American journal of clinical and experimental urology最新文献

Objectives: This study aimed to investigate the expression of Vascular endothelial growth factor (VEGF) and Secreted phosphoprotein-1 (SPP1) isoforms in bladder cancer tissue and their correlation with tumor grade and muscle invasion.

Methods: In a prospective study, we examined 40 patients who had been diagnosed with bladder cancer. The diagnosis was confirmed through cystoscopy, biopsy, and histopathology. We used the RT-PCR method to measure the levels of human SPP1 and VEGF splice isoforms. Statistical analysis of the average of three replicates was performed using SPSS for Windows version 23.0.

Results: The study revealed a significant correlation between patients' histological grades and muscle invasiveness. Additionally, the investigation of 5 isoforms in patients showed that SPP1A, SPP1B, and VEGFA isoforms were significantly associated with tumor grade. However, the SPP1C and VEGFC isoforms showed no significant association with tumor grade. A new index was calculated based on the logistic regression model (mean_SPP1A - (mean_VEGFC * 0.7)), and the cut-off and the Area Under the ROC curve (AUC) were 23.1 and 0.951, respectively.

Conclusions: The relationship between the grade of bladder cancer and the two isoforms of SPP1A and VEGFC shows that these indicators could help pathologists differentiate between high and low-grade bladder cancer and potentially be used as predictive markers.

Purpose: Currently, there is an urgent need for prognostic prediction models for renal clear cell carcinoma (ccRCC). This study aims to establish a prognostic prediction model based on differential genes associated with TNM staging and validate it through both in vitro and in vivo experiments.

Method: Through the cross-analysis of the differential genes between T1-2 and T3-4 stages, N1 and N2 stages, as well as M0 and M1 stages in ccRCC, a nomogram prognostic model was constructed using multivariate COX regression analysis. Finally, the function of human serum amyloid A2 (SAA2) was verified through in vivo and in vitro experiments.

Result: Through cross-analysis of the differential genes between T1-2 and T3-4 stages, N1 and N2 stages, as well as M0 and M1 stages, 67 genes were identified. Through multivariate COX regression analysis, examination of expression differences between cancerous and normal tissues, and assessment of their impact on prognosis, we have derived a nomogram prognostic prediction model composed of ITPKA, PDIA2, SAA2, SHOX2, TREML3P, and ZIC2. Furthermore, through Transwell migration and invasion assays, EdU proliferation assays, and in vivo experiments, we validated that SAA2 promotes the proliferation, migration, and invasion of ccRCC.

Conclusion: The nomogram prognostic prediction model, consisting of ITPKA, PDIA2, SAA2, SHOX2, TREML3P, and ZIC2, is capable of predicting the prognosis of ccRCC patients. Among them, SAA2 promotes the proliferation, migration, and invasion of ccRCC cells.

Objectives: This study aims to evaluate the overall impact of incorporating deep learning (DL) with magnetic resonance imaging (MRI) for improving diagnostic performance in the detection and stratification of prostate cancer (PC).

Methods: A systematic search was conducted in the PubMed database to identify relevant studies. The QUADAS-2 tool was employed to assess the scientific quality, risk of bias, and applicability of primary diagnostic accuracy studies. Additionally, adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines was evaluated to determine the extent of heterogeneity among the included studies. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.

Results: A total of 29 articles involving 17,954 participants were included in the study. The median agreement to the 42 CLAIM checklist items across studies was 61.90% (IQR: 57.14-66.67, range: 40.48-80.95). Most studies utilized T2-weighted imaging (T2WI) and/or apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) as input for evaluating the performance of DL-based architectures. Notably, the detection and stratification of PC in the transition zone was the least explored area.

Conclusions: DL demonstrates significant advancements in the rapid, sensitive, specific, and robust detection and stratification of PC. Promising applications include enhancing the quality of DWI, developing advanced DL models, and designing innovative nomograms or diagnostic tools to improve clinical decision-making.

Autophagy, a cellular degradation mechanism, plays a dual role in the progression and therapy of bone metastases in cancers, including prostate cancer. This review delves into the intricate roles of autophagy in both tumor suppression and progression, with a focus on its impact on bone metastasis and osteosarcoma (OS). Initially, autophagy acts as a tumor suppressor by eliminating damaged organelles and proteins, thus preventing tumor initiation. However, as cancer progresses, autophagy supports cancer cell survival under stress conditions, such as nutrient deprivation and hypoxia, and contributes to drug resistance. Specifically, in bone metastasis from breast and prostate cancers, autophagy facilitates tumor cell migration, invasion, and survival. The review also highlights the therapeutic potential of targeting autophagy in cancer treatment, especially in overcoming drug resistance in osteosarcoma, where autophagy modulation could reduce chemoresistance. By understanding the dual roles of autophagy in cancer and bone metastasis, new therapeutic strategies can be developed to target this process, offering hope for improved treatment outcomes in cancers prone to bone metastasis, including prostate cancer.

