Basic Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 21, 2020; 26(39): 5983-5996
Published online Oct 21, 2020. doi: 10.3748/wjg.v26.i39.5983
Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses
Lei Shi, Xiao Han, Jun-Xiang Li, Yu-Ting Liao, Fu-Shun Kou, Zhi-Bin Wang, Rui Shi, Xing-Jie Zhao, Zhong-Mei Sun, Yu Hao
Lei Shi, Yu Hao, Department of Immunology and Microbiology, School of Life Sciences, Beijing University of Chinese Medicine, Beijing 100029, China
Xiao Han, Jun-Xiang Li, Fu-Shun Kou, Zhi-Bin Wang, Rui Shi, Xing-Jie Zhao, Zhong-Mei Sun, Gastroenterology Department, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China
Yu-Ting Liao, Department of Internal Medicine, Beijing Social Welfare Hospital, Beijing 100085, China
Author contributions: Shi L wrote the article and Han X edited it; Liao YT, Kou FS and Sun ZM performed the animal experiments; Wang ZB, Shi R and Zhao XJ analyzed the bioinformatics data; Hao Y and Li JX conducted the study.
Supported by Chinese Medicine Inheritance and Innovation “One Hundred Million” Talent Project Qihuang Scholar (to Li JX); The National Key R&D Program of China during the 13th Five-Year Plan Period, No. 2018YFC1705405; and The 66th China Postdoctoral Science Foundation, No. 2019M660575.
Institutional review board statement: The data of ulcerative colitis we analyzed in this study were all from the National Center for Biotechnology Information-Gene Expression Omnibus database. According to the guidelines approved by the National Center for Biotechnology Information-Gene Expression Omnibus, our study did not require the separate ethics committee approval.
Institutional animal care and use committee statement: We complied with the ethics standard for research activity established by the Animal Ethics Committee of Beijing University of Chinese Medicine in accordance with the guidelines issued by the Regulations of Beijing Laboratory Animal Management.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Data sharing statement: Database we used in this study can be shared from the National Center for Biotechnology Information-Gene Expression Omnibus databases.
ARRIVE guidelines statement: The authors have read the ARRIVE guidelines, and the manuscript was prepared and revised according to the ARRIVE guidelines.
Open-Access: 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:
Corresponding author: Yu Hao, PhD, Professor, Department of Immunology and Microbiology, School of Life Sciences, Beijing University of Chinese Medicine, No. 11 North Third Ring East Road, Chaoyang District, Beijing 100029, China.
Received: June 10, 2020
Peer-review started: June 10, 2020
First decision: August 22, 2020
Revised: August 30, 2020
Accepted: September 16, 2020
Article in press: September 16, 2020
Published online: October 21, 2020
Research background

Ulcerative colitis (UC) tends to occur in young and middle-aged people. It substantially affects the patient’s quality of life because it is difficult to cure, readily relapses and poses a high risk of colon cancer. However, the pathogenesis of UC is complex and multifaceted, and specific biomarkers for UC are currently unavailable.

Research motivation

In recent years, with the optimization of gene sequencing platforms, differentially expressed genes (DEGs) have been identified through bioinformatics analyses by comparing microarrays. To date, several studies have reported the results of bioinformatics analyses of samples from patients with inflammatory bowel disease using arrays or chips, but the analysis of patients with UC is still lacking. The specific molecules or biomarkers of UC are insufficient. Thus, we will apply bioinformatics methods to more clearly elucidate the underlying biomarkers and mechanisms of UC.

Research objectives

To identify UC-related DEGs by performing a bioinformatics analysis and verify them in vivo and to identify novel biomarkers and the underlying mechanisms of UC.

Research methods

Two microarray datasets from the National Center for Biotechnology Information-Gene Expression Omnibus database were used, and DEGs were analyzed using GEO2R and Venn diagrams. We annotated these genes based on functions and signaling pathways. Then protein-protein interaction (PPI) were constructed 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 the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was repeated. Finally, colitis model mice were established by administering dextran sulfate sodium, and the top three core genes were verified in colitis mice using real-time polymerase chain reaction.

Research results

One hundred and seventy-seven DEGs (118 upregulated genes and 59 downregulated genes) predominantly participated in inflammation-related pathways. Seventeen core genes were upregulated, and one gene was downregulated in the first cluster according to the PPI and MCODE analyses in Cytoscape. These genes were markedly enriched in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The top three core genes showed increased expression compared with the control mice, but only the difference in C-X-C motif chemokine receptor 2 (CXCR2) expression was statistically significant. CXCR2 may reflect the degree of inflammation in patients with UC and serve as an underlying treatment target.

Research conclusions

Core DEGs identified in patients with UC are related to inflammation and immune inflammatory reactions, indicating that these reactions are core features of the pathogenesis of UC. CXCR2 may reflect the degree of inflammation in patients with UC.

Research perspectives

CXCR2 may represent a new biomarker to determine the degree of inflammation or a treatment target in UC. In the future, the combination of CXCR2 with other biomarkers will potentially improve the ability to diagnose and dynamically monitor UC.