The potential mechanism of ursolic acid in the treatment of bladder cancer based on network pharmacology and molecular docking.

OBJECTIVE: This study explored the potential molecular mechanisms of ursolic acid (UA) in bladder cancer treatment using network pharmacology and molecular docking. METHODS: The Traditional Chinese Medicine Systems Pharmacology and UniProt databases were used to screen potential targets of UA. Relevant bladder cancer target genes were extracted using the GeneCards database. All data were pooled and intercrossed to obtain common target genes of UA and bladder cancer. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. Molecular docking was conducted to verify the possible binding conformation between UA and bladder cancer cells. Then, in vitro experiments were performed to further validate the predicted results. RESULTS: UA exerts anti-tumor effects on bladder cancer through multiple targets and pathways. Molecular docking indicated that UA undergoes stable binding with the proteins encoded by the top six core genes (STAT3, VEGFA, CASP3, TP53, IL1B, and CCND1). The in vitro experiments verified that UA can induce bladder cancer cell apoptosis by regulating the PI3K/Akt signaling pathway. CONCLUSIONS: Our study illustrated the potential mechanism of UA in bladder cancer based on network pharmacology and molecular docking. The results will provide scientific references for follow-up studies and clinical treatment.

Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.

OBJECTIVES: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.