The classification of immunophenotypes in muscle-invasive bladder cancer (MIBC) is critical for predicting immunotherapy response and clinical outcomes, yet current assessment methods lack standardization and scalability. We developed and validated an artificial intelligence-based MIBC Immunophenotype Diagnostic System using computational pathology to enable reproducible classification from routine hematoxylin and eosin-stained whole-slide images. In this multicenter retrospective diagnostic study, consecutive patients who underwent partial or radical cystectomy between 2014 and 2024 from two Chinese hospitals and The Cancer Genome Atlas cohort were included, with an independent cohort receiving immune checkpoint inhibitors for treatment efficacy evaluation. The system integrates Hover-Net-based nuclear classification with cell structure graph networks to model spatial cellular interactions within the tumor microenvironment. Across external validation cohorts, the model achieved macro-area under the curve values of 0.922-0.956 and macro-accuracy of 0.922-0.950, demonstrating robust generalizability. In a human-AI collaboration study, the system outperformed junior and senior pathologists and significantly improved junior pathologists' diagnostic accuracy while reducing review time. Predicted Inflamed tumors exhibited enriched CD8+ T-cell infiltration, elevated checkpoint gene expression, and stronger correlation with immunotherapy response. These findings support clinical translation for precision immuno-oncology in bladder cancer.

BACKGROUND:Lymphovascular invasion (LVI) is a well-established adverse prognostic factor in prostate cancer (PCa). This study aimed to develop and validate an artificial intelligence (AI)-based framework leveraging multi-instance learning (MIL) and foundation models for accurate and interpretable prediction of LVI in prostate cancer using whole-slide images (WSIs). METHODS:A weakly supervised deep-learning pipeline based on the clustering-constrained attention MIL framework was implemented to analyze hematoxylin and eosin (H&E)-stained WSIs from two independent cohorts: 280 patients from Renmin Hospital of Wuhan University (RHWU) and 340 patients from The Cancer Genome Atlas (TCGA). Feature extraction was performed using pretrained encoders including UNI-v2, CONCH, and ResNet-50. Attention heatmaps were used to interpret model focus, whereas biologic correlates of model predictions were explored through differential expression analysis and gene ontology (GO) enrichment. RESULTS:The proposed models achieved strong predictive performance, with UNI-v2 outperforming the other encoders (area under the curve [AUC], 0.839 for RHWU and 0.854 for TCGA). Attention-based interpretability highlighted high-risk histopathologic regions characterized by hyperchromatic nuclei, prominent nucleoli, and increased mitotic activity. Exploratory transcriptomic analysis showed 381 differentially expressed genes (DEGs) between LVI-positive and LVI-negative groups. Gene ontology enrichment showed that upregulated DEGs in the LVI-positive group were enriched in mitotic and immune-related pathways, whereas downregulated genes were associated with ion transport. CONCLUSIONS:This study exhibited a robust and interpretable AI framework for predicting LVI in PCa from WSIs using weakly supervised learning and domain-adapted foundation models. The model achieved high accuracy, provided biologically meaningful insights, and showed potential for clinical translation as a decision-support tool in precision pathology.

INTRODUCTION:Renal fibrosis as a common pathological endpoint in chronic kidney disease (CKD) and end-stage renal disease (ESRD) is a serious threat to patients' life and health. In recent years, the link between epigenetic modifications and renal fibrosis has been increasingly discovered. OBJECTIVES:This study aimed to investigate the effects of the deubiquitinating enzyme OTUD7B on renal fibrosis and its underlying mechanism. METHODS:Transcriptome sequencing of control and fibrotic cells were performed to screen for deubiquitinating enzymes, and the expression of these enzymes were validated in cellular models, animal models, and clinical specimens. The specific role of OTUD7B in renal fibrosis was elucidated through knockdown and overexpression experiments. Subsequently, IP-MS was employed to identify OTUD7B downstream targets, confirming its interaction with PRDX1 and detecting PRDX1 ubiquitination levels and sites. Finally, IP and UbiBrowser were used to predicte SMURF1 as the PRDX1 ubiquitin ligase. Detection of ubiquitination levels and sites revealed that OTUD7B and SMURF1 maintained PRDX1 protein stability together. RESULTS:The reduction of renal fibrosis deubiquitinating enzyme, OTUD7B, was identified by RNA sequencing and validated in cellular, animal and human samples. In addition, its function was verified by knockdown and overexpression of OTUD7B, with overexpression attenuating renal fibrosis and knockdown exacerbating it. Mechanistically, through reducing SMURF1-mediated K63-linked ubiquitination and subsequent lysosomal degradation of PRDX1, OTUD7B elevated PRDX1 expression, resulting in attenuation of renal fibrosis. CONCLUSION:Collectively, targeting the OTUD7B-SMURF1-PRDX1 axis may offer a promising therapeutic approach for renal fibrosis.

