Cargando…

Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis

Introduction: Crohn’s disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Yuxin, Cai, Daxing, Hu, Weitao, Fang, Taiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471261/
https://www.ncbi.nlm.nih.gov/pubmed/36118873
http://dx.doi.org/10.3389/fgene.2022.950136
_version_ 1784789032200306688
author Sun, Yuxin
Cai, Daxing
Hu, Weitao
Fang, Taiyong
author_facet Sun, Yuxin
Cai, Daxing
Hu, Weitao
Fang, Taiyong
author_sort Sun, Yuxin
collection PubMed
description Introduction: Crohn’s disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD. Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and CD tissues were identified using the GEO2R online tool. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were conducted using the clusterProfiler function in the R package. Protein-protein interaction network (PPI) analysis and visualization were performed with STRING and Cytoscape. Ten hub genes were identified using cytoHubba’s MCC algorithm and validated with datasets GSE6731 and GSE52746. Finally, the miRNA gene regulatory network was constructed by Cytoscape and NetworkAnalyst to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 97 DEGs were identified, consisting of 88 downregulated genes and 9 upregulated genes. The enriched functions and pathways of the DEGs include immune system process, response to stress, response to cytokine and extracellular region. KEGG pathway analysis indicates that the genes were significantly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway, Rheumatoid arthritis and TNF signaling pathway. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 10 hub genes including IL1B, CXCL8, CXCL10, CXCL1, CXCL2, CXCL5, ICAM1, IL1RN, TIMP1 and MMP3 were selected. Based on the DEG-miRNAs network construction, 5 miRNAs including hsa-mir-21-5p, hsa-mir-93-5p, hsa-mir-98-5p, hsa-mir-1-3p and hsa-mir-335-5p were identified as potential critical miRNAs. Conclusion: In conclusion, a total of 97 DEGs, 10 hub genes and 5 miRNAs that may be involved in the progression or occurrence of CD were identified in this study, which could be regarded as biomarkers of CD.
format Online
Article
Text
id pubmed-9471261
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-94712612022-09-15 Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis Sun, Yuxin Cai, Daxing Hu, Weitao Fang, Taiyong Front Genet Genetics Introduction: Crohn’s disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD. Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and CD tissues were identified using the GEO2R online tool. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were conducted using the clusterProfiler function in the R package. Protein-protein interaction network (PPI) analysis and visualization were performed with STRING and Cytoscape. Ten hub genes were identified using cytoHubba’s MCC algorithm and validated with datasets GSE6731 and GSE52746. Finally, the miRNA gene regulatory network was constructed by Cytoscape and NetworkAnalyst to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 97 DEGs were identified, consisting of 88 downregulated genes and 9 upregulated genes. The enriched functions and pathways of the DEGs include immune system process, response to stress, response to cytokine and extracellular region. KEGG pathway analysis indicates that the genes were significantly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway, Rheumatoid arthritis and TNF signaling pathway. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 10 hub genes including IL1B, CXCL8, CXCL10, CXCL1, CXCL2, CXCL5, ICAM1, IL1RN, TIMP1 and MMP3 were selected. Based on the DEG-miRNAs network construction, 5 miRNAs including hsa-mir-21-5p, hsa-mir-93-5p, hsa-mir-98-5p, hsa-mir-1-3p and hsa-mir-335-5p were identified as potential critical miRNAs. Conclusion: In conclusion, a total of 97 DEGs, 10 hub genes and 5 miRNAs that may be involved in the progression or occurrence of CD were identified in this study, which could be regarded as biomarkers of CD. Frontiers Media S.A. 2022-08-31 /pmc/articles/PMC9471261/ /pubmed/36118873 http://dx.doi.org/10.3389/fgene.2022.950136 Text en Copyright © 2022 Sun, Cai, Hu and Fang. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Genetics
Sun, Yuxin
Cai, Daxing
Hu, Weitao
Fang, Taiyong
Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis
title Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis
title_full Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis
title_fullStr Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis
title_full_unstemmed Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis
title_short Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis
title_sort identifying hub genes and mirnas in crohn’s disease by bioinformatics analysis
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471261/
https://www.ncbi.nlm.nih.gov/pubmed/36118873
http://dx.doi.org/10.3389/fgene.2022.950136
work_keys_str_mv AT sunyuxin identifyinghubgenesandmirnasincrohnsdiseasebybioinformaticsanalysis
AT caidaxing identifyinghubgenesandmirnasincrohnsdiseasebybioinformaticsanalysis
AT huweitao identifyinghubgenesandmirnasincrohnsdiseasebybioinformaticsanalysis
AT fangtaiyong identifyinghubgenesandmirnasincrohnsdiseasebybioinformaticsanalysis