have achieved remarkable success, they have overlooked the functional information associated with miRNAs. We present a computational framework that can be used to prioritize human cancer miRNAs by measuring the association between cancer and miRNAs based on the functional consistency score (FCS) of the miRNA target genes and the cancer-related genes. This approach proved successful in identifying the validated cancer miRNAs for 11 common human cancers with area under ROC curve (AUC) ranging from 71.15% to 96.36%. The FCS method had a significant advantage over miRNA differential expression analysis when identifying cancer-related miRNAs with a fine regulatory mechanism, such as miR-27a in colorectal cancer. Furthermore, a case study examining thyroid cancer showed that the FCS method can uncover novel cancer-related miRNAs such as miR-27a/b, which were showed significantly upregulated in thyroid cancer samples by qRT-PCR analysis. Our method can be used on a web-based server, CMP (cancer miRNA prioritization) and is freely accessible at http://bioinfo.hrbmu.edu.cn/CMP. This time- and cost-effective computational framework can be a valuable complement to experimental studies and can assist with future studies of miRNA involvement in the pathogenesis of cancers.

miRSponge: a manually curated database for experimentally supported miRNA sponges and ceRNAs

In this study, we describe miRSponge, a manually curated database, which aims at providing an experimentally supported resource for microRNA (miRNA) sponges. Recent evidence suggests that miRNAs are themselves regulated by competing endogenous RNAs (ceRNAs) or ‘miRNA sponges’ that contain miRNA binding sites. These competitive molecules can sequester miRNAs to prevent them interacting with their natural targets to play critical roles in various biological and pathological processes. It has become increasingly important to develop a high quality database to record and store ceRNA data to support future studies. To this end, we have established the experimentally supported miRSponge database that contains data on 599 miRNA-sponge interactions and 463 ceRNA relationships from 11 species following manual curating from nearly 1200 published articles. Database classes include endogenously generated molecules including coding genes, pseudogenes, long non-coding RNAs and circular RNAs, along with exogenously introduced molecules including viral RNAs and artificial engineered sponges. Approximately 70% of the interactions were identified experimentally in disease states. miRSponge provides a user-friendly interface for convenient browsing, retrieval and downloading of dataset. A submission page is also included to allow researchers to submit newly validated miRNA sponge data. Database URL: http://www.bio-bigdata.net/miRSponge.

A novel reannotation strategy for dissecting DNA methylation patterns of human long intergenic non-coding RNAs in cancers

Despite growing consensus that long intergenic non-coding ribonucleic acids (lincRNAs) are modulators of cancer, the knowledge about the deoxyribonucleic acid (DNA) methylation patterns of lincRNAs in cancers remains limited. In this study, we constructed DNA methylation profiles for 4629 tumors and 705 normal tissue samples from 20 different types of human cancer by reannotating data of DNA methylation arrays. We found that lincRNAs had different promoter methylation patterns in cancers. We classified 2461 lincRNAs into two categories and three subcategories, according to their promoter methylation patterns in tumors. LincRNAs with resistant methylation patterns in tumors had conserved transcriptional regulation regions and were ubiquitously expressed across normal tissues. By integrating cancer subtype data and patient clinical information, we identified lincRNAs with promoter methylation patterns that were associated with cancer status, subtype or prognosis for several cancers. Network analysis of aberrantly methylated lincRNAs in cancers showed that lincRNAs with aberrant methylation patterns might be involved in cancer development and progression. The methylated and demethylated lincRNAs identified in this study provide novel insights for developing cancer biomarkers and potential therapeutic targets.

A global map for dissecting phenotypic variants in human lincRNAs.

Large intergenic noncoding RNAs (lincRNAs) are emerging as key factors of multiple cellular processes. Cumulative evidence has linked lincRNA polymorphisms to diverse diseases. However, the global properties of lincRNA polymorphisms and their implications for human disease remain largely unknown. Here we performed a systematic analysis of naturally occurring variants in human lincRNAs, with a particular focus on lincRNA polymorphism as novel risk factor of disease etiology. We found that lincRNAs exhibited a relatively low level of polymorphisms, and low single-nucleotide polymorphism (SNP) density lincRNAs might have a broad range of functions. We also found that some polymorphisms in evolutionarily conserved regions of lincRNAs had significant effects on predicted RNA secondary structures, indicating their potential contribution to diseases. We mapped currently available phenotype-associated SNPs to lincRNAs and found that lincRNAs were associated with a wide range of human diseases. Some lincRNAs could be responsible for particular diseases. Our results provided not only a global perspective on genetic variants in human lincRNAs but also novel insights into the function and etiology of lincRNA. All the data in this study can be accessed and retrieved freely via a web server at http://bioinfo.hrbmu.edu.cn/lincPoly.

A novel method to quantify gene set functional association based on gene ontology

Numerous gene sets have been used as molecular signatures for exploring the genetic basis of complex disorders. These gene sets are distinct but related to each other in many cases; therefore, efforts have been made to compare gene sets for studies such as those evaluating the reproducibility of different experiments. Comparison in terms of biological function has been demonstrated to be helpful to biologists. We improved the measurement of semantic similarity to quantify the functional association between gene sets in the context of gene ontology and developed a web toolkit named Gene Set Functional Similarity (GSFS; http://bioinfo.hrbmu.edu.cn/GSFS). Validation based on protein complexes for which the functional associations are known demonstrated that the GSFS scores tend to be correlated with sequence similarity scores and that complexes with high GSFS scores tend to be involved in the same functional catalogue. Compared with the pairwise method and the annotation method, the GSFS shows better discrimination and more accurately reflects the known functional catalogues shared between complexes. Case studies comparing differentially expressed genes of prostate tumour samples from different microarray platforms and identifying coronary heart disease susceptibility pathways revealed that the method could contribute to future studies exploring the molecular basis of complex disorders.

LincSNP 2.0: an updated database for linking disease-associated SNPs to human long non-coding RNAs and their TFBSs

We describe LincSNP 2.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), an updated database that is used specifically to store and annotate disease-associated single nucleotide polymorphisms (SNPs) in human long non-coding RNAs (lncRNAs) and their transcription factor binding sites (TFBSs). In LincSNP 2.0, we have updated the database with more data and several new features, including (i) expanding disease-associated SNPs in human lncRNAs; (ii) identifying disease-associated SNPs in lncRNA TFBSs; (iii) updating LD-SNPs from the 1000 Genomes Project; and (iv) collecting more experimentally supported SNP-lncRNA-disease associations. Furthermore, we developed three flexible online tools to retrieve and analyze the data. Linc-Mart is a convenient way for users to customize their own data. Linc-Browse is a tool for all data visualization. Linc-Score predicts the associations between lncRNA and disease. In addition, we provided users a newly designed, user-friendly interface to search and download all the data in LincSNP 2.0 and we also provided an interface to submit novel data into the database. LincSNP 2.0 is a continually updated database and will serve as an important resource for investigating the functions and mechanisms of lncRNAs in human diseases.