An updated systematic review and meta-analysis of the effects of testosterone replacement therapy on erectile function and prostate.

Introduction: Testosterone replacement therapy (TRT) is a generally accepted method treating for aging-related late-onset hypogonadism (LOH). However, the efficacy and safety of TRT remain controversial. An updated systematic review and meta-analysis aimed to determine the effectiveness and security of TRT treating for LOH. Methods: Randomized controlled trials (RCTs) of TRT for LOH were searched in the databases of Pubmed, Embase, Clinicaltrials.gov and Cochrane from 1990 to 2023 and an updated meta-analysis was conducted. Results: The results of 28 RCTs involving 3461 patients were included and scrutinized in this analysis. Among these, 11 RCTs were of long-term duration (≥12 months), while 18 RCTs were short-term studies (<12 months) comparing TRT with a placebo. TRT modalities comprised injection, oral administration, and transdermal administration. International Index of Erectile Function (IIEF) (Weighted Mean difference (WMD) 3.26; 95%; 95% confidence interval (CI) 1.65-4.88; P<0.0001) was obviously improved in the TRT group. International Prostate Symptom Score (IPSS) (WMD 0.00; 95% CI -0.45-0.45; P=1.0), Prostate Volume (PV) (WMD 0.38; 95% CI -0.64-1.41; P=0.46), Maximum Flow Rate (Qmax) (WMD 1.86; 95% CI -0.98-4.69; P=0.20), Postvoid Residual Urine Volume (PVR) (WMD 3.20; 95% CI -5.87-12.28; P=0.49) and Prostate-Specific Antigen (PSA) (WMD 0.08; 95% CI -0.00-0.17; P=0.06) were not significantly statistical between two groups. Conclusion: This meta-analysis reveals that TRT could improve the IIEF score of hypogonadal men without detriment to the IPSS score, PV, Qmax, PVR and PSA regardless of the administration method or duration of treatment.The meta-analysis was registered at PROSPERO (CRD42023413434).

Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a novel lncRNAs-related model to predict prognosis of ccRCC patients. METHODS: The data were obtained from public databases. We identified CCNB2 in ccRCC using Kaplan-Meier survival analysis, univariate and multivariate Cox regression, and Gene Ontology analysis. External validation was then performed. The risk model was constructed based on prognostic lncRNAs by the LASSO algorithm and multivariate Cox regression. Receiver operating characteristics (ROC) curves were used to evaluate the model. Consensus clustering analysis was performed to re-stratify the patients. Finally, we analyzed the tumor-immune microenvironment and performed screening of potential drugs. RESULTS: CCNB2 associated with late clinicopathological parameters and poor prognosis in ccRCC and was an independent predictor for disease-free survival. In addition, CCNB2 shared the same expression pattern with known suppressive immune checkpoints. A risk model dependent on the expression of three prognostic CCNB2-related lncRNAs (SNHG17, VPS9D1-AS1, and ZMIZ1-AS1) was constructed. The risk signature was an independent predictor of ccRCC. The area under the ROC (AUC) curve for overall survival at 1-, 3-, 5-, and 8-year was 0.704, 0.702, 0.741, and 0.763. The high-risk group and cluster 2 had stronger immunogenicity and were more sensitive to immunotherapy. CONCLUSION: CCNB2 could be an important biomarker for predicting prognosis in ccRCC patients. Furthermore, we developed a novel lncRNAs-related risk model and identified two CCNB2-related molecular clusters. The risk model performed well in predicting overall survival and immunological microenvironment of ccRCC.

Association between circulating immune cells and the risk of prostate cancer: a Mendelian randomization study.

