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 sys...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2023-04-01
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| Series: | World Journal of Pediatric Surgery |
| Online Access: | https://wjps.bmj.com/content/6/2/e000547.full |
| _version_ | 1827965652883734528 |
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| author | Yong Liu Ya Gao Hui Yu Xinlin Chen Dian Chen Weili Yang Wenyao Xu Weikang Pan Jing Miao Wanying Jia Baijun Zheng Donghao Tian |
| author_facet | Yong Liu Ya Gao Hui Yu Xinlin Chen Dian Chen Weili Yang Wenyao Xu Weikang Pan Jing Miao Wanying Jia Baijun Zheng Donghao Tian |
| author_sort | Yong Liu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T17:36:47Z |
| format | Article |
| id | doaj.art-db2fb5dbe2a54ee89ac512322c7f9f21 |
| institution | Directory Open Access Journal |
| issn | 2516-5410 |
| language | English |
| last_indexed | 2024-04-09T17:36:47Z |
| publishDate | 2023-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | World Journal of Pediatric Surgery |
| spelling | doaj.art-db2fb5dbe2a54ee89ac512322c7f9f212023-04-17T19:30:06ZengBMJ Publishing GroupWorld Journal of Pediatric Surgery2516-54102023-04-016210.1136/wjps-2022-000547Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasetsYong Liu0Ya Gao1Hui Yu2Xinlin Chen3Dian Chen4Weili Yang5Wenyao Xu6Weikang Pan7Jing Miao8Wanying Jia9Baijun Zheng10Donghao Tian112 Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China1 Evidence-Based Medicine Center, Lanzhou University, Lanzhou, Gansu, ChinaDepartment of Orthopedic Surgery, Hunan Children`s Hospital, Changsha, Hunan, ChinaDepartment of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Hubei, China1 Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaShanghai Mental Health Center,Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Pediatric Surgery, the Second Affiliated Hospital, Xi`an Jiaotong University, Xi`an, ChinaDepartment of Pediatric Surgery, the Second Affiliated Hospital, Xi`an Jiaotong University, Xi`an, ChinaDepartment of Pediatric Surgery, the Second Affiliated Hospital, Xi`an Jiaotong University, Xi`an, ChinaDepartment of Pediatric Surgery, the Second Affiliated Hospital, Xi`an Jiaotong University, Xi`an, ChinaDepartment of Pediatric Surgery, the Second Affiliated Hospital, Xi`an Jiaotong University, Xi`an, ChinaDepartment of Pediatric Surgery, the Second Affiliated Hospital, Xi`an Jiaotong University, Xi`an, ChinaObjective 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.https://wjps.bmj.com/content/6/2/e000547.full |
| spellingShingle | Yong Liu Ya Gao Hui Yu Xinlin Chen Dian Chen Weili Yang Wenyao Xu Weikang Pan Jing Miao Wanying Jia Baijun Zheng Donghao Tian Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets World Journal of Pediatric Surgery |
| 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 |
| url | https://wjps.bmj.com/content/6/2/e000547.full |
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