Chronic kidney disease and its progression to end-stage renal disease represent a major global health burden, driven by renal interstitial fibrosis. The function of the intermediate filament protein Keratin 20 (KRT20) in this context remains largely unexplored. This study investigated the role and mechanism of KRT20 in renal fibrosis. Integrated transcriptomic analysis revealed significant upregulation of KRT20 in fibrotic kidneys. With unilateral ureteral obstruction (UUO) mouse models and TGF-β1-stimulated human renal tubular epithelial (HK-2) cells, we found that KRT20 expression increased progressively with fibrotic severity. Functional studies demonstrated that KRT20 knockdown exacerbated fibrosis and epithelial-mesenchymal transition while its overexpression attenuated. Mechanistically, the transcription factor JUNB, activated by TGF-β1, directly bound to the KRT20 promoter to drive its transcription. Upregulated KRT20 protein subsequently interacted with integrin ITGB1, leading to activation of the PI3K/AKT survival signaling pathway. Collectively, these findings delineated a novel protective “JUNB–KRT20–ITGB1–PI3K/AKT” axis in renal fibrosis, identifying KRT20 as a potential therapeutic target for enhancing endogenous anti-fibrotic response.

BACKGROUND:Bladder cancer remains a significant challenge in oncology owing to its high recurrence rates and limited treatment options, particularly in cases of resistance to standard therapies. AIMS:Our study aimed to pinpoint a lactylation-associated gene signature capable of predicting prognosis and providing important theoretical support for drug development and precision therapy in bladder cancer patients. MATERIALS AND METHODS:Leveraging RNA sequencing data from the TCGA and GEO databases, we scrutinized the expression profiles of lactylation-associated genes and pinpointed a signature comprising eight genes strongly linked to prognosis based on a machine learning integrative framework. Our prognostic model, incorporating the expression levels of these lactylation-associated genes, demonstrated high accuracy in predicting patient outcomes, including survival rates and response to immunotherapy. Furthermore, functional analyses revealed the potential mechanisms through which lactylation-associated genes contribute to bladder cancer progression and treatment resistance. Further validation of the close association of these eight genes with bladder cancer was also confirmed through in vitro RT-PCR experiments and Human Protein Atlas (HPA). The drug enrichment analysis and molecular docking provide us with potential drugs and their binding modes with target proteins. To further investigate the relationship between the model gene and bladder cancer, we conducted a series of in vitro experiments. RESULTS:We found that knockdown of AHNAK reduced the proliferation, migration, and invasion abilities of bladder cancer cells and also promoted cell apoptosis. DISCUSSION:Overall, our study highlights the importance of lactylation-associated genes as prognostic markers and potential therapeutic targets in bladder cancer. CONCLUSION:Our identification of this gene signature lays the groundwork for personalized treatment strategies and enhanced patient management in clinical practice.

Background Cystatin SN (CST1), a cysteine protease inhibitor, participates in various cancers, yet its function and mechanism in bladder cancer (BCa) are unclear. Methods Public databases, BCa cells and clinical samples were used to analyze CST expression profiles. Functional experiments detected cell proliferation, migration and invasion. Intracellular iron, ROS, MDA, GSH and mitochondrial morphology reflected ferroptosis. Co-IP assays verified the intracellular interaction between CST1 and GPX4, and ubiquitination assays clarified GPX4 stabilization via suppressed proteasomal degradation. Xenograft and rescue assays validated the CST1-GPX4 axis. Results The expression of CST1 is markedly higher in bladder cancer cells than in normal urothelial cells. CST1 knockdown inhibits malignant phenotypes and induces ferroptosis. CST1 binds GPX4 to block its ubiquitination and post-translationally elevate GPX4 protein without altering its transcription. GPX4 overexpression rescues CST1 silencing-induced ferroptosis and growth defects. In vivo CST1 facilitates tumor growth and GPX4 upregulation. Conclusion CST1 interacts with GPX4 to inhibit its ubiquitin-dependent degradation, suppress ferroptosis and promote BCa progression, and may serve as a potential therapeutic target for bladder cancer.

