BACKGROUND: Published analyses of prostate cancer nested case-control and survival data in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study cohort suggested that men with higher baseline vitamin D [25(OH)D] concentrations have both (i) increased prostate cancer risk and (ii) decreased prostate cancer-specific fatality.
METHODS: To investigate possible factors responsible for a spurious association with prostate cancer fatality, we reanalysed baseline serum vitamin D associations with prostate cancer risk and prostate cancer-specific fatality in case-control data nested within the ATBC Study (1000 controls and 1000 incident prostate cancer cases). Conditional logistic regression and Cox proportion hazard models were used, respectively, to estimate odds ratios for risk and hazard ratios for prostate cancer-specific fatality, overall and by disease aggressiveness. We replicated these case-control analyses using baseline serum measurements of alpha-tocopherol (vitamin E), beta-carotene and retinol (vitamin A), and used the entire ATBC Study cohort (n = 29 085) to estimate marginal associations between these baseline vitamins and prostate cancer incidence and fatality following blood collection.
RESULTS: Vitamin D analyses agreed closely with those originally published, with opposite risk and fatality associations. By contrast, the analyses of alpha-tocopherol, beta-carotene and retinol yielded concordant associations for prostate cancer incidence and prostate cancer-specific fatality.
CONCLUSIONS: We found evidence of neither artefacts in the nested prostate cancer case-control data set nor detection or collider biases in the fatality analyses. The present findings therefore support a valid inverse (i.e. beneficial) association between vitamin D and prostate cancer-specific survival that warrants further evaluation, including possibly in controlled trials.

BACKGROUND: When a pathogenic BRCA1 or BRCA2 mutation is identified in a family, cascade genetic testing of family members is recommended since the results may inform screening or treatment decisions in men and women. However, rates of cascade testing are low, and men are considerably less likely than women to pursue cascade testing. To facilitate cascade testing in men, we designed a Web-based genetic education tool that addressed barriers to cascade testing, was individually tailored, delivered proactively, and could be used in lieu of pretest genetic counseling to streamline the cascade testing process.
METHODS: We randomized 63 untested men from hereditary cancer families to Web-based genetic education (WGE) versus enhanced usual care (EUC). WGE participants were provided access to a genetic education website after which they could accept or decline genetic testing or opt for pretest genetic counseling. EUC participants received an informational brochure and a letter informing them of their eligibility for genetic testing and recommending they schedule genetic counseling. The primary outcome was the uptake of genetic testing.
RESULTS: Men in the WGE group were more likely to complete genetic counseling and/or genetic testing (43% vs. 12.1%; χ2 [n = 63, df = 1] = 7.77, p = 0.005). WGE participants were also more likely to complete genetic testing compared to men in the EUC group (30% vs. 9.1%; χ2 [n = 63, df = 1] = 4.46, p = 0.03).
CONCLUSIONS: This preliminary trial suggests that a streamlined approach to genetic testing using proactively delivered genetic education may reduce barriers to cascade testing for at-risk men, leading to increased uptake. These results should be interpreted cautiously given the select sample and high rate of non-response.

OBJECTIVE: The aims of the study were to construct a new prognostic prediction model for detecting prostate cancer (PCa) patients using machine-learning (ML) techniques and to compare those models across systematic and target biopsy detection techniques.
METHODS: The records of the two main hospitals in Riyadh, Saudi Arabia, were analyzed for data on diagnosed PCa from 2019 to 2023. Four ML algorithms were utilized for the prediction and classification of PCa.
RESULTS: A total of 528 patients with prostate-specific antigen (PSA) greater than 3.5 ng/mL who had undergone transrectal ultrasound-guided prostate biopsy were evaluated. The total number of confirmed PCa cases was 234. Age, prostate volume, PSA, body mass index (BMI), multiparametric magnetic resonance imaging (mpMRI) score, number of regions of interest detected in MRI, and the diameter of the largest size lesion were significantly associated with PCa. Random Forest (RF) and XGBoost (XGB) (ML algorithms) accurately predicted PCa. Yet, their performance for classification and prediction of PCa was higher and more accurate for cases detected by targeted and combined biopsy (systematic and targeted together) compared to systematic biopsy alone. F1, the area under the curve (AUC), and the accuracy of XGB and RF models for targeted biopsy and combined biopsy ranged from 0.94 to 0.97 compared to the AUC of systematic biopsy for RF and XGB algorithms, respectively.
CONCLUSIONS: The RF model generated and presented an excellent prediction capability for the risk of PCa detected by targeted and combined biopsy compared to systematic biopsy alone. ML models can prevent missed PCa diagnoses by serving as a screening tool.

