Abstract
Objectives To reveal the molecular mechanisms of ulcerative colitis (UC) and provide potential biomarkers for UC gene therapy. Methods We downloaded the GSE87473 microarray dataset from the Gene Expression Omnibus (GEO) and identified the differentially expressed genes (DEGs) between UC samples and normal samples. Then, a module partition analysis was performed based on a weighted gene coexpression network analysis (WGCNA), followed by pathway and functional enrichment analyses. Furthermore, we investigated the hub genes. At last, data validation was performed to ensure the reliability of the hub genes. Results Between the UC group and normal group, 988 DEGs were investigated. The DEGs were clustered into 5 modules using WGCNA. These DEGs were mainly enriched in functions such as the immune response, the inflammatory response, and chemotaxis, and they were mainly enriched in KEGG pathways such as the cytokine-cytokine receptor interaction, chemokine signaling pathway, and complement and coagulation cascades. The hub genes, including dual oxidase maturation factor 2 (DUOXA2), serum amyloid A (SAA) 1 and SAA2, TNFAIP3-interacting protein 3 (TNIP3), C-X-C motif chemokine (CXCL1), solute carrier family 6 member 14 (SLC6A14), and complement decay-accelerating factor (CD antigen CD55), were revealed as potential tissue biomarkers for UC diagnosis or treatment. Conclusions This study provides supportive evidence that DUOXA2, A-SAA, TNIP3, CXCL1, SLC6A14, and CD55 might be used as potential biomarkers for tissue biopsy of UC, especially SLC6A14 and DUOXA2, which may be new targets for UC gene therapy. Moreover, the DUOX2/DUOXA2 and CXCL1/CXCR2 pathways might play an important role in the progression of UC through the chemokine signaling pathway and inflammatory response.
Highlights
Ulcerative colitis (UC) is a chronic nonspecific inflammation of the rectum and colon whose etiology and pathogenesis are not yet well defined [1]
We identified the differentially expressed genes (DEGs) between UC samples and normal samples. en, a module partition analysis was performed based on a weighted gene coexpression network analysis (WGCNA), followed by pathway and functional enrichment analyses. en, data validation was performed to ensure the reliability of the hub genes. is study forecasts the molecular mechanism of UC and the potential biomarkers for UC therapy
Hub Genes. e brown module was most relevant to the disease; we analyzed the correlation of gene expression in the brown module in the following study
Summary
Ulcerative colitis (UC) is a chronic nonspecific inflammation of the rectum and colon whose etiology and pathogenesis are not yet well defined [1]. E etiology of UC is considered to be multifactorial, including genetic and environmental factors such as urban lifestyles, dietary factors, high levels of hygiene, and gut microbiota, all of which are associated with disease progression; the pathogenesis of UC remains unclear [3]. Bioinformatics can be effectively used to analyze UC microarray data, providing theoretical reference for further exploration of the mechanisms of inflammatory bowel disease, and help to find potential target genes. As the latest bioinformatics research method, WGCNA is commonly used to reveal differences between genes in different samples [4]. UC gene expression data uploaded by Li et al were downloaded. En, data validation was performed to ensure the reliability of the hub genes. Is study forecasts the molecular mechanism of UC and the potential biomarkers for UC therapy We identified the DEGs between UC samples and normal samples. en, a module partition analysis was performed based on a WGCNA, followed by pathway and functional enrichment analyses. en, data validation was performed to ensure the reliability of the hub genes. is study forecasts the molecular mechanism of UC and the potential biomarkers for UC therapy
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