We herein reported a 27-year-old woman with a right renal mass for two years. She underwent laparoscopic partial nephrectomy. Immunohistochemical examination of the specimen confirmed the diagnosis of solitary fibrous tumor by revealing its positive staining for cluster of differentiation (CD)34, epithelial membrane antigen (EMA), B-cell lymphoma-2 (Bcl-2) and CD99 in the tumor cells. No adjuvant treatment was carried out. The patient was in good health without local recurrence or metastasis during 2 years of follow-up. Laparoscopic partial nephrectomy for renal solitary fibrous tumor is an alternative treatment to radical nephrectomy. It can provide a good outcome. However, further follow-up and more cases of renal solitary fibrous tumor treated with laparoscopic partial nephrectomy are necessary to compare the oncological outcome with radical nephrectomy.

Dielectric elastomer actuators (DEAs) are attracting much attention as candidates for next-generation flexible actuation. Among various DE matrices, polyacrylate rubber (AR) is especially promising owing to their intrinsically high dielectric constant (ε_r) and good mechanical performance. In particular, its mechanical behavior is close to that of porcine bladder tissue, making it a potentially good material for soft biomedical actuators for artificial bladder constructs. To achieve high actuated strain, which requires high ε_r, high breakdown strength, and low elastic modulus, an AR DE composite filled with silane-functionalized TiO₂ was fabricated, exhibiting good electromechanical performance enabled by strengthened interfacial polarization. To improve compatibility between TiO₂ and AR matrix, TiO₂ was preferentially modified with a silane coupling agent (CA) that features a double bond as its functional group, which can be introduced on TiO₂ surface and participate in vulcanization with AR, thereby forming co-crosslinking bridges that strengthen interfacial bonding, improve filler dispersion, and increase interfacial polarizability within the matrix. As a result, at relatively low filler loadings, the composite exhibits a significantly increased ε_r, while maintaining low modulus, low dielectric loss and high elasticity. The 10 CA@TiO₂/AR composite exhibits a maximal actuated strain of 7.9% at 31.9 kV/mm without pre-stretch, which is 1.48 times that of pure AR and 1.32 times that of the 10 TiO₂/AR composite.

Energy metabolism plays a pivotal role in cancer clinical treatment and has become an important means of clinical diagnosis of tumour progression. However, current research mostly focuses on changes in metabolic products and neglects the deeper mechanisms of transcriptional regulation. This paper proposes a new perspective, establishing a comprehensive network that reveals the interaction between metabolism and transcription, which explores how tumour metabolism affects tumour progression through transcriptional modifications, and provides a novel approach for optimizing tumour treatment strategies. This viewpoint is conducive to overcoming current bottlenecks in treatment and promoting the development of drug combinations and personalized medicine.

Background: Metastasis is the primary cause of mortality in patients with prostate cancer (PCa), yet effective treatments remain scarce. Identifying reliable biomarkers and understanding their underlying mechanisms is crucial for advancing clinical management. Methods: Firstly, we integrated single-cell and bulk transcriptomic data and employed the Scissor tool to characterize tumor cells with metastatic advantages (termed metastatic cells). Then, independent predictive genes for metastasis were identified through univariate and multivariate regression analyses. The role of hub genes in PCa metastasis was further validated using multiple large datasets, malignant phenotype experiments, in vivo metastatic models, and a clinical-sample-based immunohistochemical cohort. Further, we explored the metabolic characteristics related to hub genes through unbiased functional annotation, and validated the upregulated glycolysis by measuring l -lactic acid production, extracellular acidification rates (ECAR), and oxygen consumption rates (OCR). Finally, multi-omics data were employed to investigate the promoter-methylation-dependent regulation of alpha-2-glycoprotein 1 ( AZGP1 ) transcription, with methylation confirmed through PCa cell-based methylation-specific PCR (MSP) assays. Results: AZGP1 was identified as an independent protective predictor of metastasis, which was validated in vitro and in vivo. Metabolic functional annotation revealed that glycolysis was upregulated in AZGP1 -positive luminal cells. Consistently, overexpression of AZGP1 in PCa cells was associated with lower l -lactic acid levels, reduced ECAR, and increased OCR. In addition, DNA methylation at the cg26429636 region was linked to decreased transcriptional expression of AZGP1 . MSP assays revealed an unmethylated pattern in PCa cells with high AZGP1 expression, and higher methylation levels in AZGP1 -low cells. Conclusions: Promoter methylation of AZGP1 leads to reduced transcriptional expression, thereby promoting glycolysis in tumor cells and facilitating metastasis. The detection of AZGP1 methylation levels offers a valuable reference for dynamic surveillance of PCa metastasis.

