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Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses
BACKGROUND: Ulcerative colitis (UC) is an inflammatory bowel disease that is difficult to diagnose and treat. To date, the degree of inflammation in patients with UC has mainly been determined by measuring the levels of nonspecific indicators, such as C-reactive protein and the erythrocyte sedimenta...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584051/ https://www.ncbi.nlm.nih.gov/pubmed/33132649 http://dx.doi.org/10.3748/wjg.v26.i39.5983 |
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author | Shi, Lei Han, Xiao Li, Jun-Xiang Liao, Yu-Ting Kou, Fu-Shun Wang, Zhi-Bin Shi, Rui Zhao, Xing-Jie Sun, Zhong-Mei Hao, Yu |
author_facet | Shi, Lei Han, Xiao Li, Jun-Xiang Liao, Yu-Ting Kou, Fu-Shun Wang, Zhi-Bin Shi, Rui Zhao, Xing-Jie Sun, Zhong-Mei Hao, Yu |
author_sort | Shi, Lei |
collection | PubMed |
description | BACKGROUND: Ulcerative colitis (UC) is an inflammatory bowel disease that is difficult to diagnose and treat. To date, the degree of inflammation in patients with UC has mainly been determined by measuring the levels of nonspecific indicators, such as C-reactive protein and the erythrocyte sedimentation rate, but these indicators have an unsatisfactory specificity. In this study, we performed bioinformatics analysis using data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) databases and verified the selected core genes in a mouse model of dextran sulfate sodium (DSS)-induced colitis. AIM: To identify UC-related differentially expressed genes (DEGs) using a bioinformatics analysis and verify them in vivo and to identify novel biomarkers and the underlying mechanisms of UC. METHODS: Two microarray datasets from the NCBI-GEO database were used, and DEGs between patients with UC and healthy controls were analyzed using GEO2R and Venn diagrams. We annotated these genes based on their functions and signaling pathways, and then protein-protein interactions (PPIs) were identified using the Search Tool for the Retrieval of Interacting Genes. The data were further analyzed with Cytoscape software and the Molecular Complex Detection (MCODE) app. The core genes were selected and a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed. Finally, colitis model mice were established by administering DSS, and the top three core genes were verified in colitis mice using real-time polymerase chain reaction (PCR). RESULTS: One hundred and seventy-seven DEGs, 118 upregulated and 59 downregulated, were initially identified from the GEO2R analysis and predominantly participated in inflammation-related pathways. Seven clusters with close interactions in UC formed: Seventeen core genes were upregulated [C-X-C motif chemokine ligand 13 (CXCL13), C-X-C motif chemokine receptor 2 (CXCR2), CXCL9, CXCL5, C-C motif chemokine ligand 18, interleukin 1 beta, matrix metallopeptidase 9, CXCL3, formyl peptide receptor 1, complement component 3, CXCL8, CXCL1, CXCL10, CXCL2, CXCL6, CXCL11 and hydroxycarboxylic acid receptor 3] and one was downregulated [neuropeptide Y receptor Y1 (NYP1R)] in the top cluster according to the PPI and MCODE analyses. These genes were substantially enriched in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The top three core genes (CXCL13, NYP1R, and CXCR2) were selected and verified in a mouse model of colitis using real-time PCR Increased expression was observed compared with the control mice, but only CXCR2 expression was significantly different. CONCLUSION: Core DEGs identified in UC are related to inflammation and immunity inflammation, indicating that these reactions are core features of the pathogenesis of UC. CXCR2 may reflect the degree of inflammation in patients with UC. |
format | Online Article Text |
id | pubmed-7584051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-75840512020-10-30 Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses Shi, Lei Han, Xiao Li, Jun-Xiang Liao, Yu-Ting Kou, Fu-Shun Wang, Zhi-Bin Shi, Rui Zhao, Xing-Jie Sun, Zhong-Mei Hao, Yu World J Gastroenterol Basic Study BACKGROUND: Ulcerative colitis (UC) is an inflammatory bowel disease that is difficult to diagnose and treat. To date, the degree of inflammation in patients with UC has mainly been determined by measuring the levels of nonspecific indicators, such as C-reactive protein and the erythrocyte sedimentation rate, but these indicators have an unsatisfactory specificity. In this study, we performed bioinformatics analysis using data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) databases and verified the selected core genes in a mouse model of dextran sulfate sodium (DSS)-induced colitis. AIM: To identify UC-related differentially expressed genes (DEGs) using a bioinformatics analysis and verify them in vivo and to identify novel biomarkers and the underlying mechanisms of UC. METHODS: Two microarray datasets from the NCBI-GEO database were used, and DEGs between patients with UC and healthy controls were analyzed using GEO2R and Venn diagrams. We annotated these genes based on their functions and signaling pathways, and then protein-protein interactions (PPIs) were identified using the Search Tool for the Retrieval of Interacting Genes. The data were further analyzed with Cytoscape software and the Molecular Complex Detection (MCODE) app. The core genes were selected and a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed. Finally, colitis model mice were established by administering DSS, and the top three core genes were verified in colitis mice using real-time polymerase chain reaction (PCR). RESULTS: One hundred and seventy-seven DEGs, 118 upregulated and 59 downregulated, were initially identified from the GEO2R analysis and predominantly participated in inflammation-related pathways. Seven clusters with close interactions in UC formed: Seventeen core genes were upregulated [C-X-C motif chemokine ligand 13 (CXCL13), C-X-C motif chemokine receptor 2 (CXCR2), CXCL9, CXCL5, C-C motif chemokine ligand 18, interleukin 1 beta, matrix metallopeptidase 9, CXCL3, formyl peptide receptor 1, complement component 3, CXCL8, CXCL1, CXCL10, CXCL2, CXCL6, CXCL11 and hydroxycarboxylic acid receptor 3] and one was downregulated [neuropeptide Y receptor Y1 (NYP1R)] in the top cluster according to the PPI and MCODE analyses. These genes were substantially enriched in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The top three core genes (CXCL13, NYP1R, and CXCR2) were selected and verified in a mouse model of colitis using real-time PCR Increased expression was observed compared with the control mice, but only CXCR2 expression was significantly different. CONCLUSION: Core DEGs identified in UC are related to inflammation and immunity inflammation, indicating that these reactions are core features of the pathogenesis of UC. CXCR2 may reflect the degree of inflammation in patients with UC. Baishideng Publishing Group Inc 2020-10-21 2020-10-21 /pmc/articles/PMC7584051/ /pubmed/33132649 http://dx.doi.org/10.3748/wjg.v26.i39.5983 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
spellingShingle | Basic Study Shi, Lei Han, Xiao Li, Jun-Xiang Liao, Yu-Ting Kou, Fu-Shun Wang, Zhi-Bin Shi, Rui Zhao, Xing-Jie Sun, Zhong-Mei Hao, Yu Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses |
title | Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses |
title_full | Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses |
title_fullStr | Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses |
title_full_unstemmed | Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses |
title_short | Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses |
title_sort | identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses |
topic | Basic Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584051/ https://www.ncbi.nlm.nih.gov/pubmed/33132649 http://dx.doi.org/10.3748/wjg.v26.i39.5983 |
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