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Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets
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 sy...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111925/ https://www.ncbi.nlm.nih.gov/pubmed/37082700 http://dx.doi.org/10.1136/wjps-2022-000547 |
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author | Xu, Wenyao Yu, Hui Chen, Dian Pan, Weikang Yang, Weili Miao, Jing Jia, Wanying Zheng, Baijun Liu, Yong Chen, Xinlin Gao, Ya Tian, Donghao |
author_facet | Xu, Wenyao Yu, Hui Chen, Dian Pan, Weikang Yang, Weili Miao, Jing Jia, Wanying Zheng, Baijun Liu, Yong Chen, Xinlin Gao, Ya Tian, Donghao |
author_sort | Xu, Wenyao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10111925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-101119252023-04-19 Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets Xu, Wenyao Yu, Hui Chen, Dian Pan, Weikang Yang, Weili Miao, Jing Jia, Wanying Zheng, Baijun Liu, Yong Chen, Xinlin Gao, Ya Tian, Donghao World J Pediatr Surg Original Research 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. BMJ Publishing Group 2023-04-17 /pmc/articles/PMC10111925/ /pubmed/37082700 http://dx.doi.org/10.1136/wjps-2022-000547 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/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/. |
spellingShingle | Original Research Xu, Wenyao Yu, Hui Chen, Dian Pan, Weikang Yang, Weili Miao, Jing Jia, Wanying Zheng, Baijun Liu, Yong Chen, Xinlin Gao, Ya Tian, Donghao Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets |
title | Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets |
title_full | Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets |
title_fullStr | Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets |
title_full_unstemmed | Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets |
title_short | Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets |
title_sort | identifying the potential transcriptional regulatory network in hirschsprung disease by integrated analysis of microarray datasets |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111925/ https://www.ncbi.nlm.nih.gov/pubmed/37082700 http://dx.doi.org/10.1136/wjps-2022-000547 |
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