Sustainable functional urethral reconstruction improves early urinary continence after robot-assisted radical prostatectomy: a randomised controlled trial.

OBJECTIVE: To evaluate the impact of sustainable functional urethral reconstruction (SFUR) on early recovery of urinary continence (UC) after robot-assisted radical prostatectomy. PATIENTS AND METHODS: Overall, 96 patients with primary prostate cancer were randomised into the SFUR or standard group (n = 48 each). The primary outcome was the 1-month UC recovery. Secondary outcomes included short-term (≤3 months) UC recovery, urinary function, micturition-related bother, perioperative complications, and oncological outcomes. Kaplan-Meier curves and Cox proportional hazard models were used to assess the 3-month UC recovery. Generalised estimating equations were used to compare postoperative urinary function and micturition-related bother. RESULTS: The 1-month UC recovery rates, median 24-h pad weights, and median operative time in the SFUR and standard groups were 73% and 49% (P = 0.017), 0 and 47 g (P = 0.001), and 125 and 103 min (P = 0.025), respectively. The UC recovery rates in the SFUR vs standard groups were 53% vs 23% at 1 week (P = 0.003), 53% vs 32% at 2 weeks (P = 0.038), and 93% vs 77% at 3 months (P = 0.025). The median time to UC recovery in the SFUR and standard groups was 5 and 34 days, respectively (log-rank P = 0.006); multivariable Cox regression supported this result (hazard ratio 1.73, 95% confidence interval 1.08-2.79, P = 0.024). Similar results were observed when UC was defined as 0 pads/day. Urinary function (P = 0.2) and micturition-related bother (P = 0.8) were similar at all follow-up intervals. The perioperative complication rates, positive surgical margin rates, and 1-year biochemical recurrence-free survival were comparable between both groups (all P > 0.05). CONCLUSION: SFUR resulted in earlier UC recovery without compromising postoperative urinary function. Long-term validation and multicentre studies are required to confirm the results of this novel technique.

How education level affects postoperative rehabilitation and follow-up: a single-center experience.

BACKGROUND: Radical prostatectomy remains the fundamental treatment for prostate cancer, and improving patients' compliance with postoperative follow-ups is essential for improving patients' quality of life. This study investigates the effect of education levels on patients' recovery and follow-up after radical prostatectomy. METHODS: Data from 1,112 patients undergoing radical prostatectomy between 2011 and 2020 were collected using medical records, and "pc-follow" systems were used to collect patients' baseline information, education level, pathological information, number of outpatient visits, the time interval between each visit, and PSA test data. RESULTS: Regarding postoperative outpatient data, there was no difference in the number of outpatient visits among the different education level groups in Shanghai (P = 0.063). A significant difference was found in the interval between outpatient visits among the groups (P < 0.001). Furthermore, significant differences were detected in the number and duration of outpatient clinic visits among the education level groups in all patients (P = 0.016, P = 0.0027). By contrast, no significant difference was found in the recovery time of urinary continence between all patients and those in Shanghai, grouped according to education level (P = 0.082, P = 0.68). For all patients and patients in the Shanghai area, the number of PSA follow-ups increased gradually with an increasing level of education (P < 0.001, P = 0.0029). CONCLUSIONS: Education level affected the number of postoperative clinic visits, compliance, and the number of PSA tests. However, no significant effect on the recovery of urinary continence was found. Further, clinicians must increase their focus on patients with low education levels to achieve equitable access to health services for all patients.

Identification of novel molecular subtypes and a signature to predict prognosis and therapeutic response based on cuproptosis-related genes in prostate cancer.

