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.

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.

PurposeTo establish an individualized prostate biopsy model that reduces unnecessary biopsy cores based on multiparameter MRI (mpMRI).Materials and MethodsThis 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.ResultsBased 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.ConclusionsIn 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.