Machine learning-based characterization of PANoptosis-related biomarkers and immune infiltration in ulcerative colitis: A comprehensive bioinformatics analysis and experimental validation

Abstract

Ulcerative colitis (UC) is a heterogeneous autoimmune condition. PANoptosis, a new form of programmed cell death, plays a role in inflammatory diseases. This study aimed to identify differentially expressed PANoptosis-related genes (PRGs) involved in immune dysregulation in UC. Three key PRGs-BIRC3, MAGED1, and PSME2 were found using weighted gene co-expression network analysis (WGCNA) and machine learning. Immune infiltration analysis revealed that these key PRGs were associated with neutrophils, CD8+ T cells, activated CD4 T cells, and NK cells. Moreover, these key PRGs were significantly enriched in pathways related to inflammatory bowel disease, the IL-17 signaling pathway, and NOD-like receptor signaling pathway. The expression levels of the key PRGs were validated in various datasets, animal models, and UC intestinal tissue samples. Our findings confirmed the involvement of PANoptosis in UC and predict hub genes and immune characteristics, providing new insights for further investigations into UC pathogenic mechanisms and therapeutic strategies.

Keywords: Bioinformatics analysis; Immune cell; Machin learning; PANoptosis; Ulcerative colitis.