A novel lncRNA–miRNA–mRNA competitive endogenous RNA network for uveal melanoma prognosis constructed by weighted gene co-expression network analysis

Yu, Hongying; Cheng, Yuanrong; Li, Weihua; Li, Zhiquan; Wu, Peixin; Qiu, Sujuan; Zeng, Baozhu; Huang, Bing

doi:10.1016/j.lfs.2020.118409

Cited by 8 publications

(16 citation statements)

References 66 publications

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“…Subsequently, DEGs were extracted following the Linear Models for Microarray Data (LIMMA) method, and the gene with |log2 fold change (FC)|>1 and adjusted P value of < 0.05 was considered to be statistically significant [44] . A heatmap was visualized using the heatmap package of R, based on standard methods [ 18 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

“…These methods are typically data-driven and do not consider any biological information of the component genes as input; nonetheless, they have the advantage of identifying the distinguishing features without having any biological information about those features. In this regard, weighted gene co-expression network analysis (WGCNA) is a new biological construction method offering an alternative approach to discover new biomarkers for melanoma based on the known functions and interactions of individual molecules [ 17 , 18 ]. Furthermore, WGCNA has the added advantage of combining condition-specific transcriptome data and allowing the understanding of the functional role of individual genes and other biological molecules such as microRNAs (miRNAs), which have the capability of discriminating between diseased and healthy cells or between different melanoma stages [18] .…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, weighted gene co-expression network analysis (WGCNA) is a new biological construction method offering an alternative approach to discover new biomarkers for melanoma based on the known functions and interactions of individual molecules [ 17 , 18 ]. Furthermore, WGCNA has the added advantage of combining condition-specific transcriptome data and allowing the understanding of the functional role of individual genes and other biological molecules such as microRNAs (miRNAs), which have the capability of discriminating between diseased and healthy cells or between different melanoma stages [18] . Bioinformatics methods such as differentiation and enrichment analysis, clustering class discovery, mechanistic analysis, and class prediction of targeted mRNAs-miRNAs in VM+EMT network can help us in the discovery of the complexity of the melanomagenesis.…”

Section: Introductionmentioning

confidence: 99%

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Comparative mRNA/micro-RNA co-expression network drives melanomagenesis by promoting epithelial–mesenchymal transition and vasculogenic mimicry signaling

Liu

et al. 2021

Translational Oncology

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Highlights The expression of PLA1A, DSC3, and DMKN genes function as oncogenes that trigger. melanomagenesis on interaction with miR-563, miR-363, miR-770–5p, and miR-370. PLA1A and DMKN genes can be used as a promising diagnosis biomarkers to distinguish melanoma patients. The expression of PLA1A and DMNK are regulated by EMT and formation of VE. PLA1A/miR-563 and DMNK/miR-770–5p/miR-370 may contribute to melanomagenesis by triggering the EMT signaling pathway and VM formation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative mRNA/micro-RNA co-expression network drives melanomagenesis by promoting epithelial–mesenchymal transition and vasculogenic mimicry signaling

Liu

et al. 2021

Translational Oncology

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“…Thanks to UM TCGA findings, it is now possible to statistically elaborate these data to identify the expression relationships among RNA molecules and correlative interaction data from other sources. Other previous studies reported a systemic integrated view of RNA correlations in UM [ 35 , 36 ]; however, in this present work we propose the most comprehensive analysis and make these elaborations available through an interactive web-based user interface. The approach used to build VECTOR was already used in our previous papers on both UM and other cancer models [ 12 , 15 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

VECTOR: An Integrated Correlation Network Database for the Identification of CeRNA Axes in Uveal Melanoma

