scholarly journals Elevated urinary levels of urokinase-type plasminogen activator receptor (uPAR) in pancreatic ductal adenocarcinoma identify a clinically high-risk group

BMC Cancer ◽  
2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Claudio Sorio ◽  
Andrea Mafficini ◽  
Federico Furlan ◽  
Stefano Barbi ◽  
Antonio Bonora ◽  
...  
BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e037267
Author(s):  
Dóra Illés ◽  
Emese Ivány ◽  
Gábor Holzinger ◽  
Klára Kosár ◽  
M Gordian Adam ◽  
...  

IntroductionPancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis with an overall 5-year survival of approximately 8%. The success in reducing the mortality rate of PDAC is related to the discovery of new therapeutic agents, and to a significant extent to the development of early detection and prevention programmes. Patients with new-onset diabetes mellitus (DM) represent a high-risk group for PDAC as they have an eightfold higher risk of PDAC than the general population. The proposed screening programme may allow the detection of PDAC in the early, operable stage. Diagnosing more patients in the curable stage might decrease the morbidity and mortality rates of PDAC and additionally reduce the burden of the healthcare.Methods and analysisThis is a prospective, multicentre observational cohort study. Patients ≥60 years old diagnosed with new-onset (≤6 months) diabetes will be included. Exclusion criteria are (1) Continuous alcohol abuse; (2) Chronic pancreatitis; (3) Previous pancreas operation/pancreatectomy; (4) Pregnancy; (5) Present malignant disease and (6) Type 1 DM. Follow-up visits are scheduled every 6 months for up to 36 months. Data collection is based on questionnaires. Clinical symptoms, body weight and fasting blood will be collected at each, carbohydrate antigen 19–9 and blood to biobank at every second visit. The blood samples will be processed to plasma and analysed with mass spectrometry (MS)-based metabolomics. The metabolomic data will be used for biomarker validation for early detection of PDAC in the high-risk group patients with new-onset diabetes. Patients with worrisome features will undergo MRI or endoscopic ultrasound investigation, and surgical referral depending on the radiological findings. One of the secondary end points is the incidence of PDAC in patients with newly diagnosed DM.Ethics and disseminationThe study has been approved by the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (41085-6/2019). We plan to disseminate the results to several members of the healthcare system includining medical doctors, dietitians, nurses, patients and so on. We plan to publish the results in a peer-reviewed high-quality journal for professionals. In addition, we also plan to publish it for lay readers in order to maximalise the dissemination and benefits of this trial.Trial registration numberClinicalTrials.gov NCT04164602


2021 ◽  
Author(s):  
YuHai Hu ◽  
YiPing Chen

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive, fatal tumor. N6-methylandenosine (m6A) methylation is the major epigenetic modification of RNA including lncRNAs. The roles of m6A-related lncRNAs in PDAC have not been fully clarified. The aim of this study is to assess gene signatures and prognostic value of m6A-related lncRNAs in PDAC. Methods The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset were explored to identify m6A-related lncRNAs. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to construct the m6A-related lncRNAs prognostic riskscore (m6A-LPR) model to predict the overall survival (OS) in the TCGA training cohort. Kaplan–Meier curve with log-rank test and receiver operating characteristic (ROC) curve were used to evaluate the prognostic value of the m6A-LPR. Furthermore, the robustness of the m6A-LPR was further validated in the ICGC cohort. Tumor immunity was evaluated using ESTIMATE and CIBERSORT algorithms. Results A total of 262 m6A-related lncRNAs were identified in two datasets. In the TCGA training cohort, 28 prognostic m6A-related lncRNAs were identified and the m6A-LPR including four m6A-related lncRNAs was constructed. The m6A-LPR was able to identify high-risk patients with significantly poorer OS and accurately predict OS in both the TCGA training cohort and the ICGC validation cohort. Analysis of tumor immunity revealed that high-risk group had remarkably lower stromal, immune, and ESTIMATE scores. Moreover, high-risk group was associated with significantly higher levels of plasma B cells and resting NK cells infiltration, and lower levels of infiltrating resting memory CD4 T cells, monocytes and resting mast cells. Conclusions Our study proposed a robust m6A-related prognostic signature of lncRNAs for predicting OS in PDAC, which provides some clues for further studies focusing on the mechanism process underlying m6A modification of lncRNAs.


