Integrated strategy of differentially expressed genes associated with ulcerative colitis

The role of alcohol dehydrogenase 1C in regulating inflammatory responses in ulcerative colitis

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease caused by an interaction of genetics, immune responses, and environmental factors. However, the precise pathogenesis of UC remains poorly understood. The aim of the present study was to explore the potential target genes implicated in UC and to elucidate its underlying pathogenic mechanism. Firstly, three UC-associated microarray datasets (GSE75214, GSE87466, and GSE92517) were obtained to screen the differentially expressed genes (DEGs). As a result, alcohol dehydrogenase 1C (ADH1C) was the most significantly downregulated DEG in UC. We confirmed that ADH1C was downregulated in colonic tissue taken from patients with UC and colitis mice. Moreover, ADH1C expression was also decreased in colonic cell lines (NCM460 and HT29) treated with mixed proinflammatory cytokines (TNF-α, IFN-γ, IL-1β). Interestingly, we found that overexpression of ADH1C could distinctly decrease the production of IL-6 and IL-8. To elucidate the molecular mechanism of ADH1C in UC, gene set enrichment analysis (GSEA) was performed and demonstrated that immune-related pathways were mainly enriched in the low ADH1C group. Further studies displayed that STAT1/NF-κB pathway was activated in colitis mice and inflammatory cell model. Importantly, overexpression of ADH1C could suppress the phosphorylation of STAT1 and IκB. Therefore, ADH1C might regulate inflammatory responses through STAT1/NF-κB pathway. In conclusion, ADH1C was downregulated during inflammation, and its increased expression could inhibit the activation of STAT1/NF-κB pathway, thereby alleviating the secretion of IL-6 and IL-8. These findings indicate that ADH1C may be a potential therapeutic target for UC therapy.

Bioinformatics Analysis of Differentially Expressed Genes and Protein-Protein Interaction Networks Associated with Functional Pathways in Ulcerative Colitis

Background

This bioinformatics study aimed to identify differentially expressed genes (DEGs) and protein–protein interaction (PPI) networks associated with functional pathways in ulcerative colitis based on 3 Gene Expression Omnibus (GEO) datasets.

Material/Methods

The GSE87466, GSE75214, and GSE48958 MINiML formatted family files were downloaded from the GEO database. DEGs were identified from the 3 datasets, and volcano maps and heat maps were drawn after R language standardization and analysis, respectively. Venn diagram software was used to identify common DEGs. PPI analysis of common DEGs was performed using the Search Tool for the Retrieval of Interacting Genes. Gene modules and hub genes were visualized in the PPI network using Cytoscape. Enrichment analysis was performed for all common DEGs, module genes, and hub genes.

Results

A total of 90 DEGs were selected, which included 3 functional modules and 1 hub gene module. CXCL8 module genes were mainly enriched in cytokine-mediated signaling pathways and interleukin (IL)-10 signaling. CCL20 module genes were mainly enriched in the IL-17 signaling pathway and cellular response to IL-1. Hub gene modules mainly involved IL-10, IL-4, and IL-13 signaling pathways. CXCL8, CXCL1, and IL-1β were the top 3 hub genes and were mainly involved in IL-10 signaling.

Conclusions

Bioinformatics analysis using 3 GEO datasets identified CXCL8, CXCL1, and IL-1β, which are involved in IL-10 signaling, as the top 3 hub genes in ulcerative colitis. The findings from this study remain to be validated, but they may contribute to the further understanding of the pathogenesis of ulcerative colitis.

Identification of Differential Intestinal Mucosa Transcriptomic Biomarkers for Ulcerative Colitis by Bioinformatics Analysis

Background

Ulcerative colitis (UC) is a complicated disease caused by the interaction between genetic and environmental factors that affect mucosal homeostasis and triggers inappropriate immune response. The purpose of the study was to identify significant biomarkers with potential therapeutic targets and the underlying mechanisms.

Methods

The gene expression profiles of GSE48958, GSE73661, and GSE59071 are from the GEO database. Differentially expressed genes (DEGs) were screened by the GEO2R tool. Next, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was applied to analyze gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Then, protein-protein interaction (PPI) was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING).

Results

There were a total of 128 common DEGs genes, including 86 upregulated genes enriched in extracellular space, regulation of inflammatory response, chemokine-mediated signaling pathway, response to lipopolysaccharide, and cell proliferation, while 42 downregulated genes enriched in the integral component of the membrane, the integral component of the plasma membrane, apical plasma membrane, symporter activity, and chloride channel activity. The KEGG pathway analysis results demonstrated that DEGs were particularly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, pertussis, and rheumatoid arthritis. 18 central modules of the PPI networks were selected with Cytotype MCODE. Furthermore, 18 genes were found to significantly enrich in the extracellular space, inflammatory response, chemokine-mediated signaling pathway, TNF signaling pathway, regulation of cell proliferation, and immune response via reanalysis of DAVID.

Conclusion

The study identified DEGs, key target genes, functional pathways, and pathway analysis of UC, which may provide potential molecular targets and diagnostic biomarkers for UC.

