Abstract
It is difficult to directly obtain pathological diagnosis of perihilar cholangiocarcinoma (pCCA). Analysis of bile in the pCCA microenvironment, based on metabolomics and statistical methods, can help in clinical diagnosis. Clinical information, bile samples, blood liver function, blood CA199, CEA, and other indicators were collected from 33 patients with pCCA and 16 patients with gallstones. Bile samples were analyzed using untargeted metabolomics methods. A combination of multivariate and univariate analyses were used to screen for potential differential metabolites Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and differential metabolite remodeling, we explored changes in the pCCA pathway and potential therapeutic targets. There were significant differences in patient blood TBIL, ALT, AST, TBA, CA19-9, and CEA indices (p < 0.05, |log2(fc)| ≥ 1) between two groups. A significant correlation was found between these different indicators by Spearman's analysis. The clinical parameters were correlated with mass-to-charge ratios of 305 (Positive Ion Mode, POS) and 246 (Negative Ion Mode, NEG) in the metabolic group (|r| ≥ 0.7, P ≤ 10(-7)). The result of this study indicated that bile untargeted metabolomics combined with statistical analysis techniques may be used for diagnose and treatment of pCCA.