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Background We aimed to use Mendelian randomization (MR) to determine the causality between fifteen major mental disorders (MDs) and benign prostatic hyperplasia (BPH), prostate cancer (PCa), and prostatitis.Methods The main MR analysis was performed using the inverse variance-weighted (IVW) method.Results The study found that insomnia (odds ratio [OR], 1.6190; p = .0017) was significantly associated with an increased risk of BPH, and mood disorders (OR, 1.1590; p = .0221) was nominally associated with an increased risk of BPH. Conversely, BPH was suggestively associated with a low epilepsy risk (OR, 0.9988; p = .0043), and was nominally associated with an increased risk of insomnia (OR, 1.0061; p = .0291). Furthermore, attention deficit hyperactivity disorder (ADHD) was suggestively associated with a low PCa risk (OR = 0.9474; p = .0058). However, no causal relationship was observed between PCa and MDs. Finally, anorexia nervosa (OR, 1.1686; p = .0248) and depression (OR, 336.5383; p = .0308) were nominally positively correlated with prostatitis. Prostatitis was suggestively associated with increased risk of ADHD (OR, 1.0868; p = .0413).Conclusion Our findings provide clinicians with a basis for developing programs to prevent or treat MDs and prostatic diseases.

Abstract Bone metastasis is an essential factor affecting the prognosis of prostate cancer (PCa), and circulating tumor cells (CTCs) are closely related to distant tumor metastasis. Here, the protein-protein interaction (PPI) networks and Cytoscape application were used to identify diagnostic markers for metastatic events in PCa. We screened ten hub genes, eight of which had area under the ROC curve (AUC) values > 0.85. Subsequently, we aim to develop a bone metastasis-related model relying on differentially expressed genes in CTCs for accurate risk stratification. We developed an integrative program based on machine learning algorithm combinations to construct reliable bone metastasis-related genes prognostic index (BMGPI). On the basis of BMGPI, we carefully evaluated the prognostic outcomes, functional status, tumor immune microenvironment, somatic mutation, copy number variation (CNV), response to immunotherapy and drug sensitivity in different subgroups. BMGPI was an independent risk factor for disease-free survival in PCa. The high risk group demonstrated poor survival as well as higher immune scores, higher tumor mutation burden (TMB), more frequent co-occurrence mutation, and worse efficacy of immunotherapy. This study highlights a new prognostic signature, the BMGPI. BMGPI is an independent predictor of prognosis in PCa patients and is closely associated with the immune microenvironment and the efficacy of immunotherapy.

BackgroundNon-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility.MethodsNOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein–protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA–microRNA (miRNA)–transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression.ResultsTwo immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in toll-like receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data.ConclusionIt appears that SHC1 and FGFR1 could be significant immune-related oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility.

BackgroundAlthough studies on the effects of diet on fertility has progressed, some cumulative evidence has piled against popular hypotheses. The aim of our study was to investigate the effects of 31 diets including 23 individual dietary intakes and 8 dietary habits on infertility in men and women.MethodsThe datas of diets and infertility were collected from genome-wide association studies (GWAS). Mendelian randomization (MR) methods were used to analyze causal relationships. Multivariate MR (MVMR) adjusted for the effects of other exposures on causality. And MR-Egger, Cochran’s Q, radial MR, and MR-PRESSO tests were employed to assess heterogeneity and horizontal pleiotropy.ResultsOur study found that coffee intake (OR, 3.6967; 95% CI, 1.0348 – 13.2065; P = 0.0442) and cooked vegetable intakes (OR, 54.7865; 95% CI, 2.9011 – 1030.5500; P = 0.0076) increased the risk of male infertility. For women, beer was a risk factor for infertility (OR, 4.0932; 95% CI, 1.8728 – 8.9461; P = 0.0004); but processed meat was negatively associated with infertility (OR, 0.5148; 95% CI, 0.2730 – 0.9705; P = 0.0401). MVMR demonstrated selenium as a protective factor against female infertility (OR, 7.4474e-12; 95% CI, 5.4780e-22 – 1.0125e-01; P = 0.0314).ConclusionWe found the causal relationships between four diets and infertility. We look forward to more high-quality epidemiologic studies to prove our conclusions.

BackgroundThere 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.MethodsWe 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.ResultsOur 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.ConclusionThis 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.

IntroductionTestosterone 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.MethodsRandomized 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.ResultsThe 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 (

BackgroundStudies 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.MethodsWe 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.ResultsFour 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.ConclusionrPFS 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 registrationhttps://inplasy.com, identifier INPLASY2023100072.

BackgroundCOVID-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.MethodsWe 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.ResultsWe 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.ConclusionOur 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.

BackgroundThe 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.MethodsThe 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.ResultsForty-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.ConclusionThe 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.

Background: Non-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility. Methods: NOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein-protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA-microRNA (miRNA)-transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression. Results: Two immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in tolllike receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data. Conclusion: It appears that SHC1 and FGFR1 could be significant immunerelated oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility. [ABSTRACT FROM AUTHOR]