Journal of inflammation research · 2025
Comprehensive Analysis of Selenium Metabolism and Selenoproteins-Associated Gene Signatures in Ulcerative Colitis.
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Gao Chang, Wang Jiaojiao, Dai Siqi, Wang Danning, Wang Jianwei
Аннотация
BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory bowel disease marked by persistent mucosal inflammation. Oxidative stress plays a vital role in UC pathogenesis. Selenium (Se), an essential trace element, functions via selenoproteins and metabolites. Selenocysteine (Sec), the 21st amino acid, is incorporated into selenoproteins with strong antioxidant and anti-inflammatory properties. However, the role of selenium metabolism and selenoproteins (SeMet) in UC remains poorly understood.
METHODS: Gene expression and clinical data from UC patients and healthy controls were obtained from the GEO database. SeMet-related gene sets were collected from Molecular Signatures Database (MSigDB). Weighted gene co-expression network analysis (WGCNA) and differential expression analysis identified key modules and genes associated with UC. Machine learning algorithms were used to screen signature genes and construct a UC risk prediction model. Single-cell RNA sequencing (scRNA-seq) was performed to examine gene expression at the cellular level. Expression of differentially regulated genes (DRGs) and signature genes was validated using quantitative polymerase chain reaction (qPCR), Western blotting, and immunohistochemistry (IHC).
RESULTS: DRGs were significantly upregulated in UC and used to classify 161 UC samples into two subtypes. Six candidate signature genes were identified by integrating WGCNA and machine learning, showing high diagnostic potential and inter-correlation. scRNA-seq revealed upregulation of many selenoproteins in epithelial cells and downregulation of SELENOP in immune cells. The six signature genes were consistently upregulated across multiple cell types. WARS1 (tryptophanyl-tRNA synthetase 1), one of the signature genes, responded strongly to oxidative stress, and its knockdown elevated inflammatory cytokine levels.
CONCLUSION: SeMet-related genes are crucial in UC pathogenesis, particularly through antioxidant defense and immune modulation. The identified six-gene signature offers promising diagnostic and therapeutic potential for UC.
METHODS: Gene expression and clinical data from UC patients and healthy controls were obtained from the GEO database. SeMet-related gene sets were collected from Molecular Signatures Database (MSigDB). Weighted gene co-expression network analysis (WGCNA) and differential expression analysis identified key modules and genes associated with UC. Machine learning algorithms were used to screen signature genes and construct a UC risk prediction model. Single-cell RNA sequencing (scRNA-seq) was performed to examine gene expression at the cellular level. Expression of differentially regulated genes (DRGs) and signature genes was validated using quantitative polymerase chain reaction (qPCR), Western blotting, and immunohistochemistry (IHC).
RESULTS: DRGs were significantly upregulated in UC and used to classify 161 UC samples into two subtypes. Six candidate signature genes were identified by integrating WGCNA and machine learning, showing high diagnostic potential and inter-correlation. scRNA-seq revealed upregulation of many selenoproteins in epithelial cells and downregulation of SELENOP in immune cells. The six signature genes were consistently upregulated across multiple cell types. WARS1 (tryptophanyl-tRNA synthetase 1), one of the signature genes, responded strongly to oxidative stress, and its knockdown elevated inflammatory cytokine levels.
CONCLUSION: SeMet-related genes are crucial in UC pathogenesis, particularly through antioxidant defense and immune modulation. The identified six-gene signature offers promising diagnostic and therapeutic potential for UC.