The biological characteristics of bladder cancer include enhanced invasion and migration, which are the main causes of death in patients. Starvation is a typical feature of the bladder cancer microenvironment and can induce autophagy. Autophagy has an important relationship with the invasion and migration of tumors. However, the role of autophagy in the invasion and migration of bladder cancer cells remains unclear. Hence, the aim of the current study was to clarify this role and underlying mechanism. In this study, we found that starvation enhanced the epithelial‐mesenchymal transition (EMT)‐mediated invasion and migration of T24 and 5637 cells while inducing autophagy. The inhibition of autophagy with chloroquine (CQ) or 3‐methyladenine (3MA) decreased EMT‐mediated invasion and migration. In addition, the expression of transforming growth factor 1 (TGF‐β1) and phosphorylated Smad3 (p‐Smad3) increased after starvation. The inhibition of autophagy with CQ or 3MA also decreased the expression of TGF‐β1 and p‐Smad3. The inhibitor of TGF‐β receptor sb431542 also inhibited the invasion, migration, and EMT of T24 and 5637 cells during starvation. Furthermore, recombinant TGF‐β1 induced autophagy and inhibition of the TGF‐β/Smad signaling pathway with sb431542 suppressed autophagy. In summary, our results suggested that autophagy promotes the invasion and migration of bladder cancer cells by inducing EMT through the TGF‐β1/Smad3 signaling pathway. Moreover, autophagy and TGF‐β1 can form a positive feedback loop to synergistically promote invasion and migration. Thus, our findings may provide a theoretical basis for the prevention of invasion and migration in bladder cancer.

Purpose: NUSAP1 has been reported to be involved in the progression of several types of cancer. However, its expression and exact role in bladder cancer (BLCA) remains elusive. The aim of this study was to determine the expression and role of NUSAP1 in BLCA. Methods: Tissue microarray, real-time PCR, Western blot and immunohistochemistry assays were carried out to determine NUSAP1 expression in BLCA tissues and cells. The biological roles of NUSAP1 were investigated using CCK-8, EdU labeling, flow cytometry, Transwell, and wound healing assays. Additionally, the effect of NUSAP1 on epithelialmesenchymal transition (EMT) was investigated by Western blotting and real-time PCR. Results: We found that NUSAP1 was upregulated in BLCA, and its expression was closely related to the poor prognosis of patients. Subsequently, we transfected 5637 and T24 cell lines with NUSAP1 siRNA and an NUSAP1 overexpression plasmid, respectively. NUSAP1 downregulation in 5637 cells inhibited cell proliferation, migration, and invasiveness and enhanced chemosensitivity to gemcitabine, while NUSAP1 overexpression in T24 cells resulted in the inverse effects. Moreover, NUSAP1 regulated EMT via the TGF-β signaling pathway, and when TGF-beta receptor 1 (TGFBR1) was inhibited with the inhibitor SB525334, the invasion and metastasis ability of BLCA cells was significantly suppressed, as well as p-Smad2/3 and vimentin expression. Conclusion: Our above data demonstrate that NUSAP1 contributes to BLCA progression via the TGF-β signaling pathway.

Background Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults. Methods We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model. Results The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur. Conclusions Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.

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Cystadenomas of the seminal vesicles are extremely rare. Here, we report a large seminal vesicle cystadenoma. A 37‐year‐old man presented a 6‐month history of haemospermia, 10 days of Lower Urinary Tract symptoms (LUTSs) and gross haematuria. Transabdominal ultrasonography, computed tomography and magnetic resonance imaging were performed and revealed a large solid‐cystic pelvic mass morphometrically measured 7.0 cm × 11.9 cm × 8.6 cm on the right seminal vesicle, which caused hydronephrosis of the right kidney. The prostate‐specific antigen of the patient was 27.860 ng/dl. Laparoscopic exploration found the capsule of tumour was complete and the tumour came from the right seminal vesicle, in addition, the mass had a certain space with the bladder and prostate, which could be separated. So a nerve‐sparing Laparoscopic Vesiculectomy was performed at last, even though the intraoperative frozen section analysis could not make sure the nature of the tumour either. The postoperative pathology revealed cystadenoma of the seminal vesicle.