Carboxypeptidase A6 was identified and validated as a novel potential biomarker for predicting the occurrence of active ulcerative colitis

Ulcerative colitis (UC) is a chronic, highly heterogeneous intestinal inflammation with changes in epithelial function and tissue damage. However, the pathogenesis is still unclear between active UC and inactive UC. Herein, weighted gene co‐expression network analysis was applied to explore the gene modules related to active UC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to further investigate the underlying mechanism of selected genes. We found that in the blue module (r = −.72), carboxypeptidase A6 (CPA6) was chosen to validate because of its high intra‐modular connectivity and module membership. In the test sets, the expression level of CPA6 was down‐regulated in active UC compared with inactive UC and normal colon. Furthermore, CPA6 expression was decreased primarily in the descending colon and only in mucosa affected by active UC. The receiver operating characteristic curve indicated that CPA6 expression had a performed well in diagnosing active UC from inactive UC (area under the curve = 0.99). Importantly, anti‐tumour necrosis factor (TNF) treatment (infliximab and golimumab) significantly increased the CPA6 expression. Finally, GSEA and GSVA found that extracellular matrix receptor, inflammatory response and epithelial‐mesenchymal transition were highly enriched in active UC with low CPA6 expression. In conclusion, CPA6 was identified and validated as a novel potential biomarker for predicting the occurrence of active UC, probably through regulating extracellular matrix or immune response.

Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis

Background

Ulcerative colitis is a type of inflammatory bowel disease posing a great threat to the public health worldwide. Previously, gene expression studies of mucosal colonic biopsies have provided some insight into the pathophysiological mechanisms in ulcerative colitis; however, the exact pathogenesis is unclear. The purpose of this study is to identify the most related genes and pathways of UC by bioinformatics, so as to reveal the core of the pathogenesis.

Methods

Genome-wide gene expression datasets involving ulcerative colitis patients were collected from gene expression omnibus database. To identify most close genes, an integrated analysis of gene expression signature was performed by employing robust rank aggregation method. We used weighted gene co-expression network analysis to explore the functional modules involved in ulcerative colitis pathogenesis. Besides, biological process and pathways analysis of co-expression modules were figured out by gene ontology enrichment analysis using Metascape.

Results

A total of 328 ulcerative colitis patients and 138 healthy controls were from 14 datasets. The 150 most significant differentially expressed genes are likely to include causative genes of disease, and further studies are needed to demonstrate this. Seven main functional modules were identified, which pathway enrichment analysis indicated were associated with many biological processes. Pathways such as ‘extracellular matrix, immune inflammatory response, cell cycle, material metabolism’ are consistent with the core mechanism of ulcerative colitis. However, ‘defense response to virus’ and ‘herpes simplex infection’ suggest that viral infection is one of the aetiological agents. Besides, ‘Signaling by Receptor Tyrosine Kinases’ and ‘pathway in cancer’ provide new clues for the study of the risk and process of ulcerative colitis cancerization.

Identification of differentially expressed genes, associated functional terms pathways, and candidate diagnostic biomarkers in inflammatory bowel diseases by bioinformatics analysis

Inflammatory bowel diseases (IBDs), including ulcerative colitis (UC) and Crohn's disease (CD), are chronic inflammatory disorders caused by genetic influences, the immune system and environmental factors. However, the underlying pathogenesis of IBDs and the pivotal molecular interactions remain to be fully elucidated. The aim of the present study was to identify genetic signatures in patients with IBDs and elucidate the potential molecular mechanisms underlying IBD subtypes. The gene expression profiles of the GSE75214 datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in UC and CD patients compared with controls using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs were performed using DAVID. Furthermore, protein-protein interaction (PPI) networks of the DEGs were constructed using Cytoscape software. Subsequently, significant modules were selected and the hub genes were identified. In the GO and KEGG pathway analysis, the top enriched pathways in UC and CD included Staphylococcus aureus infection, rheumatoid arthritis, complement and coagulation cascades, PI3K/Akt signaling pathway and osteoclast differentiation. In addition, the GO terms in the category biological process significantly enriched by these genes were inflammatory response, immune response, leukocyte migration, cell adhesion, response to molecules of bacterial origin and extracellular matrix (ECM) organization. However, several other biological processes (GO terms) and pathways (e.g., ‘chemotaxis’, ‘collagen catabolic process’ and ‘ECM-receptor interaction’) exhibited significant differences between the two subtypes of IBD. The top 10 hub genes were identified from the PPI network using respective DEGs. Of note, the hub genes G protein subunit gamma 11 (GNG11), G protein subunit beta 4 (GNB4), Angiotensinogen (AGT), Phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3) and C-C motif chemokine receptor 7 (CCR7) are disease-specific and may be used as biomarkers for differentiating UC from CD. Furthermore, module analysis further confirmed that common significant pathways involved in the pathogenesis of IBD subtypes were associated with chemokine-induced inflammation, innate immunity, adapted immunity and infectious microbes. In conclusion, the present study identified DEGs, key target genes, functional pathways and enrichment analysis of IBDs, enhancing the understanding of the pathogenesis of IBDs and also advancing the clarification of the underlying molecular mechanisms of UC and CD. Furthermore, these results may provide potential molecular targets and diagnostic biomarkers for UC and CD.