Objectives: Extracellular ATP/ADP and its metabolite adenosine play crucial roles in cellular signaling by interacting with P2 and P1/adenosine receptors. These signaling molecules are regulated by ectonucleotidases, which convert ATP/ADP into adenosine. While recent studies suggest impaired ATP hydrolysis in the aging prostate, the expression and function of ectonucleotidases and purinergic receptors in the prostate gland remain unclear. This study aims to characterize the expression patterns of purinergic enzymes and receptors in the mouse prostate and investigate their functional implications.

Methods: Mouse prostate glands were isolated and analyzed using immunofluorescent staining and microscopy imaging with specific antibodies to detect purinergic enzymes and receptors. Functional studies were conducted to assess prostate smooth muscle contraction in response to purinergic agonists, particularly α,β-meATP and ATPγS.

Results: Our analysis revealed distinct expression patterns of purinergic enzymes and receptors in the prostate: Ectonucleoside triphosphate diphosphohydrolase 1 (ENTPD1) and P2X1 receptors were predominantly localized in prostate smooth muscle cells, ENTPD2 and ecto-5'-nucleotidase (NT5E) in prostate interstitial cells, and alkaline phosphatase (ALPL) in prostate epithelial cells. Notably, ENTPD1 was identified as a key ectonucleotidase expressed in mouse prostate smooth muscle cells. Functionally, P2X1-mediated smooth muscle contraction was triggered by α,β-meATP. However, ATPγS induced contraction even after P2X1 desensitization, suggesting the involvement of additional P2Y receptors. Further analysis confirmed the presence of P2Y1, P2Y2, and P2Y11 receptors in mouse prostate smooth muscle, likely mediating the ATPγS-induced contraction.

Conclusions: This study provides a comprehensive characterization of purinergic signaling components in the mouse prostate. The identification of ENTPD1 in smooth muscle cells and the functional role of multiple P2Y receptors in smooth muscle contraction highlight potential regulatory mechanisms of prostate function. These findings lay the groundwork for future research on purinergic signaling in prostate physiology and its potential implications in age-related dysfunction, both in rodents and humans.

Objectives: Extraprostatic extension (EPE) and seminal vesicle invasion (SVI) are unfavorable factors for biochemical recurrence (BCR) following radical prostatectomy; however, some patients with SVI survive for a long duration without experiencing BCR after prostatectomy in absence of adjuvant therapy. This study aimed to clarify the heterogeneity of locally advanced prostate cancers to better understand prognosis in patients with SVI.

Methods: We retrospectively reviewed the medical records of 120 patients with SVI who underwent radical prostatectomy at two institutions. Multivariate logistic regression was used to evaluate the preoperative clinical and postoperative pathological variables as predictors of BCR. We also used Kaplan-Meier and competing risk regression analysis to assess the cumulative incidence and risk of BCR. After excluding patients who received neoadjuvant or adjuvant therapy, 55 patients with SVI were enrolled in this study.

Results: BCR occurred in 31 of these patients (56.3%). We found that Grade group and positive EPE were predictors of BCR in patients with SVI (P < 0.001 and P = 0.002, respectively). Using the multivariate model, EPE was significantly associated with BCR in patients with SVI (hazard ratio: 5.402; 95% confidence interval, 1.247-23.405; P = 0.012). Patients who were negative for EPE had significantly lower BCR rates (P = 0.002).

Conclusions: Among the patients with SVI tumors, prognosis might be different depending on presence or absence of EPE.

Introduction: The medical information generated by large language models (LLM) is crucial for improving patient education and clinical decision-making. This study aims to evaluate the performance of two LLMs (DeepSeek and ChatGPT) in answering questions related to prostate cancer radiotherapy in both Chinese and English environments. Through a comparative analysis, we aim to determine which model can provide higher-quality answers in different language environments.

Methods: A structured evaluation framework was developed using a set of clinically relevant questions covering three key domains: foundational knowledge, patient education, and treatment and follow-up care. Responses from DeepSeek and ChatGPT were generated in both English and Chinese and independently assessed by a panel of five oncology specialists using a five-point Likert scale. Statistical analyses, including the Wilcoxon signed-rank test, were performed to compare the models' performance across different linguistic contexts.