Background: There is still limited research on the association between immune cells and the risk of prostate cancer. Further investigations are warranted to comprehend the intricate associations at play. Methods: We used a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between immune cell phenotypes and prostate cancer. The summary data for immune cell phenotypes was derived from a study cohort, including 3,757 individuals from Sardinia with data on 731 immune cell phenotypes. The summary data for prostate cancer were obtained from the UK Biobank database. Sensitivity analyses were conducted, and the combination of MR-Egger and MR-Presso was used to assess horizontal pleiotropy. Cochran's Q test was employed to evaluate heterogeneity, and the results were subjected to FDR correction. Results: Our study identified two immune cell phenotypes significantly associated with the risk of prostate cancer, namely CD25 on naive-mature B cells (OR = 0.998, 95% CI, 0.997-0.999, P = 2.33E-05, FDR = 0.017) and HLA DR on CD14- CD16- cells (OR = 1.001, 95% CI, 1.000-1.002, P = 8.01E-05, FDR = 0.03). When adjusting FDR to 0.2, we additionally found six immune cell phenotypes influencing the incidence of prostate cancer. These include FSC-A on B cells (OR = 1.002, 95% CI, 1.001-1.002, P = 7.77E-04, FDR = 0.133), HLA DR on plasmacytoid dendritic cells (OR = 1.001, 95% CI, 1.000-1.001, P = 0.001, FDR = 0.133), CD14+ CD16- monocyte % monocytes (OR = 1.002, 95% CI, 1.001-1.003, P = 0.001, FDR = 0.133), and HVEM on effector memory CD4+ T cells (OR = 1.001, 95% CI, 1.000-1.002, P = 0.002, FDR = 0.169), which are positively correlated with the risk of prostate cancer. Conversely, CD25 on IgD+ B cells (OR = 0.998, 95% CI, 0.997-0.999, P = 0.002, FDR = 0.169) and Monocytic Myeloid-Derived Suppressor Cells AC (OR = 0.999, 95% CI, 0.999-1.000, P = 0.002, FDR = 0.17) are negatively correlated with the risk of prostate cancer. Conclusion: This study has revealed causal relationships between immune cell phenotypes and prostate cancer, supplying novel insights that might aid in identifying potential therapeutic targets of prostate cancer.

Comparative efficacy of olaparib in combination with or without novel antiandrogens for treating metastatic castration-resistant prostate cancer.

Background: Studies using novel antiandrogens (NAA) in patients with metastatic castration-resistant prostate cancer (mCRPC) have shown overall survival benefit. As patients develop resistance to NAA therapy, the poly(ADP-ribose) polymerase inhibitor (PARPi) olaparib in combination with NAA may become a promising therapy. However the overall benefit of olaparib monotherapy or combination therapy still needs to be evaluated. Therefore, we performed a network meta-analysis to assess the efficacy and toxicity between olaparib, olaparib combined with abiraterone and NAA. Methods: We searched PubMed, EMBASE, the Cochrane Library and American Society of Clinical Oncology (ASCO) University Meeting abstracts for randomized controlled trials reporting olaparib and NAA from 2010 up to March, 2023. Network meta-analysis using Stata 16.0 and R 4.4.2, hazard ratios (HR) with 95% confidence intervals (CI) were used to assess the results. Results: Four trials reported olaparib, olaparib plus abiraterone and apalutamide plus abiraterone. radiographic progression-free survival (rPFS) was significantly lower in patients on apalutamide plus abiraterone compared to olaparib (HR, 1.43; 95% CI, 1.06-1.93). rPFS was similar for olaparib plus abiraterone and olaparib (HR, 1.35; 95% CI, 0.99-1.84); likewise, olaparib plus abiraterone and apalutamide plus abiraterone were similar (HR, 1.06; 95% CI, 0.83-1.35). In addition, there was no significant difference between the three interventions for OS. But olaparib has the highest probability of being a preferred treatment for improving rPFS and OS. Conclusion: rPFS was in favor of olaparib compared with apalutamide plus abiraterone. But there were no difference between olaparib plus abiraterone and either olaparib or apalutamide plus abiraterone. Apalutamide plus abiraterone might be the most preferred intervention in cases where AEs are involved. Systematic review registration: https://inplasy.com, identifier INPLASY2023100072.

Development and validation of a disulfidptosis and M2 TAM-related classifier for bladder cancer to explore tumor subtypes, immune landscape and drug treatment.

