2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Dysregulated lncRNAs are involved in the progress of myocardial infarction by constructing regulatory networks

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Long noncoding RNAs (lncRNAs) mediate important epigenetic regulation in a wide range of biological processes. However, the effect of all dysregulated lncRNAs in myocardial infarction (MI) is not clear. Whole transcriptome sequencing analysis was used to characterize the dynamic changes in lncRNA and mRNA expression. A gene network was constructed, and genes were classified into different modules using WGCNA. In addition, for all dysregulated lncRNAs, gene ontology analysis and cis-regulatory analysis were applied. The results demonstrated that a large number of the differentially co-expressed genes were primarily linked to the immune system process, inflammatory response, and innate immune response. The functional pathway analysis of the MEblue module included immune system process and apoptosis, and MEbrown included the T-cell receptor signal pathway by WGCNA. In addition, through cis-acting analysis of lncRNA regulation, the cis-regulated mRNAs were mainly enriched in immune system processes, innate immune responses, and VEGF signal pathways. We found that lncRNA regulation of mRNAs plays an important role in immune and inflammatory pathways. Our study provides a foundation to further understand the role and potential mechanism of dysregulated lncRNAs in the regulation of MI, in which many of them could be potential targets for MI.

          Related collections

          Most cited references52

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
                Bookmark

                Author and article information

                Contributors
                Journal
                Open Med (Wars)
                Open Med (Wars)
                med
                Open Medicine
                De Gruyter
                2391-5463
                08 March 2023
                2023
                : 18
                : 1
                : 20230657
                Affiliations
                Department of Cardiovascular Medicine, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College , Nanchang, 330000, China
                Article
                med-2023-0657
                10.1515/med-2023-0657
                9999115
                36910851
                1f2490b9-5806-4e1b-81f4-682a6a1e2749
                © 2023 the author(s), published by De Gruyter

                This work is licensed under the Creative Commons Attribution 4.0 International License.

                History
                : 25 September 2022
                : 08 January 2023
                : 07 February 2023
                Page count
                Pages: 13
                Categories
                Research Article

                long noncoding rna,myocardial infarction,immune system,inflammation,regulatory networks

                Comments

                Comment on this article