Objective To explore the relationship between Beclin-1 and the development of pancreatic ductal adenocarcinoma (PDAC). Methods ① Twenty-five PDAC specimens and 20 matched adjacent normal pancreatic tissues were obtained after radical surgery between April 2009 and November 2009 in West China Hospital of Sichuan University. Beclin-1 mRNA and protein expressions were examined by using real-time PCR and immunohistochemistry, respectively. Correlations between expressions of Beclin-1 protein with clinical data of PDAC patients were evaluated. ② PDAC cells were divided into 2 groups, cells of transfection group were transfected with PLenO-WPI-Beclin-1 vector, and cells of non-transfection group didn’t transfected with PLenO-WPI-Beclin-1 vector. Expressions levels of Beclin-1 mRNA in the 2 groups were detected by real-time PCR at 24 hours and 48 hours after transfection. ③ PDAC cells were divided into 3 groups, cells of transfection group were transfected with PLenO-WPI-Beclin-1 vector, cells of empty vector group transfected with PLenO-WPI, cells of blank control group didn’t accepted any vector. OD value was detected by MTT once a day during 1–7 days after transfection. Results ① Expression levels of Beclin-1 mRNA and its protein were significantly lower in PDAC tissue than those of adjacent normal pancreatic tissues (P<0.05). Increased Beclin-1 expression was associated with early TNM stage of Ⅰ and Ⅱ(P<0.05) and negative distant metastasis (P=0.011). ② At the same time point of 24 hours and 48 hours after transfection, the expression levels of Beclin-1 mRNA were higher in transfection group than those of non-transfection group (P<0.05). ③ MTT assay showed that PANC-1 cell proliferation ability was lower in the transfection group compared to the blank control group and empty vector groups in vitro on day 4–7 after transfection (P<0.05), but there was no significant in the cell proliferation ability among the 3 groups on day 1, 2, and 3 (P>0.05). Conclusions Down regulation of Beclin-1 and autophagy inhibition play an important role in the tumorigenesis and development of PDAC. Activating autophagy via overexpression of Beclin-1 may be a potential treatment for some PDACs and warrants further investigation.
Objective To summarize the diagnosis and treatment progress of borderline resectable pancreatic ductal adenocarcinoma (BR-PDAC) in recent years. Methods Through the retrieval of relevant literatures, the progress in the diagnosis and treatment of BR-PDAC in recent years were reviewed, to summarize the current status of definition, management, and outcome of BR-PDAC. Results Pancreatic surgery had significantly changed during the past years and resection approaches had been extended beyond standard procedures, including vascular and multivisceral resections. Consequently, BR-PDAC, which had recently been defined by the International Study Group for Pancreatic Surgery (ISGPS), had become a controversial issue with regard to its management in terms of upfront resection vs. neoadjuvant treatment and sequential resection. The key point was preoperative diagnostic accuracy to define the resectability of BR-PDAC and radical tumor resection followed by neoadjuvant treatment. Conclusion Surgery followed by neoadjuvant treatment is the only treatment option for BR-PDAC with the chance of long-term survival.
Objective To determine feasibility of texture analysis of CT images for the discrimination of nonhypervascular pancreatic neuroendocrine tumor (PNET) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 15 pathologically proved as PNETs and 30 PDACs in West China Hospital of Sichuan University from January 2009 to January 2017 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (18 parameters), run-length matrix (10 parameters), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (15 parameters), run-length matrix (10 parameters), histogram (1 parameter), absolute gradient (1 parameter), and autoregressive model (3 parameters). In AP and PVP, the parameter with the highest diagnostic performance were both Teta2, and the area under curve (AUC) value was 0.829 and 0.740 (P<0.001,P=0.009), respectively. By the B11 of MaZda, the misclassification rate of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 28.89% (13/45), 28.89% (13/45), 0 (0/45), and 4.44% (2/45), respectively. In PVP, the misclassification rate of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 35.56% (16/45), 33.33% (15/45), 4.44% (2/45), and 11.11% (5/45), respectively. Conclusions CT texture analysis is feasible in the discrimination of nonhypervascular PNET and PDAC. Teta2 is the parameter with the highest diagnostic performance, and in AP, LDA/KNN modality has the lowest misclassification rate.