BACKGROUND: Disulfidptosis, as a new mode of programmed cell death, is closely associated with tumorigenesis. Meanwhile, M2 tumor-associated macrophage (TAM) plays an important role in tumor progression. Here, we propose to combine these two perspectives to detect novel disulfidptosis and M2 TAM-related biomarkers in bladder cancer (BCa) to identify various tumor subtypes, construct prognostic features, reveal immune and somatic mutational landscapes, and screen for drugs in BCa. METHODS: We used weighted gene co-expression network analysis (WGCNA) to mine M2 TAM-related genes. Consensus unsupervised clustering was performed to identify potential tumor subtypes. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were utilized to build the risk model. We then explored the immune cell, immune function, immune checkpoint expression patterns and somatic mutational landscape in clusters and risk groups. In addition, we performed sensitivity analysis for anti-cancer drugs. RESULTS: We identified 3057 M2 TAM-related genes and intersected them with disulfidptosis-related genes to obtain 95 disulfidptosis and M2 TAM-related genes (DMRGs). In terms of tumor subtypes, two molecular clusters were identified. Cluster 1 showed stronger immunogenicity and higher tumor mutational burden (TMB). We also predicted 50 drugs with high sensitivity in cluster 1. On the basis of risk grouping, the high-risk group had poor overall survival in the training, test, and validation groups. Ten screened anti-cancer drugs were more sensitive in the high-risk group. A nomogram predicting survival of BCa patients was also established. CONCLUSION: By combining two hotspot perspectives, disulfidptosis and M2 TAM, we provide a valuable risk score signature for establishing individualized treatment regimens and drug choices. The risk score may serve as an independent risk factor for BCa patients.

Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.

Background: COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms. Methods: We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the "Limma" package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses. Results: We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH. Conclusion: Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them.

Metabolic syndrome-related prognostic index: Predicting biochemical recurrence and differentiating between cold and hot tumors in prostate cancer.

Background: The prostate, as an endocrine and reproductive organ, undergoes complex hormonal and metabolic changes. Recent studies have shown a potential relationship between metabolic syndrome and the progression and recurrence of prostate cancer (PCa). This study aimed to construct a metabolic syndrome-related prognostic index (MSRPI) to predict biochemical recurrence-free survival (BFS) in patients with PCa and to identify cold and hot tumors to improve individualized treatment for patients with PCa. Methods: The Cancer Genome Atlas database provided training and test data, and the Gene Expression Omnibus database provided validation data. We extracted prognostic differentially expressed metabolic syndrome-related genes (DEMSRGs) related to BFS using univariate Cox analysis and identified potential tumor subtypes by consensus clustering. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression were used to construct the MSRPI. We further validated the predictive power of the MSRPI using KaplanMeier survival analysis and receiver operating characteristic (ROC) curves, both internally and externally. Drug sensitivity was predicted using the half-maximal inhibitory concentration (IC50). Finally, we explored the landscape of somatic mutations in the risk groups. Results: Forty-six prognostic DEMSRGs and two metabolic syndrome-associated molecular clusters were identified. Cluster 2 was more immunogenic. Seven metabolic syndrome-related genes (CSF3R, TMEM132A, STAB1, VIM, DUOXA1, PILRB, and SLC2A4) were used to construct risk equations. The high-risk index was significantly associated with a poor BFS, which was also validated in the validation cohort. The area under the ROC curve (AUC) for BFS at 1-, 3-, and 5- year in the entire cohort was 0.819, 0.785, and 0.772, respectively, demonstrating the excellent predictive power of the MSRPI. Additionally, the MSRPI was found to be an independent prognostic factor for BFS in PCa. More importantly, MSRPI helped differentiate between cold and hot tumors. Hot tumors were associated with the high-risk group. Multiple drugs demonstrated significantly lower IC50 values in the high-risk group, offering the prospect of precision therapy for patients with PCa. Conclusion: The MSRPI developed in this study was able to predict biochemical recurrence in patients with PCa and identify cold and hot tumors. MSRPI has the potential to improve personalized precision treatment.