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The incidence and mortality rate of biliary tract cancer have been increasing worldwide; however, its diagnosis and prognosis have not improved in recent years. A novel approach, termed 'metabolomics', may have the potential to be developed as an effective diagnostic tool. The present study prospectively obtained bile samples from 115 individuals, including 32 patients with biliary tract cancer, 61 patients with benign biliary tract diseases and 22 normal controls. A liquid chromatography/mass spectrometry (LC/MS)‑based approach was used to investigate the differences in bile samples between the three groups, followed by multivariate statistical analysis, which included partial least squares projection to latent structures with discriminant analysis (PLS‑DA) and orthogonal projection to latent structures with discriminant analysis (OPLS‑DA). The metabolomic 2D score plot and 3D plot revealed clear separation between the cancer, benign and normal control groups by PLS‑DA. To further address the significant difficulties in clinically differentiating between biliary tract cancer and benign biliary tract diseases, OPLS‑DA was performed to distinguish between the two disease groups and to select potential biomarkers. The cancer and benign groups were well differentiated. The metabolic analysis revealed significantly lower levels of lysophosphatidylcholine, phenylalanine, 2‑octenoylcarnitine, tryptophan and significantly higher levels of taurine‑ and glycine‑conjugated bile acids in the bile from patients with biliary tract cancer compared with those in the bile from patients with benign biliary tract disease. The present study suggested that an LC/MS‑based metabolomic investigation provides a potent and promising approach for discriminating biliary tract cancer from benign biliary tract diseases and the identified specific metabolites may offer potential as novel biomarkers for the early detection of biliary tract cancer.

Identification of bile biomarkers of biliary tract cancer through a liquid chromatography/mass spectrometry-based metabolomic method