Purpose: Early diagnosis is crucial to improve outcomes for pancreatic cancer patients (PC). The present study is designed to identify differently expressed peptides involved in PC as potential biomarkers.
Experimental design: The serum proteome of 22 PC patients, 12 pancreatitis patients (PP), and 45 healthy controls (HC) are analyzed using magnetic bead-based weak cation exchange (MB-WCX) and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). Next, a supervised neural network (SNN) algorithm model is established by ClinProTools and the candidate biomarker identified using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Finally, the candidate biomarker is validated in tissue samples.
Results: The SNN algorithm model discriminates PC from HC with 92.97% sensitivity and 94.55% specificity. Seventy-six differentially expressed peptides are identified, seven of which are significantly different among PC, PP, and HC (p < 0.05). Only one peak (m/z: 1466.99) tends to be upregulated in samples from HC, PP, and PC, which is identified as region of RNA-binding motif protein 6 (RBM6). In subsequent tissue analysis, it is verified that RBM6 expression is significantly higher in PC tissues than paracancerous tissue.
Conclusions and clinical relevance: The results indicate that RBM6 might serve as a candidate diagnostic biomarker for PC.
Clinical relevance: Methods used in this study could generate serum peptidome profiles of PC, PP, and HC, and present an approach to identify potential biomarkers for diagnosis of this malignancy.
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.