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

      Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets

      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

          Objective

          Hirschsprung disease (HSCR) is one of the common neurocristopathies in children, which is associated with at least 20 genes and involves a complex regulatory mechanism. Transcriptional regulatory network (TRN) has been commonly reported in regulating gene expression and enteric nervous system development but remains to be investigated in HSCR. This study aimed to identify the potential TRN implicated in the pathogenesis and diagnosis of HSCR.

          Methods

          Based on three microarray datasets from the Gene Expression Omnibus database, the multiMiR package was used to investigate the microRNA (miRNA)–target interactions, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Then, we collected transcription factors (TFs) from the TransmiR database to construct the TF–miRNA–mRNA regulatory network and used cytoHubba to identify the key modules. Finally, the receiver operating characteristic (ROC) curve was determined and the integrated diagnostic models were established based on machine learning by the support vector machine method.

          Results

          We identified 58 hub differentially expressed microRNAs (DEMis) and 16 differentially expressed mRNAs (DEMs). The robust target genes of DEMis and DEMs mainly enriched in several GO/KEGG terms, including neurogenesis, cell–substrate adhesion, PI3K–Akt, Ras/mitogen-activated protein kinase and Rho/ROCK signaling. Moreover, 2 TFs ( TP53 and TWIST1), 4 miRNAs ( has-miR-107, has-miR-10b-5p, has-miR-659-3p, and has-miR-371a-5p), and 4 mRNAs ( PIM3, CHUK, F2RL1, and CA1) were identified to construct the TF–miRNA–mRNA regulatory network. ROC analysis revealed a strong diagnostic value of the key TRN regulons (all area under the curve values were more than 0.8).

          Conclusion

          This study suggests a potential role of the TF–miRNA–mRNA network that can help enrich the connotation of HSCR pathogenesis and diagnosis and provide new horizons for treatment.

          Related collections

          Most cited references66

          • 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: found
            Is Open Access

            clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

            Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

              Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
                Bookmark

                Author and article information

                Journal
                World J Pediatr Surg
                World J Pediatr Surg
                wjps
                wjps
                World Journal of Pediatric Surgery
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2096-6938
                2516-5410
                2023
                17 April 2023
                : 6
                : 2
                : e000547
                Affiliations
                [1 ]departmentDepartment of Pediatric Surgery, the Second Affiliated Hospital , Ringgold_12480Xi'an Jiaotong University , Xi'an, China
                [2 ]departmentInstitute of Neurobiology, Environment and Genes Related to Diseases Key Laboratory of Chinese Ministry of Education , Ringgold_12480Xi'an Jiaotong University , Xi'an, China
                [3 ]departmentDepartment of Pulmonary and Critical Care Medicine, Peking University Third Hospital , Ringgold_12465Peking University , Beijing, China
                Author notes
                [Correspondence to ] Dr Donghao Tian; yd1720@ 123456xjtu.edu.cn
                Author information
                http://orcid.org/0000-0002-4617-7640
                http://orcid.org/0000-0002-1886-6123
                Article
                wjps-2022-000547
                10.1136/wjps-2022-000547
                10111925
                4e3cf7b2-b562-4650-b5f2-40abb9eaca80
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/.

                History
                : 12 December 2022
                : 13 March 2023
                Funding
                Funded by: Shaanxi Science and Technology Department;
                Award ID: 2022SF-133/033
                Funded by: Xi'an Jiaotong University;
                Award ID: YXJLRH2022053
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81770513
                Award ID: 82071692
                Award ID: 82170531
                Categories
                Original Research
                1506
                Custom metadata
                unlocked

                congenital abnormalities,pediatrics,neonatal screening

                Comments

                Comment on this article