2021 ◽  
Vol 1 (5) ◽  
pp. 399-409
Author(s):  
WATARU IZUMO ◽  
RYOTA HIGUCHI ◽  
TORU FURUKAWA ◽  
TAKEHISA YAZAWA ◽  
SHUICHIRO UEMURA ◽  
...  

Background: Gemcitabine together with nab-paclitaxel (GnP) has been shown to improve outcomes in patients with pancreatic ductal adenocarcinoma (PDAC). However, the predictive markers for treatment effects remain unclear. This study aimed to identify early prognostic factors in patients with PDAC receiving GnP. Patients and Methods: We analyzed 113 patients who received GnP for PDAC and evaluated the relationship between clinical factors and outcomes. Results: The median survival time (MST) was 1.2 years. In multivariate analysis, baseline carbohydrate antigen 19-9 (CA19-9) ≥747 U/ml [hazard ratio (HR)=1.9], baseline controlling nutrition status (CONUT) score ≥5 (HR=3.7) and changing rate of CA19-9 after two GnP cycles ≥0.69 (HR=3.7) were independent risk factors for poor prognosis. When examining outcomes according to pre-chemotherapeutic measurable factors (baseline CA19-9 and CONUT), the MSTs of patients with pre-chemotherapeutic zero risk factors (pre-low-risk group, n=63) and one or more risk factors (pre-high-risk group, n=50) were 1.7 and 0.65 years (p<0.001), respectively. The MST for those with a changing rate of CA19-9 after two GnP cycles <0.69 and ≥0.69 was significantly different in both groups (2.0 and 1.2 years in the pre-low-risk group, p<0.001; 1.0 and 0.52 years in the pre-high-risk group, p<0.001). Conclusion: These results may be useful for decision-making regarding treatment strategies in patients with PDAC receiving GnP.


2021 ◽  
Vol 12 ◽  
Author(s):  
Wenjing Song ◽  
Xin He ◽  
Pengju Gong ◽  
Yan Yang ◽  
Sirui Huang ◽  
...  

Objective: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Although progress has been made in the treatment of PDAC, its prognosis remains unsatisfactory. This study aimed to develop novel prognostic genes related to glycolysis in PDAC and to apply these genes to new risk stratification.Methods: In this study, based on the Cancer Genome Atlas (TCGA) PAAD cohort, the expression level of glycolysis-related gene at mRNA level in PAAD and its relationship with prognosis were analyzed. Non-negative matrix decomposition (NMF) clustering was used to cluster PDAC patients according to glycolytic genes. Prognostic glycolytic genes, screened by univariate Cox analysis and LASSO regression analysis were established to calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group and the low-risk group were analyzed, and the signal pathway was further enriched to analyze the correlation between glycolysis genes. In addition, based on RNA-seq data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC samples, and ESTIMATE was used to calculate the immune score of the samples.Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples were divided into two clusters by NMF cluster analysis. Survival analysis showed that PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with cluster 2 samples (P &lt; 0.001). A risk prediction model based on 11 glycolysis genes was constructed, according to which patients were divided into two groups, with significantly poorer prognosis in high-risk group than in low-risk group (P &lt; 0.001). Both internal validation and external dataset validation demonstrate good predictive ability of the model (AUC = 0.805, P &lt; 0.001; AUC = 0.763, P &lt; 0.001). Gene aggregation analysis showed that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk group showed immunosuppressed status and infiltrated by relatively more macrophages and less CD8+T cell.Conclusions: These findings suggested that the gene signature based on glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value for PDAC.


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