Background: Prostate cancer (PCa) is the most common malignant tumor of the male urinary system. Cuproptosis, as a novel regulated cell death, remains unclear in PCa. This study aimed to investigate the role of cuproptosis-related genes (CRGs) in molecular stratification, prognostic prediction, and clinical decision-making in PCa. Methods: Cuproptosis-related molecular subtypes were identified by consensus clustering analysis. A prognostic signature was constructed with LASSO cox regression analyses with 10-fold cross-validation. It was further validated in the internal validation cohort and eight external validation cohorts. The tumor microenvironment between the two risk groups was compared using the ssGSEA and ESTIMATE algorithms. Finally, qRT-PCR was used to explore the expression and regulation of these model genes at the cellular level. Furthermore, 4D Label-Free LC-MS/MS and RNAseq were used to investigate the changes in CRGs at protein and RNA levels after the knockdown of the key model gene B4GALNT4. Results: Two cuproptosis-related molecular subtypes with significant differences in prognoses, clinical features, and the immune microenvironment were identified. Immunosuppressive microenvironments were associated with poor prognosis. A prognostic signature comprised of five genes (B4GALNT4, FAM83D, COL1A, CHRM3, and MYBPC1) was constructed. The performance and generalizability of the signature were validated in eight completely independent datasets from multiple centers. Patients in the high-risk group had a poorer prognosis, more immune cell infiltration, more active immune-related functions, higher expression of human leukocyte antigen and immune checkpoint molecules, and higher immune scores. In addition, anti-PDL-1 immunotherapy prediction, somatic mutation, chemotherapy response prediction, and potential drug prediction were also analyzed based on the risk signature. The validation of five model genes' expression and regulation in qPCR was consistent with the results of bioinformatics analysis. Transcriptomics and proteomics analyses revealed that the key model gene B4GALNT4 might regulate CRGs through protein modification after transcription. Conclusion: The cuproptosis-related molecular subtypes and the prognostic signature identified in this study could be used to predict the prognosis and contribute to the clinical decision-making of PCa. Furthermore, we identified a potential cuproptosis-related oncogene B4GALNT4 in PCa, which could be used as a target to treat PCa in combination with cuproptosis.

Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer.

Background: Prostate cancer (PCa) is one of the most common tumors of the urinary system. Cuproptosis is a novel mode of controlled cell death that is related to the development of various tumor types. However, the functions of cuproptosis-related long noncoding RNAs (CRLs) in PCa have not yet been well studied. Methods: In this study, data of PCa patients were obtained from The Cancer Genome Atlas (TCGA) and from the Changhai Hospital. Univariate and multivariate Cox regression analyses and LASSO regression analysis were conducted to screen CRLs linked to the prognosis of PCa patients. A risk score model was constructed on the basis of CRLs to predict prognosis. PCa patients were categorized into high- and low-risk cohorts. The predictive value of the risk score was evaluated by Kaplan-Meier survival analysis, receiver operating characteristic curves, and nomograms. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore possible pathways involving CRLs in PCa. Immune function analysis confirmed the correlation between CRLs and immunity in PCa. Finally, we explored the tumor mutational burden and drug response in the high- and low-risk cohorts. Results: First, we identified seven CRLs (C1orf229, C9orf139, LIPE-AS1, MCPH1-AS1, PRR26, SGMS1-AS1, and SNHG1) that were closely related to prognosis in PCa. The risk score model based on the selected CRLs could accurately predict the prognosis of PCa patients. The high-risk cohort was associated with worse disease-free survival (DFS) time in PCa patients (p < 0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 1 year: 0.703). Nomograms were constructed based on the risk score and clinicopathological features and also exhibited great predictive efficiency for PCa. GO and KEGG analyses showed that the CRLs were mainly enriched in metabolism-related biological pathways in PCa. In addition, immune function analysis showed that patients in the high-risk cohort had higher TMB and were more sensitive to conventional chemotherapy and targeted drugs including doxorubicin, epothilone B, etoposide, and mitomycin C. Conclusion: We constructed a novel CRL-related risk score model to accurately predict the prognosis of PCa patients. Our results indicate that CRLs are potential targets for drug therapy in PCa and provide a possible new direction for personalized treatment of PCa patients.

Establishment of an Individualized Predictive Model to Reduce the Core Number for Systematic Prostate Biopsy: A Dual Center Study Based on Stratification of the Disease Risk Score.

PURPOSE: To establish an individualized prostate biopsy model that reduces unnecessary biopsy cores based on multiparameter MRI (mpMRI). MATERIALS AND METHODS: This retrospective, non-inferiority dual-center study retrospectively included 609 patients from the Changhai Hospital from June 2017 to November 2020 and 431 patients from the Fujian Union Hospital between 2014 and 2019. Clinical, radiological, and pathological data were analyzed. Data from the Changhai Hospital were used for modeling by calculating the patients' disease risk scores. Data from the Fujian Union Hospital were used for external verification. RESULTS: Based on the data of 609 patients from the Changhai Hospital, we divided the patients evenly into five layers according to the disease risk score. The area under the receiver operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) was analyzed. Twelve-core systemic biopsy (12-SBx) was used as the reference standard. The SBx cores from each layer were reduced to 9, 6, 5, 4, and 4. The data of 279 patients with benign pathological results from the Fujian Union Hospital were incorporated into the model. No patients were in the first layer. The accuracies of the models for the other layers were 88, 96.43, 94.87, and 94.59%. The accuracy of each layer would be increased to 96, 100, 100, and 97.30% if the diagnosis of non-clinically significant prostate cancer was excluded. CONCLUSIONS: In this study, we established an individualized biopsy model using data from a dual center. The results showed great accuracy of the model, indicating its future clinical application.