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      Identification of Potential Biomarkers of Prognosis-Related Long Non-Coding RNA (lncRNA) in Pediatric Rhabdoid Tumor of the Kidney Based on ceRNA Networks

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          Abstract

          Background

          Long non-coding RNA (lncRNA) can act as competing endogenous RNA (ceRNA) during tumor development. However, no study has elucidated the ceRNA network in pediatric rhabdoid tumor of the kidney (RTK) and its prognostic-related lncRNAs. The goal of the present study was to identify potential biomarkers of prognostic-related lncRNAs.

          Material/Methods

          RNA sequencing and clinical data were procured from the TARGET database. The “EdgeR” package was used to obtain differentially expressed lncRNA (DElncRNA), differentially expressed messenger RNAs (DEmRNA), and differentially expressed microRNAs (DEmiRNA). Cytoscape software was used to construct a ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted on the ceRNA network-related DEmRNA. The Kaplan-Meier method was used for predicting survival with ceRNA network-related DElncRNA. Univariate and multivariate Cox analyses were used to identify prognosis-related lncRNAs in the ceRNA network, and an RTK prognostic signature was constructed.

          Results

          We identified 1109 DElncRNAs, 215 DEmiRNAs, and 3436 DEmRNAs; and 107 DElncRNAs, 21 DEmiRNAs, and 74 DEmRNAs were included in the ceRNA regulatory network. GO enrichment analysis and KEGG pathway enrichment indicated that the DEmRNAs were mainly related to the regulation of phospholipase C activity and the MAPK signaling pathway. Survival analysis showed that 9 of 107 DElncRNAs were correlated with prognosis ( P<0.05). Univariate and multivariate Cox analysis identified 4 DElncRNAs (HNF1A-AS1, TPTEP1, SNHG6, and ZNF503-AS2) to establish a predictive model and can be used as independent prognostic biomarkers.

          Conclusions

          We constructed a ceRNA network that reveals potential lncRNA biomarkers for pediatric RTK.

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          Most cited references25

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language?

            Here, we present a unifying hypothesis about how messenger RNAs, transcribed pseudogenes, and long noncoding RNAs "talk" to each other using microRNA response elements (MREs) as letters of a new language. We propose that this "competing endogenous RNA" (ceRNA) activity forms a large-scale regulatory network across the transcriptome, greatly expanding the functional genetic information in the human genome and playing important roles in pathological conditions, such as cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Functional Classification and Experimental Dissection of Long Noncoding RNAs

              Over the last decade, it has been increasingly demonstrated that the genomes of many species are pervasively transcribed, resulting in the production of numerous long noncoding RNAs (lncRNAs). At the same time, it is now appreciated that many types of DNA regulatory elements, such as enhancers and promoters, regularly initiate bidirectional transcription. Thus, discerning functional noncoding transcripts from a vast transcriptome is a paramount priority, and challenge, for the lncRNA field. In this review, we aim to provide a conceptual and experimental framework for classifying and elucidating lncRNA function. We categorize lncRNA loci into those that regulate gene expression in cis versus those that perform functions in trans , and propose an experimental approach to dissect lncRNA activity based on these classifications. These strategies to further understand lncRNAs promise to reveal new and unanticipated biology, with great potential to advance our understanding of normal physiology and disease.
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                Author and article information

                Journal
                Med Sci Monit
                Med Sci Monit
                Medical Science Monitor
                Medical Science Monitor : International Medical Journal of Experimental and Clinical Research
                International Scientific Literature, Inc.
                1234-1010
                1643-3750
                2020
                17 December 2020
                15 October 2020
                : 26
                : e927725-1-e927725-9
                Affiliations
                [1 ]Department of Pediatric Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong, P.R. China
                [2 ]Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao, Shandong, P.R. China
                Author notes
                Corresponding Author: Qian Dong, e-mail: 18661801885@ 123456163.com
                [A]

                Study Design

                [B]

                Data Collection

                [C]

                Statistical Analysis

                [D]

                Data Interpretation

                [E]

                Manuscript Preparation

                [F]

                Literature Search

                [G]

                Funds Collection

                Article
                927725
                10.12659/MSM.927725
                7754694
                33328429
                5b15242b-b1cc-4786-af6a-0a09b36434a4
                © Med Sci Monit, 2020

                This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International ( CC BY-NC-ND 4.0)

                History
                : 29 July 2020
                : 30 September 2020
                Categories
                Database Analysis

                biological markers,pediatrics,rhabdoid tumor,rna, long noncoding

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