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Background &Aims: Pancreatic adenocarcinoma is a most devastating cancer that presents late and is rapidly progressive. This study aimed to identify unique, tissue- specific protein biomarkers capable of differentiating pancreatic adenocarcinoma (PC) from adjacent uninvolved pancreatic tissue (AP), benign pancreatic disease (B), and nonmalignant tumor tissue (NM). Methods: Tissue samples representing PC (n = 31), AP (n = 44), and B (n = 19) tissue were analyzed on hydrophobic protein chip arrays by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Training models were developed using logistic regression and validated using the 10-fold cross-validation approach. Results: The hydro- phobic protein chip array revealed 13 protein peaks differentially expressed between PC and AP (receiver operating characteristic [ROC] area under the curve [AUC], 0.64-0.85), 8 between PC and B (ROC AUC, 0.67-0.78), and 12 between PC and NM tissue (ROC AUC, 0.63-0.81). Logistic regression and cross-validation identified overlapping panels of peaks to develop a training model that distinguished PC from AP (77.4%sensitivity, 84.1%specificity), B (83.9%sensitivity, 78.9%specificity), and NM tissue (58.1%sensitivity, 90.5%specificity). The final panels selected correctly classified 80.6%of PC and 88.6%of AP samples (ROC AUC, 0.92), 93.5%of PC and 89.5%of B samples (ROC AUC, 0.99), and 71.0%of PC and 92.1%of NM samples (ROC AUC, 0.91). Conclusions: This study used surface-enhanced laser desorption/ionization time-of- flight mass spectrometry to discover a number of protein panels that can distinguish effectively between pancreatic adenocarcinoma, benign, and adjacent pancreatic tissue. Identification of these proteins will add to our understanding of the biology of pancreatic cancer. Furthermore, these protein panels may have important diagnostic implications.
Background & Aims: Pancreatic adenocarcinoma is a most devastating cancer that presents late and is rapidly progressive. This study aimed to identify unique, tissue-specific protein biomarkers capable of differentiating pancreatic adenocarcinoma (PC) from adjacent uninvolved pancreatic tissue (AP), benign pancreatic disease Methods: Tissue samples representing PC (n = 31), AP (n = 44), and B (n = 19) tissues were analyzed on hydrophobic protein chip arrays by surface- enhanced laser desorption / ionization time-of-flight mass spectrometry. Training models were developed using logistic regression and validated using the 10-fold cross-validation approach. Results: The hydro- phobic protein chip array revealed 13 protein peaks differentially expressed between PC and AP (receiver operating characteristic [ROC] area under the curve [AUC], 0.64-0.85), 8 between PC and B (ROC AUC, 0.67-0.78), and 12 between PC and NM tissue (ROC AUC, 0.63-0.81). Logistic r egression and cross-validation identified overlapping panels of peaks to develop a training model that distinguished PC from AP (77.4% sensitivity, 84.1% specificity), B (83.9% sensitivity, 78.9% specificity), and NM tissue % specificity). The final panels were correctly classified 80.6% of PC and 88.6% of AP samples (ROC AUC, 0.92), 93.5% of PC and 89.5% of B samples (ROC AUC, 0.99), and 71.0% of PC and 92.1% of NM samples (ROC AUC, 0.91). Conclusions: This study used surface-enhanced laser desorption / ionization time-of-flight mass spectrometry to discover a number of protein panels that can be advantageously effectively between pancreatic adenocarcinoma, benign, and adjacent Identification of these proteins will add to our understanding of the biology of pancreatic cancer. Furthermore, these protein panels may have important diagnostic implications.