Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses

Abstract

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-cy...

Limited Impact of 6-Mercaptopurine on Inflammation-Induced Chemokines Expression Profile in Primary Cultures of Enteric Nervous System

“…This assumption was confirmed by comparing our results to intestinal transcriptomic profiles of UC patients, supporting the rational to use ENS primary cultures to study chemokines activation under inflammatory stress. Most particularly, these results support the involvement of broad chemokine pathways in the inflammatory active phases of IBD and are in line with recent data of the literature based on the analysis of additional publicly available genome-wide data-sets of UC patients [39]. In support of this data, expression of Cxcl1 [9] and Cxcl2 [40,41] has also been shown to be increased in intestinal mucosal biopsies of IBD patients in additional independent studies.…”

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis

“…However, differences in measurement platforms, lab protocols, sample sizes, and operators render gene expression levels incomparable. In recent years, several studies based on microarray technology have been published to identify effective biomarkers in UC, most of which are prone to utilizing intersecting genes from different microarrays to perform analyses (Cheng et al, 2019(Cheng et al, , 2020Shi et al, 2020;Cao et al, 2021). As shown in Table 3, these methods can be applied to fewer datasets (≤3 datasets) because more datasets represent overly strict inclusion criteria, leading to fewer DEGs.…”

“…The expression of the six hub genes was confirmed in the DSS-induced UC mouse model. In published studies, some scholars screened diagnostic biomarkers for UC from the DEGs identified from a single dataset or an overlap of two or three datasets via bioinformatics analyses (Cheng et al, 2019(Cheng et al, , 2020Shi et al, 2020;Cao et al, 2021). In contrast, the advantage of this research lies in the larger dataset obtained by combining data from six GEO datasets, which increased the sample size and ensured the stability and relative reliability of the conclusions.…”

“…In recent years, several studies based on microarray technology have been published to identify effective biomarkers in UC (Cheng et al, 2019(Cheng et al, , 2020Shi et al, 2020;Cao et al, 2021). However, differences in measurement platforms, lab protocols, sample sizes, and operators render gene expression levels incomparable.…”

Efficacy and safety evaluation of hyperbaric oxygen therapy for patients with ulcerative colitis

“…[ 1 ] Symptoms of UC usually present with bloody mucopurulent stool, diarrhea, and bellyache, and they seriously impair the level of health for patients. [ 2 ] In addition, diagnosis of UC mainly relies on endoscopy and clinical symptoms. [ 3 ] It has been reported that the occurrence rate of UC is increasing globally.…”