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Radical surgical resection represents the only hope of cure for pancreatic neuroendocrine tumor (PanNET). Adjuvant therapy is rarely used because there is no evidence to distinguish patients with high recurrence risk. Here we investigated the recurrence feature of resected PanNET and established a novel risk stratification to predict its recurrence. We analyzed 505 PanNET patients who underwent R0 resection at our institute from January 2004 through May 2015. The median follow-up was 71months (range: 12months-143months), 129 patients (25.5%) experienced recurrence with median disease-free survival (mDFS) of 19months. Restricted cubic spline (RCS) functions revealed a positive, linear relationship between Ki-67 index and recurrence. Multivariate analysis showed T stage, N stage, insulinoma and Ki-67 index were independent predictors of recurrence (P < 0.05). Based on scores of these independent factors, we generated a recurrent-risk stage system with HCI of 0.806, superior to TNM stage (HCI 0.704) and grading system (HCI 0.706). Resected PanNET were classified into low risk (65.3%, mDFS not reached), intermediate risk (16.6%, mDFS 48months, 95%CI 26.5-73.4), high risk (13.3%, mDFS 24months, 95%CI 19.4-50.5) and very high risk (4.8%, mDFS 10months, 95%CI 6.9-13.0) (Hazard ratio: 2.650, 95%CI: 2.233-3.145, P < 0.001). This novel risk stratification thus identified PanNET patients of different recurrent-risk. Patients with very high recurrence risk may be suitable for post-operative clinical trials investigating adjuvant treatment., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Pancreatic cancer is an aggressive and devastating disease that is characterized by uncontrolled progression, invasiveness and resistance to conventional treatment. In the past decades, much effort has been given to cancer genetics and pathological classification of this disease. Our previous study has uncovered a subgroup of patients with poor outcome, which is characterized by serum signature of CEA(+)/CA125(+)/CA19-9 ≥ 1000 U/mL; however, the underlying biology mechanism remains poorly understood. By using high-throughput screening analysis, we analyzed gene expression signature in highly malignant patients with serum markers of CEA(+)/CA125(+)/CA19-9 ≥ 1000 U/mL. Multiple differentially expressed genes were identified, many of which were closely related with cancer metabolic changes. Treatment of pancreatic cancer cell lines PANC-1 with transforming growth factor-β (TGF-β), which was commonly used to induce metastasis, has uncovered that the glycolytic process and antioxidant response was up-regulated upon TGF-β stimulation. These results were consistent with the high-throughput screening analysis. Subsequent analysis indicated that among glycolytic genes, aldolase A (ALDOA) increased the most significantly upon TGF-β treatment. Further in vitro and in vivo results demonstrated that ALDOA was associated with proliferation and metastasis of pancreatic cancer cells. Moreover, ALDOA predicted poor prognosis of pancreatic cancer, partially due to its role in E-cadherin expression regulation, and the results were further validated by analysis of the correlation between ALDOA and E-cadherin expression in pancreatic cancer tissue samples. Mechanistically, the role of ALDOA in pancreatic cancer might attribute to its regulation of c-Myc, HIF1α and NRF2 (Nuclear Factor, Erythroid 2-Like 2), which were key regulators of glycolysis and antioxidant response control., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Pancreatic cancer is currently one of the deadliest solid malignancies and pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer. In the past decade, diagnostics and surgical techniques for PDAC have been evolving steadily; however, clinical outcomes of patients with PDAC have shown little, if any, improvement. Subgroup classification based on accurate prediction of prognosis in patients with pancreatic cancer is important for treatment selection and clinical decision-making. The traditional method to evaluate prognosis relies on the TNM staging system, but it may not reflect the true status of every patient due to individual biological differences. Metabolomics is a field of study that involves the identification and quantification of metabolites present in a biological system. Analysis of metabolic differences between cancerous and noncancerous tissues can provide novel insights into tumor biology that are closely associated with disease prognosis and diagnosis. Therefore, evaluation of metabolic tumor burden may improve the accuracy of the clinical decision-making process, thereby facilitating optimization of the treatment strategies for pancreatic cancer., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)