Metabolomics is a powerful new technology that allows the assessment of global low-molecular-weight metabolites in a biological system and which shows great potential in biomarker discovery. Analysis of the key metabolites in body fluids has become an important part of improving the diagnosis, prognosis, and therapy of diseases. Hepatitis C virus (HCV) is a major leading cause of liver disease worldwide and a serious burden on public health. However, the lack of a small-animal model has hampered the analysis of HCV pathogenesis. We hypothesize that an animal model (Tupaia belangeri chinensis) of HCV would produce a unique characterization of metabolic phenotypes. Ultra-performance liquid-chromatography/electrospray ionization-SYNAPT-high-definition mass spectrometry (UPLC/ESI-SYNAPT-HDMS) coupled with pattern recognition methods and system analysis was carried out to obtain comprehensive metabolomics profiling and pathways of large biological data sets. Taurine, hypotaurine, ether lipid, glycerophospholipid, arachidonic acid, tryptophan, and primary bile acid metabolism pathways were acutely perturbed, and 38 differential metabolites were identified. More important, five metabolite markers were selected via the "significance analysis for microarrays" method as the most discriminant and interesting biomarkers that were effective for the diagnosis of HCV. Network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Integrated network analysis of the key metabolites yields highly related signaling pathways associated with the differentially expressed proteins, which suggests that the creation of new treatment paradigms targeting and activating these networks in their entirety, rather than single proteins, might be necessary for controlling and treating HCV efficiently.