MNDR v2.0: an updated resource of ncRNA–disease associations in mammals

Cui, Tianyu; Zhang, Lin; Huang, Yan; Ying, Yibin; Tan, Puwen; Zhao, Yue; Hu, Yongfei; Xu, Li‐Yan; Li, En‐Min; Wang, Dong

doi:10.1093/nar/gkx1025

Cited by 112 publications

(99 citation statements)

References 30 publications

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“…4B). Notably, 95.3% (82/86) of the miRNAs were denoted to be associated with melanoma by Mammal NcRNA-Disease Repository (MNDR) v2.0 (confidence score>0.4) [28]. An examination of the degree distribution of these RNAs revealed a power-law with a slope of -0.747 and 2 = 0.932 , suggesting a typical scale-free structure of biological network.…”

Section: Construction Of An Immune Infiltration-related Cerna Networkmentioning

confidence: 99%

Comprehensive Analysis of the Tumor Microenvironment in Cutaneous Melanoma associated with Immune Infiltration

et al. 2020

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Accumulating evidence suggests that the malignant phenotypes of cancers are determined not only by the intrinsic properties of cancer cells but also by components in the tumor microenvironment (TME). In this study, we comprehensively characterized the TME of cutaneous melanoma (CM). As a result, tumor stage, tissue site, ulceration, thickness as well as patient age, sex were associated with immune infiltration. Patients of higher immune infiltration exhibited better survival outcomes, and antitumor effector cells, such as CD8 T cells and M1 macrophages, were found in significantly higher numbers in those tissues. Differential expression of mRNAs and long non-coding RNAs (lncRNAs) was analyzed and utilized to construct an immune-related competing endogenous RNA network, in which a lncRNA-associated subnetwork that could positively regulate the expression of IFN-γ was highlighted. Functional analysis confirmed that this network was remarkably enriched in functional terms related to both immune response and tumor-intrinsic pathways. Finally, a total of 109 high-confidence prognostic genes were identified, and a gene module that contained several key immune checkpoint molecules or modulators (PD-1, PD-L1, PD-L2, and LCK) was screened, which confers survival benefit for CM patients as supported by both overall and relapse-free survival rates from different datasets.

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Section: Construction Of An Immune Infiltration-related Cerna Networkmentioning

confidence: 99%

Comprehensive Analysis of the Tumor Microenvironment in Cutaneous Melanoma associated with Immune Infiltration

et al. 2020

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“…Novel datasets were obtained to build our enrichment categories, consisting of Gene Ontology [25], miRTarBase 8.0 [15], KEGG [26], miRandola 2017 [22], miRPathDB 2.0 [16], TissueAtlas [17], MNDR v2.0 [19], NPInter 4.0 [27], RNALocate v2.0 [21], TAM 2.0 [13], and TransmiR v2.0 [20]. Other pre-existing datasets have been updated, including HMDD v3.0 [18] and miRBase v22.1 [28].…”

Section: Data Collectionmentioning

confidence: 99%

“…All kinds of enrichment tools rely on high quality sets of miRNA categories that were either obtained by curation of scientific literature or collected from specific databases. For instance, curated miRNA annotations can be obtained from miRBase or miRCarta [14], miRNA-target interactions from miRTarBase [15], miRNA-pathway associations from miRPathDB [16], tissuespecific miRNAs from the human TissueAtlas [17], or miRNA-disease associations from HMDD [18] or MNDR [19], many of which were updated in the last two years. Further specialised annotations like miRNA and transcription factor interactions provided by TransmiR [20], miRNA sub-cellular localisations collected in RNALocate [21], or extra-cellular circulating miRNAs contained in miRandola [22] provide target categories for integrated enrichment analysis.…”

Section: Introductionmentioning

confidence: 99%

miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems

Kern

Fehlmann

Solomon

et al. 2020

Preprint

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AbstractGene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs.Here, we present miEAA 2.0, supporting miRNA input from Homo sapiens, Mus musculus, and Rattus norvegicus. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miR-Base miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualisations such as heatmaps and running sum curves with background distributions. Lastly, additional methods to correct for multiple hypothesis testing were implemented. We demonstrate the new features using case studies for human kidney cancer and mouse samples. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.

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“…The BRCA related mRNAs are from DisGeNET v5.0 [36] and COSMIC v86 [37], and the BRCA related lncRNAs are from LncRNADisease v2.0 [38], Lnc2Cancer v2.0 [39] and MNDR v2.0 [40]. The ground truth of lncRNA related miRNA sponge interactions is obtained by integrating the interactions from miRSponge [41], LncCeRBase [42] and LncACTdb v2.0 [43].…”

Section: Heterogeneous Data Sourcesmentioning

confidence: 99%

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

Zhang

Liu

et al. 2019

Preprint

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AbstractUntil 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 BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.Author summaryPrevious studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.

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MNDR v2.0: an updated resource of ncRNA–disease associations in mammals

Cited by 112 publications

References 30 publications

Comprehensive Analysis of the Tumor Microenvironment in Cutaneous Melanoma associated with Immune Infiltration

Comprehensive Analysis of the Tumor Microenvironment in Cutaneous Melanoma associated with Immune Infiltration

miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems

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

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