Abstract
BACKGROUND: Pancreatic cancer (PC) and biliary tract cancer (BTC) are highly aggressive cancers, characterized by their rarity, difficulty in diagnosis, and overall poor prognosis. Diagnosis of PC and BTC is complex and is made using a combination of appropriate clinical suspicion, imaging and endoscopic techniques, and cytopathological examination. However, the late-stage detection and poor prognosis of this tumor have led to an urgent need for biomarkers for early and/or predictive diagnosis and improved personalized treatments. WORKING HYPOTHESIS: There are two hypotheses for focusing on low-mass metabolites in the blood. First, valuable information can be obtained from the masses and relative amounts of such metabolites, which present as low-mass ions (LMIs) in mass spectra. Second, metabolic profiling of individuals may provide important information regarding biological changes in disease states that is useful for the early diagnosis of PC and BTC. MATERIALS AND METHODS: To assess whether profiling metabolites in serum can serve as a non-invasive screening tool for PC and BTC, 320 serum samples were obtained from patients with PC (n = 51), BTC (n = 39), colorectal cancer (CRC) (n = 100), and ovarian cancer (OVC) (n = 30), and from healthy control subjects (control) (n = 100). We obtained information on the relative amounts of metabolites, as LMIs, via triple time-of-flight mass spectrometry. All data were analyzed according to the peak area ratios of discriminative LMIs. RESULTS AND CONCLUSIONS: The levels of the 14 discriminative LMIs were higher in the PC and BTC groups than in the control, CRC and OVC groups, but only two LMIs discriminated between PC and BTC: lysophosphatidylcholine (LysoPC) (16:0) and LysoPC(20:4). The levels of these two LysoPCs were also slightly lower in the PC/BTC/CRC/OVC groups compared with the control group. Taken together, the data showed that metabolic profiling can precisely denote the status of cancer, and, thus, could be useful for screening. This study not only details efficient methods to identify discriminative LMIs for cancer screening but also provides an example of metabolic profiling for distinguishing PC from BTC. Furthermore, the two metabolites [LysoPC(16:0), LysoPC(20:4)] shown to discriminate these diseases are potentially useful when combined with other, previously identified protein or metabolic biomarkers for predictive, preventive and personalized medical approach.