UNASSIGNED: Epidemiological reports indicate a potential association between androgenic alopecia (AGA) and increased prostate cancer (PC) prevalence, but conflicting reports also exist. This study aims to elucidate the causality of AGA on PC risk using Mendelian randomization (MR) analysis.
UNASSIGNED: Two-sample MR analyses utilized public genome-wide association studies summary data for single-nucleotide polymorphisms associated with AGA. Four statistical methods were used: inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode, with IVW as the preliminary estimation method. Additionally, sensitivity analyses were conducted to address pleiotropic bias.
UNASSIGNED: Genetically proxied AGA did not demonstrate a causal effect on PC risk (IVW P > 0.05). Consistently, complementary methods yielded results aligned with IVW.
UNASSIGNED: Our MR analysis indicates no causal relationship between genetically predicted AGA and PC risk, suggesting that observed associations in epidemiological studies may not be causal.

UNASSIGNED: Building on previous research demonstrating better prostate cancer (PC) diagnostics via a biomarker-enhanced approach, this study focuses on cost analysis of PC care using the Stockholm3 test. We assessed the economic impact in European health care systems using real-world evidence for diagnostic outcomes and relevant costs.
UNASSIGNED: We evaluated two PC diagnostic strategies: (1) the conventional prostate-specific antigen (PSA) strategy with magnetic resonance imaging (MRI) and (2) PSA testing with a reflex to biomarkers at PSA ≥1.5 ng/ml in guiding decisions to perform MRI. Data from the Swedish National Prostate Cancer Register and Capio St. Göran Hospital provided real-world evidence, supplemented by health economic modeling. A comprehensive cost analysis was conducted using a Markov model for treatment pathways for four PC disease states and overall spending, for which costs from various European health care systems were used. A deterministic sensitivity analysis was performed across different cost and diagnostic scenarios.
UNASSIGNED: The average cost for the four disease states was €2 182 for benign disease, €10 023 for low-grade disease, €13 073 for intermediate- to high-grade localized or locally advance disease, and €271 210 for metastatic disease. The overall spending was €358 239 (7.7%) lower per 1000 men tested in the biomarker-enhanced strategy in comparison to the PSA strategy. The primary cost saving was attributed to lower treatment expenses for metastatic disease. Sensitivity analysis affirmed the robustness of the findings across various diagnostic and treatment scenarios.
UNASSIGNED: Biomarker-enhanced diagnostic strategies may reduce health care costs for PC management and are likely to improve quality-adjusted life years in a scenario in which metastatic disease is reduced.
UNASSIGNED: We explored different ways to detect prostate cancer more cost-effectively. We found that using a specific blood test, called Stockholm3, after a PSA (prostate-specific antigen) test to decide if an MRI scan (magnetic resonance imaging) is necessary could save money, mainly by identifying localized cancer earlier and reducing the need for expensive treatments for advanced cancer.

OBJECTIVE: Prostate cancer (PCa) is lethal. Our aim in this retrospective cohort study was to use machine learning-based methodology to predict PCa risk in patients with benign prostate hyperplasia (BPH), identify potential risk factors, and optimize predictive performance.
METHODS: The dataset was extracted from a clinical information database of patients at a single institute from January 2000 to December 2020. Patients newly diagnosed with BPH and prescribed alpha blockers/5-alpha-reductase inhibitors were enrolled. Patients were excluded if they had a previous diagnosis of any cancer or were diagnosed with PCa within 1 month of enrolment. The study endpoint was PCa diagnosis. The study utilized the extreme gradient boosting (XGB), support vector machine (SVM) and K-nearest neighbors (KNN) machine-learning algorithms for analysis.
RESULTS: The dataset used in this study included 5,122 medical records of patients with and without PCa, with 19 patient characteristics. The SVM and XGB models performed better than the KNN model in terms of accuracy and area under curve. Local interpretable model-agnostic explanation and Shapley additive explanations analysis showed that body mass index (BMI) and late prostate-specific antigen (PSA) were important features for the SVM model, while PSA velocity, late PSA, and BMI were important features for the XGB model. Use of 5-alpha-reductase inhibitor was associated with a higher incidence of PCa, with similar survival outcomes compared to non-users.
CONCLUSIONS: Machine learning can enhance personalized PCa risk assessments for patients with BPH but more research is necessary to refine these models and address data biases. Clinicians should use them as supplementary tools alongside traditional screening methods.