Results: This study ultimately included 33 questions for scoring. In Chinese, DeepSeek outperformed ChatGPT, achieving top ratings (score = 5) in 75.76% vs. 36.36% of responses (P < 0.001), excelling in foundational knowledge (76.92% vs. 38.46%, P = 0.047) and treatment/follow-up (81.82% vs. 36.36%, P = 0.031). In English, ChatGPT showed comparable performance (66.7% vs. 54.55% top-rated responses, P = 0.236), with marginal advantages in treatment/follow-up (63.64% vs. 54.55%, P = 0.563). DeepSeek maintained strengths in English foundational knowledge (69.23% vs. 30.77%, P = 0.047) and patient education (88.89% vs. 55.56%, P = 0.125). These findings underscore DeepSeek's superior Chinese proficiency and language-specific optimization impacts.

Conclusions: This study shows that DeepSeek performs excellently in providing Chinese medical information, while the two models perform similarly in an English environment. These findings underscore the importance of selecting language-specific artificial intelligence (AI) models to enhance the accuracy and reliability of medical AI applications. While both models show promise in supporting patient education and clinical decision-making, human expert review remains necessary to ensure response accuracy and minimize potential misinformation.

The landscape for the treatment of advanced and metastatic prostate cancer is rapidly changing. For patients with metastatic castration-resistant prostate cancer (mCRPC), next-generation sequencing (NGS) may identify those with Homologous Recombination Deficiency (HRD) who may benefit from Poly ADP [adenosine diphosphate]-ribose polymerase inhibitors (PARP) inhibition therapy. Ongoing questions remain, however, regarding how patients and clinicians can best select therapies to optimize patient outcomes. In this case report, we highlight a patient with rapidly progressive mCRPC with germline BRCA2 for whom olaparib was added with abiraterone and prednisone resulting in a significant but brief response. Using next-generation sequencing of a liquid biopsy, we identified Protein Phosphatase 1D (PPM1D) as a potential resistance mechanism to PARP inhibition. While this alteration has been previously reported in other tumor types, the role of PPM1D and its contribution to PARP inhibitor resistance in mCRPC has not been described; the aim of this report was to highlight the potential role it may play in prostate cancer. With the increasing availability of circulating tumor DNA (ctDNA) to assist clinicians with monitoring patients' responses on therapy, the results from this case study underscore the necessity of exploring optimal timing of liquid and/or repeat tumor biopsies to help longitudinally personalize targeted therapy to improve patient outcomes.

Kidney injury and disease pose a significant global health burden. Despite existing diagnostic methods, early detection remains challenging due to the lack of specific molecular markers to identify and stage various kidney lesions. Urinary exosomes, extracellular vesicles secreted by kidney cells, offer a promising solution. These vesicles contain a variety of biomolecules, such as proteins, RNA, and DNA. These biomolecules can reflect the unique physiological and pathological states of the kidney. This review explores the potential of urinary exosomes as biomarkers for a range of kidney diseases, including renal failure, diabetic nephropathy, and renal tumors. By analyzing specific protein alterations within these exosomes, we aim to develop more precise and tailored diagnostic tools to detect kidney diseases at an early stage and improve patient outcomes. While challenges persist in isolating, characterizing, and extracting reliable information from urinary exosomes, overcoming these hurdles is crucial for advancing their clinical application. The successful implementation of urinary exosome-based diagnostics could revolutionize early kidney disease detection, enabling more targeted treatment and improved patient outcomes.

Aneurysms are abnormalities in blood vessels that can be categorized as true aneurysms or pseudoaneurysms. Pseudoaneurysms occur when one or more layers of the blood vessel wall rupture, often as a result of trauma or medical procedures, such as nephrolithotomy. This case study discusses a pseudoaneurysm of the lower pole segmental artery of the kidney that developed after an open nephrolithotomy despite an avascular surgical plan. The patient experienced intermittent gross hematuria, highlighting the potential complications associated with renal surgeries. The diagnosis was challenging, necessitating a high suspicion index and imaging modalities such as ultrasound, CT scans, and angiography. Treatment options varied from conservative management to angioembolization, which is preferred for its minimally invasive nature and ability to preserve renal parenchyma. This study aims to demonstrate that the risk of pseudoaneurysm should be considered even in an atrophic nephrolithotomy performed without vascular invasion.