TGFß2 mediates oxidative stress-induced epithelial-to-mesenchymal transition of bladder smooth muscle.

Bladder outlet obstruction (BOO) is the primary clinical manifestation of benign prostatic hyperplasia, the most common urinary system disease in elderly men, and leads to associated lower urinary tract symptoms. Although BOO is reportedly associated with increased systemic oxidative stress (OS), the underlying mechanism remains unclear. The elucidation of this mechanism is the primary aim of this study. A Sprague-Dawley rat model of BOO was constructed and used for urodynamic monitoring. The bladder tissue of rats was collected and subjected to real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR), histological examination, and immunohistochemical staining. Through bioinformatics prediction, we found that transforming growth factor ß2 (TGFß2) expression was upregulated in rats with BOO compared with normal bladder tissue. In vitro analyses using primary bladder smooth muscle cells (BSMCs) revealed that hydrogen peroxide (H2O2) induced TGFß2 expression. Moreover, H2O2 induced epithelial-to-mesenchymal transition (EMT) by reducing E-cadherin, an endothelial marker and CK-18, a cytokeratin maker, and increasing mesenchymal markers, including N-cadherin, vimentin, and α-smooth muscle actin (α-SMA) levels. The downregulation of TGFß2 expression in BSMCs using siRNA technology alleviated H2O2-induced changes in EMT marker expression. The findings of the study indicate that TGFß2 plays a crucial role in BOO by participating in OS-induced EMT in BSMCs.

Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study.

BACKGROUND: Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). METHODS: We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. RESULTS: Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. CONCLUSION: We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.

MicroRNA-20a promotes non-small cell lung cancer proliferation by upregulating PD-L1 by targeting PTEN.

Non-small cell lung cancer (NSCLC) remains one of the most common malignant tumors worldwide. The aim of the present study was to investigate the possibility of microRNA-20a (miR-20a) as a biomarker and therapeutic target for the diagnosis and treatment of NSCLC. Bioinformatics prediction, together with functional validation, confirmed miR-20a bound to programmed death ligand-1 (PD-L1) 3'-untranslated region to upregulate PD-L1 expression. Both miR-20a and PD-L1 could promote the proliferation of NSCLC cells. The expression level of PD-L1 was controlled by PTEN; however, further upstream regulation of PD-L1 expression was largely unknown. The present study showed that miR-20a could not restore the inhibition of PD-L1 expression levels by PTEN. Knockdown of PTEN expression upregulated the expression level of PD-L1 and promoted the proliferation of NSCLC cells. PTEN negatively regulated the Wnt/ß-catenin signaling pathway by inhibiting ß-catenin and Cyclin D1. Interestingly, PTEN could reverse miR-20a-mediated proliferation of NSCLC cells and the inhibitory effect was similar to that of XAV-939. miR-20a promotes the proliferation of NSCLC cells by inhibiting the expression level of PTEN and upregulating the expression level of PD-L1. It is suggested that miR-20a could be used as a biomarker and therapeutic target for the treatment of NSCLC.