Bladder cancer (BCa) is one of the most common urothelial cancers with still noticeable incidence rate. Early detection of BCa is highly correlated with successful therapeutic outcomes. We previously showed that apurinic/apyrimidinic endonuclease 1/redox factor-1 (APE1/Ref-1) was expressed at an increased level in the serum of BCa patients when compared to the level in healthy controls. In this study, we investigated whether urinary APE1/Ref-1 was also elevated in patients with BCa. In this case-control study, voided urine was collected from 277 subjects including 169 BCa patients and 108 non-BCa controls. Urinary APE1/Ref-1 level was assessed by enzyme-linked immunosorbent assay (ELISA). APE1/Ref-1 levels were significantly elevated in BCa patients relative to levels in non-BCa controls and were correlated with tumor grade and stage. Urinary APE1/Ref-1 levels were also higher in patients with recurrence history of BCa. The receiver operating characteristics (ROC) curve of APE1/Ref-1 showed an area under the curve of 0.83, indicating the reliability and validity of this biomarker. The optimal combination of sensitivity and specificity was determined to be 82% and 80% at a cut-off value of 0.376 ng/100 μL for detection of APE1/Ref-1 in urine. In conclusion, urinary APE1/Ref-1 levels measured from noninvasively obtained body fluids would be clinically applicable for diagnosis of BCa.
BackgroundWe determined the differently expressed protein profiles and their functions in bladder cancer tissues with the aim of identifying possible target proteins and underlying molecular mechanisms for taking part in their progression.MethodsWe examined the expression of proteins by proteomic analysis and western blot in normal urothelium, non-muscle-invasive bladder cancers (NMIBCs), and muscle-invasive bladder cancers (MIBCs). The function of cofilin was analyzed using T24 human bladder cancer cells.ResultsThe expression levels of 12 proteins were altered between bladder cancers and normal bladder tissues. Of these proteins, 14-3-3σ was upregulated in both NMIBCs and MIBCs compared with controls. On the other hand, myosin regulatory light chain 2, galectin-1, lipid-binding AI, annexin V, transthyretin, CARD-inhibitor of NF-κB-activating ligand, and actin prepeptide were downregulated in cancer samples. Cofilin, an actin-depolymerizing factor, was prominent in both NMIBCs and MIBCs compared with normal bladder tissues. Furthermore, we confirmed that cofilin phosphorylation was more prominent in MIBCs than in NMIBCs using immunoblotting and immunohistochemcal analyses. Epidermal growth factor (EGF) increased the phosphorylation of cofilin and elevated the migration in T24 cells. Knockdown of cofilin expression with small interfering RNA attenuated the T24 cell migration in response to EGF.ConclusionsThese results demonstrate that the increased expression and phosphorylation of cofilin might play a role in the occurrence and invasiveness of bladder cancer. We suspected that changes in cofilin expression may participate in the progression of the bladder cancer.
Background: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods. Methods: We retrospectively reviewed the medical records of 358 patients who underwent SWL for urinary stone (kidney and upper-ureter stone) between 2015 and 2018 and evaluated the possible prognostic features, including patient population characteristics, urinary stone characteristics on a non-contrast, computed tomographic image. We performed 80% training set and 20% test set for the predictions of success and mainly used decision tree-based machine learning algorithms, such as random forest (RF), extreme gradient boosting trees (XGBoost), and light gradient boosting method (LightGBM).Results: In machine learning analysis, the prediction accuracies for stone-free were 86.0, 87.5, and 87.9%, and those for one-session success were 78.0, 77.4, and 77.0% using RF, XGBoost, and LightGBM, respectively. In predictions for stone-free, LightGBM yielded the best accuracy and RF yielded the best one in those for one-session success among those methods. The sensitivity and specificity values for machine learning analytics are (0.74 to 0.78 and 0.92 to 0.93) for stone-free and (0.79 to 0.81 and 0.74 to 0.75) for one-session success, respectively. The area under curve (AUC) values for machine learning analytics are (0.84 to 0.85) for stone-free and (0.77 to 0.78) for one-session success and their 95% confidence intervals (CIs) are (0.730 to 0.933) and (0.673 to 0.866) in average of methods, respectively. Conclusions: We applied a selected machine learning analysis to predict the result after treatment of SWL for urinary stone. About 88% accurate machine learning based predictive model was evaluated. The importance of machine learning algorithm can give matched insights to domain knowledge on effective and influential factors for SWL success outcomes.
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