Objective To determine the expression levels of micro RNA (miR)-196, miR-217, and transforming growth factor β receptor 1 (TGFβR1) protein in the pancreatic ductal adenocarcinoma tissues and its adjacent tissues, to reveal the relationship among them in the pathological process of pancreatic ductal adenocarcinoma. Methods A total of 30 cases’ pancreatic ductal adenocarcinoma tissues and its adjacent tissues were collected. The expression levels of miR-196b and miR-217 in the pancreatic ductal adenocarcinoma and adjacent tissues were detected by real-time fluorescence quantitative polymerase chain reaction method, the level of TGFβR1 protein was detected by Western blotting method. Results In the pancreatic ductal adenocarcinoma tissues, the expression levels of miR-196b and TGFβR1 protein were significantly higher than those of adjacent tissues (P<0.001), while the level of miR-217 was significantly lower than that of adjacent tissues (P=0.001). For further detection, the level of miR-196b in pancreatic ductal adenocarcinoma tissues was significantly positively correlated with the expression level of TGFβR1 protein (r=0.803, P<0.001), while the expression level of miR-217 was negatively correlated with the expression level of TGFβR1 protein (r=–0.839, P<0.001). Conclusions Expression TGFβR1 protein in pancreatic ductal adenocarcinoma tissues may be bi-directionally regulated by miR-196b and miR-217. This two-way regulating mechanism may be one of the important mechanisms for restricting the development of pancreatic ductal adenocarcinoma, implying a potential target for treatment of pancreatic cancer.
Objective To access the diagnostic performance of CT texture analysis to differentiate atypical pancreatic solid pseudopapillary tumor (SPT) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 26 patients with pathologically proved atypical SPT and 52 patients with PDAC were analyzed. 3D regions of interest (ROIs) on arterial phase (AP) and portal venous phase (PVP) images were drawn by ITK-Snap software. A.K. software (GE company, USA) was used to extract texture features for the discrimination of atypical SPT and PDAC. After removing redundancy (by a correlation analysis through R software), texture features were selected by single-factor and multi-factor logistic regression, and logistic regression model was finally established. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of single texture feature and logistic model. Results A total of 792 texture features ［396 of AP, 396 of PVP］ from AP and PVP CT images were obtained by A.K. software. Of these, 61 texture features (35 of AP, 26 of PVP) were selected by R software (result of correlation analysis showed that correlation coefficient >0.7). Two texture features, including MinIntensity and Correlation_AllDirection_offset1_SD, were selected to establish logistic model. The sensitivity and specificity of these 2 texture features were 71.15% and 76.92%, 63.46% and 76.92% respectively, the area under curve ( AUC) were 0.740 and 0.754 respectively. The model’s sensitivity and specificity were 73.08% and 80.77% respectively, the AUC value was 0.796. There was no significance among the model, MinIntensity, and Correlation_AllDirection_offset1_SD (P>0.05). Conclusions CT texture analysis of 3D ROI is a quantitative method for differential diagnosis of atypical SPT from PDAC.
Objective To explore CT features that can be used to identify nonhypervascular pancreatic neuroendocrine neoplasm (pNEN) and pancreatic ductal adenocarcinoma (PDAC). Methods The patients with pathologically confirmed the pNEN and PDAC were retrospectively included from May 2010 to May 2017. The CT features were analyzed. The CT features were extracted by the multivariate logistic regression, and their diagnostic performances were calculated. Results Forty patients with the nonhypervascular pNEN (33 unfunctional, 7 functional) and 80 patients with the PDAC were included in this study. The features of significant differences between the nonhypervascular pNEN and the PDAC included: the location, long diameter, margin, uniform lesions, calcification, and vascular shadows of the lesion (P<0.05). The margin ［OR=14.63, 95% CI (2.82, 75.99)］, calcification ［OR=4.00, 95% CI (1.03, 15.59)］, and location ［OR=3.09, 95% CI(1.19, 7.99)］ of the lesion could independently identify the nonhypervascular pNEN. The multivariate logistic regression model of the differential diagnosis of the nonhypervascular pNEN and PDAC was obtained through the CT features of significant differences. The diagnostic sensitivity was 70.00%, 95% CI (53.5,83.4); specificity was 83.54%, 95% CI (73.5, 90.9); and area under the receiver operating curve was 0.824, 95% CI (0.743, 0.887). Conclusions Multivariate logistic regression model of CT features is helpful for differential diagnosis of nonhypervascular pNEN and PDAC. Features of margin and calcification of lesion are more valuable in differential diagnosis of nonhypervascular pNEN and PDAC.
Autoimmune pancreatitis (AIP) is a unique subtype of chronic pancreatitis, which shares many clinical presentations with pancreatic ductal adenocarcinoma (PDA). The misdiagnosis of AIP often leads to unnecessary pancreatic resection. 18F-FDG positron emission tomography/ computed tomography (PET/CT) could provide comprehensive information on the morphology, density, and functional metabolism of the pancreas at the same time. It has been proved to be a promising modality for noninvasive differentiation between AIP and PDA. However, there is a lack of clinical analysis of PET/CT image texture features. Difficulty still remains in differentiating AIP and PDA based on commonly used diagnostic methods. Therefore, this paper studied the differentiation of AIP and PDA based on multi-modality texture features. We utilized multiple feature extraction algorithms to extract the texture features from CT and PET images at first. Then, the Fisher criterion and sequence forward floating selection algorithm (SFFS) combined with support vector machine (SVM) was employed to select the optimal multi-modality feature subset. Finally, the SVM classifier was used to differentiate AIP from PDA. The results prove that texture analysis of lesions helps to achieve accurate differentiation of AIP and PDA.