Barbagallo

Maria

Alecci

et al. 2021

Genes

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Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults and, although its genetic background has been extensively studied, little is known about the contribution of non-coding RNAs (ncRNAs) to its pathogenesis. Indeed, its competitive endogenous RNA (ceRNA) regulatory network comprising microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and mRNAs has been insufficiently explored. Thanks to UM findings from The Cancer Genome Atlas (TCGA), it is now possible to statistically elaborate these data to identify the expression relationships among RNAs and correlative interaction data. In the present work, we propose the VECTOR (uVeal mElanoma Correlation NeTwORk) database, an interactive tool that identifies and visualizes the relationships among RNA molecules, based on the ceRNA model. The VECTOR database contains: i) the TCGA-derived expression correlation values of miRNA-mRNA, miRNA-lncRNA and lncRNA-mRNA pairs combined with predicted or validated RNA-RNA interactions; ii) data of sense-antisense sequence overlapping; iii) correlation values of Transcription Factor (TF)-miRNA, TF-lncRNA, and TF-mRNA pairs associated with ChiPseq data; iv) expression data of miRNAs, lncRNAs and mRNAs both in UM and physiological tissues. The VECTOR web interface can be queried, by inputting the gene name, to retrieve all the information about RNA signaling and visualize this as a graph. Finally, VECTOR provides a very detailed picture of ceRNA networks in UM and could be a very useful tool for researchers studying RNA signaling in UM. The web version of Vector is freely available at the URL reported at the end of the Introduction.

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“…miRNA could trigger degradation of downstream target genes by recognizing the MRE in the 3’non-transcribed region ( 11 , 12 ). Therefore, when lncRNA and mRNA had the same MRE, both of them could competitively bind to miRNA, forming a competitive endogenous RNA (ceRNA) regulatory network ( 13 , 14 ). When miRNA bound to the target mRNA, the stability of mRNA decreased and its translation was hindered, thereby affecting gene expression.…”

Section: Introductionmentioning

confidence: 99%

The Regulatory Network and Potential Role of LINC00973-miRNA-mRNA ceRNA in the Progression of Non-Small-Cell Lung Cancer

Guo

Luo

et al. 2021

Front. Immunol.

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BackgroundThe occurrence and development of cancer could be promoted by abnormally competing endogenous RNAs (ceRNA) network. This article aims to determine the prognostic biomarker of ceRNA for non-small-cell lung cancer (NSCLC) prognosis.MethodsThe expression and clinical significance of LINC00973 in NSCLC tissues were analyzed via the The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), lnCAR, and clinical samples in Taihe Hospital. The biological functions and signaling pathways involved in target genes of ceRNA network were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Survival analysis, univariate and multivariate Cox regression analysis were used for prognostic-related mRNA.ResultsExpression of LINC00973 was increased in NSCLC tissues. High expression of LINC00973 was associated with poor prognosis of NSCLC patients. There were 15 miRNA and 238 differential mRNA in the INC00973-miRNA-mRNA ceRNA network, involving cell migration, endothelial cell proliferation, tumor growth factor (TGF)-β, cellular senescence, phosphatidylinositol 3-hydroxy kinase (PI3K)-Akt, Hippo, Rap1, mitogen-activated protein kinase (MAPK), cell cycle signaling pathway, etc. The expression levels of RTKN2, NFIX, PTX3, BMP2 and LOXL2 were independent risk factors for the poor prognosis of NSCLC patients.ConclusionsLINC00973-miRNA-mRNA ceRNA network might be the basis for determining pivotal post-translational regulatory mechanisms in the progression of NSCLC. BMP2, LOXL2, NFIX, PTX3 and RTKN2 might be valuable prognostic markers and potential therapeutic targets.

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A novel lncRNA–miRNA–mRNA competitive endogenous RNA network for uveal melanoma prognosis constructed by weighted gene co-expression network analysis

Cited by 8 publications

References 66 publications

Comparative mRNA/micro-RNA co-expression network drives melanomagenesis by promoting epithelial–mesenchymal transition and vasculogenic mimicry signaling

Comparative mRNA/micro-RNA co-expression network drives melanomagenesis by promoting epithelial–mesenchymal transition and vasculogenic mimicry signaling

VECTOR: An Integrated Correlation Network Database for the Identification of CeRNA Axes in Uveal Melanoma

The Regulatory Network and Potential Role of LINC00973-miRNA-mRNA ceRNA in the Progression of Non-Small-Cell Lung Cancer

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