Background: Delayed graft function (DGF) is a major complication of kidney transplantation that adversely affects long-term graft survival. This study aimed to develop and validate deep learning-based predictive models for DGF risk assessment in deceased donor kidney transplant recipients. Materials and methods: We retrospectively analyzed 670 consecutive patients who underwent deceased donor kidney transplantation at a single center between March 2018 and November 2023. The cohort was randomly divided into training (70%) and validation (30%) datasets. The class imbalance in the training set was addressed using a Synthetic Minority Oversampling Technique. Five deep learning algorithms were employed: bidirectional gated recurrent unit (BiGRU), Convolutional bidirectional long short-term memory, convolutional gated recurrent unit, convolutional neural network (CNN)-BiGRU, and CNN-bidirectional long short-term memory. The model performance was evaluated using receiver operating characteristic curve analysis with area under the curve (AUC), Matthews correlation coefficient, and F1 score metrics. Internal validation was performed using 1000 bootstrap iterations. Results: The study population comprised 670 deceased donor kidney transplant recipients with a mean age of 47.7 ± 11.2 years and a median preoperative serum creatinine of 907.1 (702.5–1113.8) μmol/L. The overall incidence of DGF was 21.8% (n = 146). Synthetic minority oversampling technique successfully addresses class imbalance in the training dataset. Among the 5 models evaluated, the CNN-BiGRU hybrid architecture demonstrated superior predictive performance with an AUC of 0.848 (95% confidence interval [CI] 0.798–0.899), Matthews correlation coefficient of 0.614 (95% CI, 0.609–0.619), and F1 score of 0.816 (95% CI, 0.789–0.843). The model achieved balanced discrimination, with 82.1% sensitivity and 83.5% specificity. Conclusions: The CNN-BiGRU-based prediction model demonstrated excellent performance in identifying patients at a high risk of DGF development. This artificial intelligence-powered tool offers the potential to assist the entire nephrology, urology, and transplant service communities in implementing personalized risk stratification and optimizing posttransplant management strategies to improve patient outcomes.

Background: Bladder cancer (BC) presents a major clinical challenge due to high recurrence and progression rates, highlighting the need for novel therapeutic targets. While the N6-methyladenosine (m6A) writer complex is broadly implicated, the specific function and regulatory mechanism of its adaptor protein RNA binding motif protein 15 (RBM15) remain poorly defined in BC. This study reveals the oncogenic role of RBM15 in BC and its m6A-dependent regulatory axis, providing a new rationale for targeted intervention. Methods: The clinical relevance of RBM15 was established by assessing its expression and prognostic significance in public datasets and a large clinical cohort. The biological function of RBM15 and its effect on global m6A methylation were subsequently investigated using a comprehensive suite of in vitro assays and in vivo models. We performed integrated multiomics analyses (RNA-seq, m6A-seq, and RIP-seq) and validated the underlying molecular mechanisms by performing additional targeted assays to elucidate the downstream regulatory network. Finally, the therapeutic potential of targeting this axis was validated in preclinical models using the METTL3 catalytic inhibitor STM2457. Results: RBM15 was significantly upregulated in BC, and its elevated expression served as an independent predictor of a poor prognosis. Functionally, RBM15 increased global m6A levels and promoted the malignant progression of BC cells both in vitro and in vivo; these oncogenic effects were reversed upon RBM15 knockdown. Mechanistically, RBM15 bound Zona Occludens 2 (ZO2) mRNA and recruited the methyltransferase 3–methyltransferase 14–Wilms’ tumor 1-associating protein (METTL3-METTL14-WTAP) methyltransferase complex to increase the level of the m6A modification on the ZO2 mRNA. This modification was recognized by YTH N(6)-methyladenosine RNA binding protein 2 (YTHDF2) to accelerate ZO2 mRNA decay. Although ZO2 expression was globally reduced, its preferential nuclear accumulation was increased, which promoted Snail expression and accelerated malignant progression. The METTL3 catalytic inhibitor STM2457 suppressed BC growth and lung metastasis by targeting the METTL3/RBM15/ZO2 axis, with no observable toxicity. Conclusions: RBM15 acts as an oncogenic driver in BC by facilitating the m6A-dependent degradation of ZO2 mRNA via the recruitment of the METTL3 complex and recognition by YTHDF2. Targeting this METTL3/RBM15/ZO2 axis with STM2457 represents a promising therapeutic strategy for BC.