Circulating multi-omic signature of disease severity and cholangiocarcinoma in primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease often associated with inflammatory bowel disease (IBD) that can progress to cirrhosis or cholangiocarcinoma (CCA), both carrying a poor prognosis. The mechanisms driving disease progression remain poorly understood. We aimed to identify circulating multi-omic signatures linked to PSC severity (alkaline phosphatase, bilirubin, fibrosis), IBD, and CCA to elucidate underlying biology and discover potential biomarkers for risk stratification and disease monitoring.

We quantified 737 proteins, 1083 metabolites, and 4573 miRNAs using plasma from 33 patients with PSC with various clinical presentations. We applied machine learning-driven analysis and network-based modeling for the data integration and analysis of associations. Parameters robustly identified in both methods were considered for tailored pathway enrichment analysis.

We identified circulating multi-omic profiles associated with PSC disease severity, IBD, and CCA. IBD-specific alterations were subtle; however, we noted associations between microbial-derived metabolites and colorectal cancer-related miRNAs. Severe PSC shared molecular features with PSC-CCA, indicating early activation of malignant pathways in PSC. Severe PSC was characterized by immune-interacting and epithelial-interacting proteins, bile acid and glutathione-related metabolites, and miRNAs regulating fibrosis, inflammation, extracellular matrix remodeling, and cell cycle control, overlapping with PSC-CCA. In established PSC-CCA, we observed coordinated alterations in oncogenic miRNAs, growth factor-related proteins, and depletion of sulfated phenolics and methylxanthines, reflecting extracellular matrix remodeling, inflammatory microenvironment reprogramming, and impaired hepatic detoxification. Overall, pathway enrichment analysis revealed an infection-like and cancer-associated signature defining PSC pathogenesis.

Circulating multi-omic profiles capture the key features of PSC severity and associated CCA. These findings provide mechanistic insights and identify potential biomarkers for cancer risk stratification and disease monitoring using PSC.