BACKGROUND: Ulcerative colitis (UC) is a chronic nonspecific inflammatory intestinal disease affecting the mucosa and submucosa, characterized by continuous and diffuse active inflammation. However, its underlying pathogenesis remains unclear. OBJECTIVE: This study aimed to identify potential UC biomarkers by integrating weighted gene co-expression network analysis (WGCNA) with machine learning, followed by validation in an experimental UC mouse model. METHODS: The Gene Expression Omnibus database was systematically queried, and the GSE87466 dataset, comprising of colonic tissues from 87 patients with UC and 21 healthy controls, was retrieved. Differentially expressed genes (DEGs) were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. WGCNA was used to extract UC-related DEGs. Two machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen potential biomarkers. These biomarkers were then validated using animal experiments. RESULTS: A total of 1,097 DEGs were identified. WGCNA constructed nine co-expression gene modules, with the turquoise module (520 genes) exhibiting the highest relevance to UC. LASSO and SVM-RFE analysis identified poly(ADP-ribose) polymerase family member 8 (PARP8) as a potential biomarker of UC. Immunological analysis revealed significantly higher proportions of naive B cells, activated CD4(+) memory T cells, follicular helper T cells, γδT cells, M0 macrophages, M1 macrophages, activated mast cells, and neutrophils in UC samples compared to controls. PARP8 expression positively correlated with neutrophils, M1 macrophages, and activated CD4(+) T cells, but negatively correlated with plasma cells. In vivo validation confirmed elevated PARP8 expression in dextran sulfate sodium-induced UC mice compared to controls. CONCLUSION: PARP8 may contribute to UC pathogenesis via immune-related pathways and holds promise as a diagnostic and predictive biomarker.