Translational pediatrics · 2025
Comprehensive analysis of a lipid metabolism-related gene signature for ulcerative colitis.
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Yuan Linqing, Peng Kaiyue
Аннотация
BACKGROUND: Lipid metabolism is a critical factor in the inflammatory response and development of ulcerative colitis (UC). However, the diagnosis and treatment of UC remain obscure. The molecular mechanisms underlying UC remain unclear. This study aimed to identify efficacious biomarkers for the diagnosis and treatment of UC, and extend understandings of the pivotal molecular mechanisms related to lipid metabolism in the pathogenesis of UC.
METHODS: Datasets relating to UC were obtained from the Gene Expression Omnibus (GEO) database. Key lipid metabolism-related genes (LMGs) were identified by differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the LMGs. The cell infiltration by estimation of stromal and immune cells in cancer tissues (CIBERSORT) and xCell algorithms were used to examine immune infiltration. Single-cell RNA sequencing (scRNA-seq) was used to characterize the LMGs.
RESULTS: A total of 16 differentially expressed LMGs were identified from the tissue and blood samples of UC patients and healthy controls. The WGCNA and correlation analysis of the tumor microenvironments identified seven LMGs (i.e., MTMR2, ABCD3, IMPA1, NR3C2, ETNK1, ACADSB, and MINPP1). Subsequently, the machine learning and ROC curve analyses identified five hub LMGs (i.e., NR3C2, ABCD3, CD38, ALOX15, and PIGN). The scRNA-seq analysis validated the expression of the hub LMGs and revealed significant increases in the T cells and inflammatory cells in UC.
CONCLUSIONS: Our results suggest that the LMG signature may serve as a novel diagnostic tool for identifying patients with UC. Our machine-learning model may contribute to future research on the formulation of potential therapeutic strategies.
METHODS: Datasets relating to UC were obtained from the Gene Expression Omnibus (GEO) database. Key lipid metabolism-related genes (LMGs) were identified by differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the LMGs. The cell infiltration by estimation of stromal and immune cells in cancer tissues (CIBERSORT) and xCell algorithms were used to examine immune infiltration. Single-cell RNA sequencing (scRNA-seq) was used to characterize the LMGs.
RESULTS: A total of 16 differentially expressed LMGs were identified from the tissue and blood samples of UC patients and healthy controls. The WGCNA and correlation analysis of the tumor microenvironments identified seven LMGs (i.e., MTMR2, ABCD3, IMPA1, NR3C2, ETNK1, ACADSB, and MINPP1). Subsequently, the machine learning and ROC curve analyses identified five hub LMGs (i.e., NR3C2, ABCD3, CD38, ALOX15, and PIGN). The scRNA-seq analysis validated the expression of the hub LMGs and revealed significant increases in the T cells and inflammatory cells in UC.
CONCLUSIONS: Our results suggest that the LMG signature may serve as a novel diagnostic tool for identifying patients with UC. Our machine-learning model may contribute to future research on the formulation of potential therapeutic strategies.