BACKGROUND: Ulcerative colitis (UC) is an intestinal disease characterized by long-term inflammation. Circadian rhythm disorder (CRD) affects various biological activities and has been linked to several diseases, including UC. This study aimed to investigate the role and significance of CRD in UC.
METHODS: Bulk RNA-seq data from five independent UC cohorts were obtained from the Gene Expression Omnibus (GEO) database and integrated into a single dataset. The dataset underwent differential analysis to identify differentially expressed genes (DEGs) in association with CRD. Expression levels and pathway enrichment of CRD genes were analyzed, and signature genes were identified using machine learning algorithms. Based on these signature genes, a UC risk prediction model and CRD-related molecular subtypes were established. Furthermore, single-cell RNA-seq data of UC were analyzed to discuss the key role of CRD and signature genes in the UC microenvironment. RT-PCR analysis was employed to validate the expression levels of the identified signature genes.
RESULTS: 247 DEGs associated with CRD in UC were identified (referred to as CRD-DEGs). Gene set enrichment analysis (GSEA) revealed a strong association between CRD and inflammation, as well as immune cell infiltration in UC. This association potentially impacts intestinal fibrosis. A comparison of three machine learning algorithms (Lasso, SVM-RFE, and Random Forest) resulted in the identification of 12 signature genes. A UC risk prediction model and two UC CRD subtypes were developed using these genes. Among them, STXBP1 was identified by all three machine learning algorithms and was further analyzed. STXBP1 was predominantly enriched in pathways related to inflammatory response. Elevated levels of STXBP1 are mainly caused by reduced levels of methylation of its gene promoter. RT-PCR confirmed elevated expression of certain genes in mouse UC models.
CONCLUSIONS: This study is the first to establish a strong association between CRD and the onset of UC. The newly developed UC nomogram based on CRD demonstrated high predictive accuracy, although further clinical validation is required. Understanding the intrinsic relationship between CRD and UC enhances our understanding of the potential pathogenesis of UC. This study introduces novel ideas and methods for early diagnosis, treatment, and prognosis of UC.