Cuproptosis, a recently characterized form of mitochondrial-dependent cell death triggered by copper accumulation, remains unexplored in clear cell renal cell carcinoma (ccRCC) and sunitinib resistance. Here, we reveal that the deubiquitinase USP15 suppressed cuproptosis and drove sunitinib resistance in ccRCC. Mechanistically, USP15 stabilized c-Myc through K48-linked deubiquitination at K143 and K289, leading to transcriptional upregulation of PDK1, PDK3, and PDK4 and subsequent repression of DLAT expression and pyruvate dehydrogenase, thereby conferring resistance to cuproptosis. Conversely, the E3 ligase MYCBP2 promoted K48-linked ubiquitination and degradation of c-Myc, antagonizing USP15 function. Sunitinib treatment induced features of cuproptosis, while USP15 upregulation in resistant cells suppressed these effects. USP15 depletion or elesclomol (ES)-Cu/disulfiram (DSF)-Cu restored sunitinib sensitivity both in vitro and vivo without systemic toxicity. Collectively, our findings identify USP15 as a critical regulator of cuproptosis and sunitinib resistance and highligh cuproptosis activation as a promising strategy to overcome tyrosine kinase inhibitor (TKI) resistance in ccRCC.

Background and objective:To compare hexaminolevulinate (HAL) blue light cystoscopy (BLC) with white light cystoscopy (WLC) in the detection of bladder cancer. Methods:Patients received intravesical HAL (Hexvix®) and underwent WLC before randomization to undergo high-definition BLC (System blue). Lesions identified in either WLC or BLC were evaluated by a blinded panel. The primary efficacy endpoint was the proportion of patients with histology-confirmed tumors (Ta, T1, or CIS) and with at least one such tumor found by BLC but not by WLC. The secondary endpoints included the detection of CIS, lesion detection rates, false-positive rate, and safety. Results:Of the 158 (160 screened patients) enrolled patients, 120 underwent WLC and were randomized (6 WLC, 114 BLC), and 97 were diagnosed with NMIBC. The mean age was 65.30 ± 12.18 years. Out of the 114 patients, 13 (11.4%) suffered from CIS; 84.6% (11/13) were detected with additional lesions by BLC; and 61.5% (8/13) were diagnosed solely by BLC. Compared with WLC, the proportion of patients with additional bladder cancer lesions detected by HAL BLC was 43.3% [(33.27%, 53.75%), p < 0.0001]. The proportion of patients with CIS lesions detected by HAL BLC and not by WLC was 9.6% (4.9%, 16.6%). The detection rates for CIS, Ta, T1, and T2-T4 tumors were 94.7%, 100%, 98.2%, and 100% for BLC and 42.1%, 76.1%, 91.2%, and 100% for WLC, respectively. The false-positive rates were 23.2% (19.2%, 27.7%) and 16.0% (11.9%, 20.8%) for BLC and WLC, respectively. A total of 95 patients (60.1%) reported 200 cases of AE, with 9 AEs being drug-related (fever, bladder pain, etc.). Nine device deficiencies (5.7%) occurred (eight quality issues and one device failure). No AEs and SAEs led to discontinuation. Conclusions:In the setting of modern high-definition equipment, HAL BLC significantly improves the detection of bladder cancer with favorable safety.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Although we have made many achievements in the therapy of RCC with the progress of medicine, the clinical management of metastatic RCC remains a daunting challenge. SWI/SNF chromatin remodeling complex-related genes (SCRGs) are significantly associated with tumor progression and cancer cell evolution in ccRCC. This study aimed to investigate the prognostic significance of SCRGs in ccRCC to elucidate its molecular subtype characteristics. Using 29 known SCRGs, 532 ccRCC patients from the TCGA-KIRC cohort were classified into two subtypes (SCRGcluster A and SCRGcluster B). Patients in SCRGcluster B exhibited a significantly poorer prognosis compared to those in SCRGcluster A. Functional enrichment and immune microenvironment profiles significantly differed between the subtypes, with SCRGcluster B showing higher levels of immune infiltration. Five core genes (TMCC3, TOP2A, EPS8, PIK3R3, KCNK5) were identified through the integration of multiple machine learning approaches and multivariate Cox regression analysis. A novel prognostic model based on these core genes was validated in an external cohort. Additionally, single-cell data analysis revealed the expression patterns of these core genes in ccRCC. In vitro experiments demonstrated that KCNK5 is underexpressed in ccRCC cell lines and tissues, and that its overexpression suppresses malignant phenotypes. Specifically, KCNK5 overexpression significantly inhibited the proliferation, migration, and invasion capabilities of ccRCC cell lines. Although the precise functional mechanisms of these core genes in ccRCC are not yet fully elucidated, this study provides new insights for the treatment of ccRCC.

