Endocrine, metabolic & immune disorders drug targets · 2026
Identification of Hub Neutrophil Coexpressed Genes in Acute Pancreatitis via Machine Learning.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Guan Yuting, Xu Zudong, Liang Liuling, Deng Wentao, Qin Zhilin, Yang Jiaming, Qin Shanyu
Аннотация
BACKGROUND: Acute pancreatitis (AP) is a disease with high morbidity and mortality. Neutrophils are highly correlated with the occurrence and severity of the inflammatory response in AP. However, the mechanism by which neutrophils act on AP is currently unclear. We aimed to target genes coexpressed with neutrophils to identify new possibilities for diagnosing and treating AP.
METHODS: We analyzed the differences in immune infiltration in AP and the differential expression of neutrophil-coexpressed genes. Then, models were built through various machine learning methods, and the hub neutrophil coexpressed genes were identified. Receiver operating characteristic (ROC) curves were used for validation, and the relationships between the hub neutrophil coexpressed genes and immune infiltration were explored. GSEA and GSVA were used to investigate the biological functions of genes. Finally, we explored the miRNAs and lncRNAs associated with the hub neutrophil coexpressed genes.
RESULTS: Neutrophil infiltration varied significantly in samples from patients with AP. RGS2, AHNAK, and OGT were identified as hub neutrophil coexpressed genes by taking intersections of genes involved in the machine learning approach. The hub neutrophil coexpressed genes are closely associated with a variety of immune cells. GSVA revealed that the hub neutrophil coexpressed genes are differentially expressed in multiple immune- and inflammation-related pathways. These findings suggest that these genes may influence AP progression through these pathways.
CONCLUSIONS: In this study, genes coexpressed with neutrophils in AP were identified, and their biological functions were investigated. These findings may provide more effective therapeutic strategies for the prediction, prevention, and personalized treatment of patients with AP.
METHODS: We analyzed the differences in immune infiltration in AP and the differential expression of neutrophil-coexpressed genes. Then, models were built through various machine learning methods, and the hub neutrophil coexpressed genes were identified. Receiver operating characteristic (ROC) curves were used for validation, and the relationships between the hub neutrophil coexpressed genes and immune infiltration were explored. GSEA and GSVA were used to investigate the biological functions of genes. Finally, we explored the miRNAs and lncRNAs associated with the hub neutrophil coexpressed genes.
RESULTS: Neutrophil infiltration varied significantly in samples from patients with AP. RGS2, AHNAK, and OGT were identified as hub neutrophil coexpressed genes by taking intersections of genes involved in the machine learning approach. The hub neutrophil coexpressed genes are closely associated with a variety of immune cells. GSVA revealed that the hub neutrophil coexpressed genes are differentially expressed in multiple immune- and inflammation-related pathways. These findings suggest that these genes may influence AP progression through these pathways.
CONCLUSIONS: In this study, genes coexpressed with neutrophils in AP were identified, and their biological functions were investigated. These findings may provide more effective therapeutic strategies for the prediction, prevention, and personalized treatment of patients with AP.