ObjectiveTo summarize the relationship between integrins, tumor metabolism, and tumor cells with pancreatic stellate cells in the tumor microenvironment, in order to provide targets and ideas for the treatment of pancreatic ductal adenocarcinoma.MethodTo review the literatures on pancreatic stellate cells, integrins, and amino acid metabolism as therapeutic targets for pancreatic ductal adenocarcinoma in the domestic and overseas.ResultsThe drug research for pancreatic ductal adenocarcinoma was currently under vigorous development, but remain in the animal and clinical test stage. As a new therapeutic protein, ProAgio could inhibit the expression of integrin αvβ3, activation and secretion of pancreatic stellate cells, and alanine metabolism in the microenvironment of pancreatic ductal adenocarcinoma, so as to achieve the dual effects of anti-fibrosis and anti-tumor.ConclusionsThe roles of activated pancreatic stellate cells, ProAgio, integrin αvβ3, and alanine metabolism in pancreatic ductal adenocarcinoma have been partially elucidated, but the specific mechanism still needs further investigation and may become a completely new therapeutic target someday.
ObjectiveTo optimize the culture method of human primary pancreatic ductal adenocarcinoma (PDAC) cells and cancer associated fibroblasts (CAFs) and investigate the effect of CAFs on the growth of primary PDAC cells in vitro and tumor formation in patient-derived xenograft (PDX) model.MethodsThe PDAC specimens were collected and primarily cultured. In order to observe the effect of CAFs on the growth of primary PDAC cells in vitro, the CAFs were co-cultured with primary PDAC cells consistently and the alone cultured primary PDAC cells served as the control. Then, these cells were injected into the shoulder blades of NOG mice in order to develop the PDX model.ResultsWhen the primary PDAC cells separated from the CAFs, the proliferation capacity of the primary PDAC decreased rapidly in the passage culture in vitro, and the most cells were terminated within 5 generations. By contrast, when the CAFs co-cultured with the primary PDAC cells, the proliferation capacity of primary PDAC cells were preserved, which could be stably transferred to at least 10 generations. The tumors of NOG mice were detected during 2–3 weeks after injecting the mixed cells (primary PDAC plus CAFs), while had no tumor formation after injecting CAFs alone. The rate of tumor was 92.9% (13 cases) in the primary PDAC plus CAFs group, which was higher than that of the CAFs alone group (64.3%, 9 cases), but there was no statistical difference because of the small sample size. The volume of tumor in the primary PDAC plus CAFs group at 2, 4, 6, and 8 weeks after the tumor cells injection was significantly larger than that in the CAFs alone group at the corresponding time point, the differences were statistically significant (P<0.01).ConclusionsThe CAFs could promote the growth of primary PDAC cells in vitro. This new method of co-culture CAFs with primary PDAC could improve the success rate of primary PDAC cells culture and improve the success rate of PDX model in NOG mice.
ObjectiveTo explore the factors of affecting the prognosis of pancreatic ductal adenocarcinoma (PDAC) after radical resection based on the preoperative systemic immune-inflammation index (SII) and the controlling nutritional status (CONUT) score and to establish a prognostic prediction model.MethodsThe clinicopathologic data of patients diagnosed with PDAC from January 2014 to December 2019 in the Second Hospital of Lanzhou University were retrospectively analyzed. The X-tile software was used to determine the optimal cut-off value of SII. The Kaplan-Meier method was used to analyze survival. The Cox proportional hazards regression model was used to conduct multivariate analysis of prognostic factors of PDAC after radical surgery. R4.0.5 software was used to draw a nomogram prediction model of 1-, 2-, and 3-year survival rates, then evaluate the effectiveness of the prediction model and establish a web page calculator.ResultsA total of 131 patients were included in the study. The median survival time was 18.6 months, and the cumulative survival rates at 1-, 2-, and 3-year were 73.86%, 36.44%, and 11.95%, respectively. The optimal cut-off value of preoperative SII was 313.1, and the prognosis of patients with SII>313.1 was worse than SII≤313.1 (χ2=8.917, P=0.003). The results of multivariate analysis suggested that the age>65 years old, clinical stage Ⅲ and Ⅳ, preoperative SII>313.1, and CONUT score >4 were the independent factors influencing the prognosis (overall survival) for PDAC after radical resection (P<0.05). The internal verification consistency index (C-index) of the nomogram prediction model including age, clinical stage, preoperative SII, CONUT score and postoperative chemotherapy was 0.669. The survival predicted by the nomogram correction curve fitted well with the observed survival. The decision curve analysis showed that the nomogram prediction model had a wider clinical net benefit (Threshold probability was 0.05–0.95), and the web calculator worked well.ConclusionsAge, clinical stage, preoperative SII, CONUT score are independent influencing factors for prognosis after radical PDAC surgery. Nomogram prediction model included these independent influencing factors is more accurate and web calculator will be more convenient for doctors and patients.