Renal fibrosis is a critical progression of chronic kidney disease, and epithelial-to-mesenchymal transition (EMT) and extracellular matrix(ECM) deposition are crucial pathologic change of renal fibrosis, which still lacks of effective treatment. In this study, it was found that cyanidin-3-O-glucoside (C3G) could inhibit EMT and ECM activated by unilateral ureteral obstruction (UUO) and transforming growth factor-β1 (TGF-β1) stimulation. Moreover, N-Myc downstream-regulated gene 2(NDRG2), which involved in the progression of renal fibrosis, was down-regulated in vivo and in vitro model. However, C3G pretreatment could reverse the reductive expression of NDRG2. Furthermore, we found that the combined treatment of C3G and si-NDRG2 could reverse the decreased EMT and ECM, which induced by C3G treatment only. And the activation of Phosphatidylinositol 3-kinase (PI3K)/ Protein Kinase B (AKT) pathway significantly enhanced EMT and ECM, which was decreased by C3G treatment only in TGF-β1 induced Human Kidney 2 (HK-2) cells. In conclusion, our results demonstrated that C3G alleviated EMT and ECM by elevating NDRG2 expression through the PI3K/AKT pathway, indicating that C3G could be a potential treatment against renal fibrosis.

Background: Pro-apoptotic coiled-coil domain containing 8 (CCDC8) has been linked to tumor progression and metastasis, yet its prognostic significance and underlying molecular mechanisms in bladder cancer remain to be elucidated. Materials and methods: This study utilized raw data from public databases along with a single-center retrospective case series. We performed bioinformatics analysis and immunohistochemistry to investigate the biological landscape of CCDC8 in various tumors, with a particular focus on bladder cancer. This involved examining its expression characteristics and prognostic value. Gene function enrichment analysis was conducted to perform functional annotation, evaluate the association between bladder cancer molecular subtypes and mutation spectra, and analyze the tumor immune microenvironment to predict treatment response sensitivity. Results: Our study identified CCDC8 as a novel prognostic marker for bladder cancer. We observed that high CCDC8 expression correlates with poor prognosis and a suboptimal response to immunotherapy in bladder cancer. CCDC8 was implicated in regulating tumor immune status, metabolic activity, and cell cycle-related signaling pathways, thereby influencing the biological behavior of tumor cells. Additionally, CCDC8 contributed to the suppression of the immune microenvironment, diminishing anti-tumor immune responses. Comprehensive characterization of CCDC8 was applied to prognostic prediction in bladder cancer, indicating that targeting CCDC8 may be a potential therapeutic strategy. Conclusions: These findings suggest that CCDC8 serves as an independent biomarker for predicting prognosis and immunotherapy efficacy for bladder cancer. Further investigation into its specific molecular mechanisms may offer new therapeutic strategies for treating bladder cancer.

