LncCeRBase: a database of experimentally validated human competing endogenous long non-coding RNAs

Pian, Cong; Zhang, Guangle; Tu, Tengfei; Ma, Xiangyu; Li, Fēi

doi:10.1093/database/bay061

Cited by 25 publications

(9 citation statements)

References 28 publications

(24 reference statements)

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“…Non-expressed genes across all samples in a given type of cancer were removed ( Figure 1A). Subsequently, we downloaded 2,667 experimentally validated ceRNA pairs from the LncCeRBase (Pian et al, 2018), miRSponge (Wang et al, 2015b), and lncACTdb (Wang et al, 2015a) databases. Considering the tissuespecific characteristics of ceRNAs and gene symbol mapping, seven validated ceRNA pairs in breast cancer and six validated ceRNA pairs in liver cancer were obtained ( Table 1).…”

Section: La For the Prediction Of Cerna Pairsmentioning

confidence: 99%

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Xiao

Gao

2020

Front. Genet.

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Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer.

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Section: La For the Prediction Of Cerna Pairsmentioning

confidence: 99%

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

Xiao

Gao

2020

Front. Genet.

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“…The BRCA related mRNAs are from DisGeNET v5.0 [43] and COSMIC v86 [44], and the BRCA related lncRNAs are from LncRNADisease v2.0 [45], Lnc2Cancer v2.0 [46] and MNDR v2.0 [47]. The ground truth of lncRNA related miRNA sponge interactions is obtained by integrating the interactions from miRSponge [48], LncCeRBase [49] and LncACTdb v2.0 [50].…”

Section: Heterogeneous Data Sourcesmentioning

confidence: 99%

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

Zhang

Liu

et al. 2020

PLoS Comput Biol

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Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline's performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCArelated modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

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“…lncRNAs are called for competing endogeneous RNAs (ceRNAs) when they bind to miRNA and regulate their functions. In LncCeRBase [30] there are details for ceRNAs, and lncRNAs containing genomic variants that disturb ceRNA network regulation [31]. All kinds of interactions of lncRNAs are available in LIVE [32].…”

Section: Databases For Rna Variationsmentioning

confidence: 99%

Systematics for types and effects of RNA variations

Vihinen

2020

RNA Biology

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Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.

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LncCeRBase: a database of experimentally validated human competing endogenous long non-coding RNAs

Cited by 25 publications

References 28 publications

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

Systematics for types and effects of RNA variations

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