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      The new ceRNA crosstalk between mRNAs and miRNAs in intervertebral disc degeneration

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          Abstract

          Degeneration of the intervertebral disc has been linked to lower back pain. To date, pathophysiological mechanisms of intervertebral disc degeneration (IDD) remain unclear; it is meaningful to find effective diagnostic biomarkers and new therapeutic strategies for IDD. This study aimed to reveal the molecular mechanism of IDD pathogenesis from the multidimensional transcriptomics perspective. Here, we acquired IDD bulk omics datasets (GSE67567 and GSE167199) including mRNA, microRNA expression profiles, and single-cell RNA sequencing (GSE199866) from the public Gene Expression Omnibus (GEO) database. Through principal component analysis and Venn analysis, we found different expression patterns in the IDD transcription level and identified 156 common DEGs in both bulk datasets. GO and KEGG functional analyses showed these dysregulators were mostly enriched in the collagen-containing extracellular matrix, cartilage development, chondrocyte differentiation, and immune response pathways. We also constructed a potentially dysregulated competing endogenous RNA (ceRNA) network between mRNAs and miRNAs related to IDD based on microRNA target information and co-expression analysis of RNA profiles and identified 36 ceRNA axes including ZFP36/miR-155-5p/FOS, BTG2/hsa-miR-185-5p/SOCS3, and COL9A2/hsa-miR-664a-5p/IBA57. Finally, in integrating bulk and single-cell transcriptome data analyses, a total of three marker genes, COL2A1, PAX1, and ZFP36L2, were identified. In conclusion, the key genes and the new ceRNA crosstalk we identified in intervertebral disc degeneration may provide new targets for the treatment of IDD.

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          Most cited references90

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

                Contributors
                Journal
                Front Cell Dev Biol
                Front Cell Dev Biol
                Front. Cell Dev. Biol.
                Frontiers in Cell and Developmental Biology
                Frontiers Media S.A.
                2296-634X
                02 December 2022
                2022
                : 10
                : 1083983
                Affiliations
                Department of Spine Surgery , Beijing Jishuitan Hospital , Fourth Clinical College of Peking University , Beijing, China
                Author notes

                Edited by: Shuangfei Ni, First Affiliated Hospital of Zhengzhou University, China

                Reviewed by: Cheng Huang, China-Japan Friendship Hospital, China

                Derong Xu, Qingdao University, China

                *Correspondence: Xingye Li, xingyeli@ 123456yeah.net

                This article was submitted to Molecular and Cellular Pathology, a section of the journal Frontiers in Cell and Developmental Biology

                Article
                1083983
                10.3389/fcell.2022.1083983
                9755594
                36531954
                dd52aa9e-6ccd-4f9e-9796-938c0bc87825
                Copyright © 2022 Li, An, Wang and Han.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 October 2022
                : 18 November 2022
                Categories
                Cell and Developmental Biology
                Original Research

                intervertebral disc degeneration,mirnas,cernas,single-cell transcriptome sequencing,mrnas

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