ABSTRACTObjectivesTo automatically identify and diagnose bladder outflow obstruction (BOO) and detrusor underactivity (DUA) in male patients with lower urinary tract symptoms through urodynamics exam.Patients and MethodsWe performed a retrospective review of 1949 male patients who underwent a urodynamic study at two institutions. Deep Convolutional Neural Networks scheme combined with a short‐time Fourier transform algorithm was trained to perform an accurate diagnosis of BOO and DUA, utilizing five‐channel urodynamic data (consisting of uroflowmetry, urine volume, intravesical pressure, abdominal pressure, and detrusor pressure). We used fivefold cross‐validation, constructing training and internal test sets from 1725 patients from Renmin Hospital of Wuhan University (RHWU) at a 4:1 ratio, and used an independent external validation set consisting of 224 patients from The Central Hospital of Wuhan (TCHO) to build and evaluate the DI model. We further conducted subgroup analyses to provide a more detailed description of the AI model's interpretability regarding urodynamics.ResultsThe AUC scores of BOO and DUA, which were measured through the STFT‐based deep learning method, were 0.945 ± 0.020 and 0.929 ± 0.039 in RHWU and 0.881 and 0.850 in TCHO, respectively. The diagnostic efficiency of other subgroup analyses and indicators was also effective.ConclusionIn this study, the proposed deep neural network combined with the short‐time Fourier transform method is robust and feasible for interpreting the results of urodynamics in men and has the potential for application to assist clinicians in real clinical settings.

Background: Renal cell carcinoma (RCC) is a leading malignancy of the urinary system, with clear cell RCC (ccRCC) being the most prevalent subtype. Despite advances in treatment, the prognosis of advanced RCC remains poor, and the molecular mechanisms underlying its pathogenesis are not fully understood. Methods: This study utilized multiple renal cancer cohorts from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). By integrating Mendelian randomization (MR) analyses of expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL), we investigated causal associations between candidate genes and RCC. Immune infiltration, drug sensitivity, and survival analyses were performed to further explore functional significance. In vitro experiments validated the biological role ofISOC1 in RCC progression. Results: We focused on ISOC1, a gene previously implicated in other malignancies but not well studied in RCC. Through integrative MR analysis, we identified ISOC1 as a novel RCC-associated gene, with potential tumor-suppressive functions in this specific context. ISOC1 expression was significantly linked to tumor immune infiltration, drug sensitivity, and patient prognosis. Functional assays demonstrated that ISOC1 knockdown promoted RCC cell proliferation, migration, and invasion. Conclusions: ISOC1 plays a critical role in RCC progression and may act as a tumor suppressor. These findings highlight ISOC1 as a potential biomarker for prognosis and a promising target for therapeutic intervention in RCC. Moreover, this study underscores the utility of MR-based integrative analyses in uncovering novel molecular mechanisms and therapeutic targets for cancer.

BACKGROUND:The assessment of the International Society of Urological Pathology (ISUP) nuclear grade is crucial for the management and treatment of clear cell renal cell carcinoma (ccRCC). This study aimed to explore the value of using integrated multimodal information for ISUP grading and prognostic stratification in ccRCC patients, to guide postoperative adjuvant therapy. METHODS:This retrospective study analyzed a total of 729 patients from three cohorts, utilizing whole slide i-mages and computed tomography (CT) images. Artificial intelligence algorithms were used to extract morphological and textural features from whole slide images and CT images separately, creating single-modality predictive models for ISUP grading. By combining the CT and pathology single-modality predictive features, a multimodal predictive signature (MPS) was developed. The prognostic performance of the MPS model was further validated in two independent cohorts. RESULTS:The single-modality predictive models for CT and pathology performed well in predicting ISUP grade for ccRCC. The MPS model achieved higher area under the curve values of 0.95, 0.93, and 0.95 across three independent patient cohorts. Additionally, the MPS model was able to distinguish patients with poorer overall survival. In the external validation cohort, uni- and multivariate analyses showed hazard ratios of 2.542 (95% confidence interval [CI]: 1.363-4.741, P < 0.0001) and 1.723 (95% CI: 0.888-3.357, P = 0.003), respectively. The C-index values for the two cohorts were 0.75 and 0.71. Furthermore, the MPS outperformed single-modality models, providing a complementary tool for current risk stratification in ccRCC adjuvant therapy. CONCLUSION:Our novel MPS model demonstrated high accuracy in ISUP grading for ccRCC patients. With further validation across multiple centers, the MPS model could be used for precise detection of nuclear grading in ccRCC, serving as an effective tool for assisting clinical decision-making.

