PurposeApurinic/apyrimidinic endonuclease 1/redox factor-1 (APE1/Ref-1) is a multifunctional protein that shows elevated expression in a number of cancers. We attempted to determine whether serum APE1/Ref-1 is elevated in patients with bladder cancer.Materials and MethodsSerum APE1/Ref-1 levels were determined using enzyme-linked immunosorbent assay in serum from patients with bladder cancer who had not received chemotherapy or radiotherapy (n=51) and non-tumor controls (n=55). The area under the receiver operating characteristic area under the curve was applied to determine the correlation between clinical factors and the serum levels of APE1/Ref-1.ResultsSerum levels of APE1/Ref-1 in bladder cancer patients were significantly elevated compared to those of the control group (3.548±0.333 ng/100 μL [n=51] for bladder cancer vs. 1.547±0.319 ng/100 μL [n=55] for the control group), with a sensitivity and specificity of 93% and 59%, respectively. Serum APE1/Ref-1 levels are associated with tumor stage, grade, muscle invasion, and recurrence.ConclusionSerum APE1/Ref-1 might be useful as a potential serologic biomarker for bladder cancer.

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.

There is growing interest in identifying regulators of autophagy. The molecular mechanism underlying transforming growth factor-β activated kinase 1 (TAK1)-induced autophagy is poorly understood. We found that TAK1 inhibits p70 S6 kinase1 (S6K1) phosphorylation by interfering interaction of raptor with S6K1, thus inducing autophagy. The factors that determine whether autophagy is cytoprotective or cytotoxic have not been fully elucidated. In Drosophila, TAK1 overexpression leads to an impaired eye phenotype despite inhibition of apoptosis, indicating that the phenotype was mainly due to autophagy. Also, TAK1 overexpression increases lactate dehydrogenase (LDH) level in mammalian cells. When treated with autophagy inhibitors, the level of TAK1-induced cytotoxicity or cell death was significantly attenuated, indicating that TAK1 induces cytotoxic autophagic cell death. This study provides the first in vitro and in vivo evidence of TAK1-induced autophagy and we believe that our findings significantly contribute to the understanding of the mechanisms underlying the induction of autophagy.

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We investigated effects of Neuregulin 1 (NRG1) on the expression of nicotinic acetylcholine receptor (nAChR) in major pelvic ganglion (MPG) from adult rat. MPG neurons were found to express transcripts for type I and III NRG1s as well as α and β-type epidermal growth factor (EGF)-like domains. Of the four ErbB receptor isoforms, ErbB1, ErbB2, and ErbB3 were expressed in MPG neurons. Treating MPG with NRG1β significantly increased the transcript and protein level of the nAChR α3 and β4 subunits. Consistent with these molecular data, nicotinic currents (I(ACh) ) were significantly up-regulated in NRG1β-treated sympathetic and parasympathetic MPG neurons. In contrast, the type III NRG1 and the α form of the NRG1 failed to alter the I(ACh) . Inhibition of the ErbB2 tyrosine kinase completely abolished the effects of NRG1β on the I(ACh) . Stimulation of the ErbB receptors by NRG1β activated the phosphatidylinositol-3-kinase (PI3K) and mitogen-activated protein kinase (MAPK). Immunoblot analysis revealed that PI3K-mediated activation of Akt preceded Erk1/2 activation in NRG1β-treated MPG neurons. Furthermore, specific PI3K inhibitors abrogated the phosphorylation of Erk1/2, while inhibition of MEK did not prevent the phosphorylation of Akt. Taken together, these findings suggest that NRG1 up-regulates nAChR expression via the ErbB2/ErbB3-PI3K-MAPK signaling cascade and may be involved in maintaining the ACh-mediated synaptic transmission in adult autonomic ganglia.

Purpose:The expression of Nitric oxide Synthase (NOS) and aquaporin (AQP) water channels in rat bladder is recently reported. The aim of this study is to evaluate the expression of inducible NOS (iNOS), aquaporin-3 (AQP-3) in cyclophosphamide (CYP) induced rat bladder. Materials and Methods:The 32 Sprague-Dawley rats were divided into cystitis group (n=20) and control group (n=12). In cystitis group, 100mg/kg CYP was injected every second day for 1 week whereas in control group, normal saline was injected. After extracting of the bladder and dividing dome, body and trigone of the bladder, independently H&E staining and immunohistochemical staining for iNOS and AQP-3 were performed. Expressions of iNOS and AQP-3 were analyzed with a confocal laser scanning microscope and an image analyzer. Results: The expression of iNOS significantly increased in the mucosa, submucosa layer of dome in cystitis group (p<0.05). The expression of AQP-3 significantly increased in the mucosa, submucosa, vessel layer of dome in cystitis group (p<0.05). Conclusions: These results suggest that inflammatory change activates NOS and AQP-3 expression in the bladder tissue of rats. These may imply that NOS and AQP-3 have a pathophyiological role in the cyclophophamide induced interstitial cystitis. Further study on the NOS and AQP-3 in bladder is needed for clinical application.

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.