The survival rate of
pancreatic cancer patients is the lowest among those with common solid
tumors, and early detection is one of the most feasible means of improving outcomes. We compared plasma
proteomes between
pancreatic cancer patients and sex- and age-matched healthy controls using surface-enhanced
laser desorption/ionization coupled with hybrid quadrupole time-of-flight mass spectrometry. Proteomic spectra were generated from a total of 245 plasma samples obtained from two institutes. A discriminating proteomic pattern was extracted from a training cohort (71
pancreatic cancer patients and 71 healthy controls) using a support vector machine learning algorithm and was applied to two validation cohorts. We recognized a set of four mass peaks at 8,766, 17,272, 28,080, and 14,779 m/z, whose mean intensities differed significantly (Mann-Whitney U test, P < 0.01), as most accurately discriminating
cancer patients from healthy controls in the training cohort [sensitivity of 97.2% (69 of 71), specificity of 94.4% (67 of 71), and area under the curve value of 0.978]. This set discriminated
cancer patients in the first validation cohort with a sensitivity of 90.9% (30 of 33) and a specificity of 91.1% (41 of 45), and its discriminating capacity was further validated in an independent cohort at a second institution. When combined with CA19-9, 100% (29 of 29 patients) of
pancreatic cancers, including early-stage (stages I and II)
tumors, were detected. Although a multi-institutional large-scale study will be necessary to confirm clinical significance, the
biomarker set identified in this study may be applicable to using plasma samples to diagnose
pancreatic cancer.