Background:Prostate cancer (PCa) remains a leading cause of male morbidity and mortality globally, where transrectal ultrasound (TRUS) serves as the cornerstone imaging modality for diagnosis and therapeutic guidance. However, automated segmentation of prostatic anatomy in TRUS is persistently hindered by technical constraints. This study aimed to develop prostate segmentation model trained by deep learning from ultrasound images of patients with benign prostatic hyperplasia (BPH) and to verify if the developed model can be applied for the ultrasound image segmentation of patients with PCa. Methods:A total of 370 and 68 prostate ultrasound images were collected from 260 BPH patients and 62 PCa patients, respectively. U-Net, LinkNet and PSPNet neural network were used to train segmentation model. The Dice coefficients of the model segmentation for test set comprising BPH and PCa images were calculated. Two independent-sample t-tests were used to compare the Dice coefficients. Results:The study demonstrated significant radiomic differences between PCa and BPH in ultrasound imaging, with least absolute shrinkage and selection operator (LASSO) regression identifying 9 discriminative features including shape and texture parameters. The U-Net model achieved superior segmentation performance with a peak Intersection over Union (IoU) of 0.9602 and maintained robustness across resolutions. The independent-sample t-test proved that the two groups did not differ significantly (P>0.05). Four post-segmentation image-processing methods all proved that the model was effective (P>0.05). Conclusions:We proved that prostatic segmentation model trained on ultrasound images of BPH could be applied in PCa.

Renal cell carcinoma (RCC) is one of the most common urological malignancies worldwide, and advanced patients often face challenges with chemotherapy resistance and poor prognosis. Ferroptosis, a novel form of cell death, offers potential therapeutic prospects. In this study, we found that DJ-1 was elevated in kidney renal clear cell carcinoma (KIRC), and this abnormal expression pattern was closely associated with clinical pathological characteristics and worse prognosis. Our experiments both in vivo and in vitro revealed that DJ-1 enhanced the malignant characteristics of KIRC, leading to increased tumor growth. Additionally, DJ-1 inhibited ferroptosis through promoting homocysteine (Hcy) synthesis in the transsulfuration pathway in KIRC cells. Mechanistic studies revealed that O-GlcNAc transferase (OGT) mediated O-GlcNAcylation of DJ-1 was crucial for maintaining its homodimeric structure. Importantly, O-GlcNAcylation-deficient mutation of DJ-1 at T19 residue enhanced the interaction between S-adenosyl homocysteine hydrolase (SAHH) and the negative regulatory factor S-adenosyl homocysteine hydrolase-like-1 (AHCYL1), thereby inhibited the activities of SAHH and transsulfuration pathway. In summary, the oncogenic role of DJ-1 in KIRC was closely related to the reduction of ferroptosis, and the O-GlcNAcylation of DJ-1 exerted an antioxidant effect by activating the transsulfuration pathway. Therefore, DJ-1, specifically O-GlcNAcylation of DJ-1 could represent an important target for ferroptosis-based anti-tumor therapy.

In our research, we constructed models of renal ischemia–reperfusion (I/R)‐exposed acute kidney injury (AKI) and unilateral ureteral obstruction (UUO)‐stimulated renal fibrosis (RF) in C57BL/6 mice and HK‐2 cells. We firstly authenticated that oral pinocembrin (PIN) administration obviously mitigated tissue damage and renal dysfunction induced by I/R injury, and PIN attenuated UUO‐caused RF, as confirmed by the reduced expression of fibrotic markers as well as hematoxylin–eosin (H&E), Sirius red, immunohistochemistry, and Masson staining. Meanwhile, the beneficial role of PIN was again demonstrated in HK‐2 cells with hypoxia–reoxygenation (H/R) or transforming growth factor beta‐1 (TGF‐β1) treatment. Importantly, the “ingredient–target–pathway–disease” network was established through bioinformatics analysis and molecular docking, which showed that PIN may target cytochrome P450 1B1 (CYP1B1) and modulate the mitogen‐activated protein kinase (MAPK) pathway to exert its impact during injury. Furthermore, experiments confirmed that PIN usage remarkably constrained CYP1B1 expression, reactive oxygen species (ROS) production, MAPK‐pathway‐associated inflammation, or apoptosis during I/R injury or UUO exposure. PIN also ameliorated the elevated protein phosphorylation of MAPK pathway components [p38, extracellular signal‐regulated kinase (ERK) and c‐Jun N‐terminal kinase 1 (JNK ERK and JNK)], which validated the PIN‐induced inhibition of the MAPK signaling pathway in renal I/R or UUO injury. Moreover, the AAV9 (adeno‐associated virus 9)‐packed CYP1B1 or pcDNA‐CYP1B1 overexpression plasmid was utilized to treat C57BL/6 mice or HK‐2 cells to overexpress CYP1B1, respectively. Notably, CYP1B1 overexpression considerably abolished PIN's restriction impact on ROS generation and MAPK pathway activation. In conclusion, via bioinformatics analysis, molecular docking, animal model, and cellular experiments, we proved that PIN alleviates renal I/R injury/UUO‐generated renal fibrosis through regulating the CYP1B1/ROS/MAPK axis.

