Abstract
Background: Diabetes is a risk factor associated with pancreatic ductal adenocarcinoma (PDAC), and new adult-onset diabetes can be an early sign of pancreatic malignancy. Development of blood-based biomarkers to identify diabetic patients who warrant imaging tests for cancer detection may represent a realistic approach to facilitate earlier diagnosis of PDAC in a risk population. Methods: A spectral library-based proteomic platform was applied to interrogate biomarker candidates in plasma samples from clinically well-defined diabetic cohorts with and without PDAC. Random forest algorithm was used for prediction model building and receiver operating characteristic (ROC) curve analysis was applied to evaluate the prediction probability of potential biomarker panels. Results: Several biomarker panels were cross-validated in the context of detection of PDAC within a diabetic background. In combination with carbohydrate antigen 19-9 (CA19-9), the panel, which consisted of apolipoprotein A-IV (APOA4), monocyte differentiation antigen CD14 (CD14), tetranectin (CLEC3B), gelsolin (GSN), histidine-rich glycoprotein (HRG), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), plasma kallikrein (KLKB1), leucine-rich alpha-2-glycoprotein (LRG1), pigment epithelium-derived factor (SERPINF1), plasma protease C1 inhibitor (SERPING1), and metalloproteinase inhibitor 1 (TIMP1), demonstrated an area under curve (AUC) of 0.85 and a two-fold increase in detection accuracy compared to CA19-9 alone. The study further evaluated the correlations of protein candidates and their influences on the performance of biomarker panels. Conclusions: Proteomics-based multiplex biomarker panels improved the detection accuracy for diagnosis of early stage PDAC in diabetic patients.
Highlights
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that represents the majority of pancreatic cancer cases
For the full panel consisting of all 11 protein candidates (APOA4+CD14+CLEC3B+GSN+histidine-rich glycoprotein (HRG)+inhibitor heavy chain H3 (ITIH3)+KLKB1+LRG1+SERPING1+SERPINF1+TIMP1), without (APOA4+CD14+CLEC3B+GSN+HRG+ITIH3+KLKB1+LRG1+SERPING1+SERPINF1+TIMP1), and with the combination of carbohydrate antigen 19-9 (CA19-9), the LOO-area under curve (AUC) values were 0.81 and 0.85
All subjects from the 2 pilot cohorts gave their informed consent for inclusion before they participated in the studies
Summary
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that represents the majority of pancreatic cancer cases. PDAC has the lowest five-year relative survival rate (9%) compared to other cancer types [1]. Detection of PDAC may markedly improve the survival rate [1,2]. When PDAC is detected at early stages as localized disease, the five-year survival rate could be improved to 22% [3]. Detection of PDAC at early stages could represent an effective strategy to improve the survival rate of PDAC patients. Development of blood-based biomarkers to identify diabetic patients who warrant imaging tests for cancer detection may represent a realistic approach to facilitate earlier diagnosis of PDAC in a risk population. In combination with carbohydrate antigen 19-9 (CA19-9), the panel, which consisted of apolipoprotein A-IV (APOA4), monocyte differentiation antigen CD14
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