BackgroundPancreatic ductal adenocarcinoma is an aggressive malignancy with late presentation, metastatic potential and very poor prognosis. Therefore, there is an urgent need for novel diagnostic and prognostic biomarkers. MicroRNAs are small non-coding RNAs that post-transcriptionally regulate gene expression. Altered expression of microRNAs has been reported in wide range of malignancies, including pancreatic ductal adenocarcinoma. The aim of this study was to analyze the expression of selected microRNAs in normal pancreas, chronic pancreatitis and pancreatic ductal adenocarcinoma tissues and evaluate their diagnostic and prognostic potential.FindingsUsing quantitative real-time PCR, expression levels of 4 microRNAs were examined in 74 tumor tissues, 18 tissues of chronic pancreatitis and 9 adjacent normal tissues and correlated with clinicopathological features of patients. Expression levels of miR-21, miR-34a and miR-198 were significantly higher, whereas levels of miR-217 were significantly lower in pancreatic ductal adenocarcinomas compared to healthy tissues and tissues of chronic pancreatitis. Moreover, increased expression of miR-21 and miR-198 was significantly associated with shorter disease free survival and overall survival.ConclusionsOur data suggest that altered expression of examined microRNAs is related to neoplastic transformation and progression of the disease and these microRNAs could serve as diagnostic and prognostic biomarkers for pancreatic ductal adenocarcinoma.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1373952531543898

Background Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for

Background: The current model used to preoperatively stratify endometrial cancer (EC) patients into low- and high-risk groups is based on histotype, grade, and imaging method and is not optimal. Our study aims to prove whether a new model incorporating immunohistochemical markers, L1CAM, ER, PR, p53, obtained from preoperative biopsy could help refine stratification and thus the choice of adequate surgical extent and appropriate adjuvant treatment. Materials and Methods: The following data were prospectively collected from patients operated for EC from January 2016 through August 2018: age, pre- and post-operative histology, grade, lymphovascular space invasion, L1CAM, ER, PR, p53, imaging parameters obtained from ultrasound, CT chest/abdomen, final FIGO stage, and current decision model (based on histology, grade, imaging method). Results: In total, 132 patients were enrolled. The current model revealed 48% sensitivity and 89% specificity for high-risk group determination. In myometrial invasion >50%, lower levels of ER ( p = 0.024), PR (0.048), and higher levels of L1CAM ( p = 0.001) were observed; in cervical involvement a higher expression of L1CAM ( p = 0.001), lower PR ( p = 0.014); in tumors with positive LVSI, higher L1CAM ( p = 0.014); in cases with positive LN, lower expression of ER/PR ( p < 0.001), higher L1CAM ( p = 0.002) and frequent mutation of p53 ( p = 0.008). Cut-offs for determination of high-risk tumors were established: ER <78% ( p = 0.001), PR <88% ( p = 0.008), and L1CAM ≥4% ( p < 0.001). The positive predictive values (PPV) for ER, PR, and L1CAM were 87% (60.8–96.5%), 63% (52.1–72.8%), 83% (70.5–90.8%); the negative predictive values (NPV) for each marker were as follows: 59% (54.5–63.4%), 65% (55.6–74.0%), and 77% (67.3–84.2%). Mutation of p53 revealed PPV 94% (67.4–99.1%) and NPV 61% (56.1–66.3%). When immunohistochemical markers were included into the current diagnostic model, sensitivity improved (48.4 vs. 75.8%, p < 0.001). PPV was similar for both methods, while NPV (i.e., the probability of extremely low risk in negative test cases) was improved (66 vs. 78.9%, p < 0.001). Conclusion: We proved superiority of new proposed model using immunohistochemical markers over standard clinical practice and that new proposed model increases accuracy of prognosis prediction. We propose wider implementation and validation of the proposed model.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.