Background:Adult obesity increases the risk of kidney cancer (KIC), yet the link between early body size traits and KIC remains uncertain. This study aimed to investigate the causal relationship between early body size characteristics and KIC, including its subtypes, using Mendelian randomization (MR). Methods:We utilized data from public genome-wide association study (GWAS) databases on birth weight and body mass index (BMI) across childhood, adolescence, and adulthood as exposure variables, and KIC and its subtypes as outcome variables. A two-way two-sample MR analysis was performed to explore these associations, with the inverse variance weighted (IVW) method as the primary analytical approach and sensitivity analyses to assess result stability. Results:IVW analysis revealed significant associations between childhood obesity [odds ratio (OR) =1.08, 95% confidence interval (CI): 1.04-1.14, P<0.001], childhood BMI (OR =1.23, 95% CI: 1.07-1.42, P=0.003), adolescent BMI (OR =1.22, 95% CI: 1.07-1.40, P=0.003), and adult BMI (OR =1.75, 95% CI: 1.41-2.17, P<0.001) with increased risk of KIC. Similar associations were observed for clear cell renal cell carcinoma (ccRCC), with childhood obesity (OR =1.09, 95% CI: 1.02-1.15, P=0.007), childhood BMI (OR =1.33, 95% CI: 1.14-1.55, P<0.001), adolescent BMI (OR =1.24, 95% CI: 1.04-1.47, P=0.01), and adult BMI (OR =1.97, 95% CI: 1.51-2.57, P<0.001) significantly linked to higher ccRCC risk. No evidence of reverse causation was found. Conclusions:This study provides MR evidence supporting a causal association between early-life obesity and KIC. Our findings suggest that reducing obesity in early life may have a potential positive impact on the prevention of KIC.

The aim of this study was to guide prostatectomy by employing artificial intelligence for the segmentation of tumor gross tumor volume (GTV) and neurovascular bundles (NVB). The preservation and dissection of NVB differ between intrafascial and extrafascial robot-assisted radical prostatectomy (RARP), impacting postoperative urinary control. We trained the nnU-Net v2 neural network using data from 220 patients in the PI-CAI cohort for the segmentation of prostate GTV and NVB in biparametric magnetic resonance imaging (bpMRI). The model was then validated in an external cohort of 209 patients from Renmin Hospital of Wuhan University (RHWU). Utilizing three-dimensional reconstruction and point cloud analysis, we explored the spatial distribution of GTV and NVB in relation to intrafascial and extrafascial approaches. We also prospectively included 40 patients undergoing intrafascial and extrafascial RARP, applying the aforementioned procedure to classify the surgical approach. Additionally, 3D printing was employed to guide surgery, and follow-ups on short- and long-term urinary function in patients were conducted. The nnU-Net v2 neural network demonstrated precise segmentation of GTV, NVB, and prostate, achieving Dice scores of 0.5573 ± 0.0428, 0.7679 ± 0.0178, and 0.7483 ± 0.0290, respectively. By establishing the distance from GTV to NVB, we successfully predicted the surgical approach. Urinary control analysis revealed that the extrafascial approach yielded better postoperative urinary function, facilitating more refined management of patients with prostate cancer and personalized medical care. Artificial intelligence technology can accurately identify GTV and NVB in preoperative bpMRI of patients with prostate cancer and guide the choice between intrafascial and extrafascial RARP. Patients undergoing intrafascial RARP with preserved NVB demonstrate improved postoperative urinary control.