The association between metformin use and risk of prostate cancer remains controversial, while data from randomized trials is lacking. We aim to evaluate the association of genetically proxied metformin effects with prostate cancer risk using a drug-target Mendelian randomization (MR) approach. Summary statistics for prostate cancer were obtained from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome Consortium (79,148 cases and 61,106 controls). Cis-expression quantitative trait loci (cis-eQTL) variants in the gene targets of metformin were identified in the GTEx project and eQTLGen consortium. We also obtained male-specific genome-wide association study data for type 2 diabetes, body mass index (BMI), total testosterone, bioavailable testosterone, estradiol, and sex hormone binding globulin for mediation analysis. Inverse-variance weighted (IVW) regression, weighted median, MR-Egger regression, and MR-PRESSO were performed in the main MR analysis. Multivariable MR was used to identify potential mediators and genetic colocalization analysis was performed to assess any shared genetic basis between two traits of interest. We found that genetically proxied metformin effects (1-SD HbA1c reduction, equivalent to 6.75 mmol/mol) were associated with higher risk of prostate cancer (odds ratioIVW [ORIVW]: 1.55, 95% confidence interval, CI: 1.23-1.96, p = 3.0 × 10-3). Two metformin targets, mitochondrial complex I (ORIVW: 1.48, 95% CI: 1.07-2.03, p = 0.016) and gamma-secretase complex (ORIVW: 2.58, 95%CI :1.47-4.55, p = 0.001), showed robust associations with prostate cancer risk, and their effects were partly mediated through BMI (16.4%) and total testosterone levels (34.3%), respectively. These results were further supported by colocalization analysis that expressions of NDUFA13 and BMI, APH1A, and total testosterone may be influenced by shared genetic factors, respectively. In summary, our study indicated that genetically proxied metformin effects may be associated with an increased risk of prostate cancer. Repurposing metformin for prostate cancer prevention in general populations is not supported by our findings.

UNASSIGNED: The Capio Prostate Cancer Center (Capio PCC) in Stockholm, Sweden, adopts a comprehensive diagnostic approach, utilizing prostate-specific antigen (PSA), Stockholm3, and magnetic resonance imaging (MRI) for prostate cancer risk assessment, followed by targeted and systematic biopsies for high-risk cases.
UNASSIGNED: This study aims to elucidate the clinical process and real-world outcomes of the Capio PCC model for prostate cancer diagnosis at Capio S:t Göran Hospital.
UNASSIGNED: Between 2018 and 2022, a cohort of 12 406 men aged 45-75 yr underwent prostate cancer testing, adhering to Capio PCC\'s structured diagnostic protocol.
UNASSIGNED: We provide a comprehensive description of the Capio PCC model and present results from its implementation, including assessments of PSA, Stockholm3, MRI scans, and biopsies. A comparative analysis is conducted between the diagnostic outcomes obtained at Capio PCC and those obtained at other regions in Sweden.
UNASSIGNED: The median participant age was 61 yr (interquartile range [IQR]: 55-67), with PSA levels at 1.6 ng/ml (IQR: 0.8-3.3) and Stockholm3 scores at 4 (IQR: 3-11). Among 1064 men (8.6%) undergoing biopsies, 611 (57% of biopsied) were diagnosed with International Society of Urological Pathology grade ≥ 2 cancer. Notably, employing a Stockholm3 ≥ 15 cutoff for biopsy, in lieu of PSA ≥ 3 ng/ml, reduced biopsy recommendations by 43%. For men with PSA levels between 1.5 and 2.9 ng/ml, 360 (12%) exhibited Stockholm3 scores of ≥ 15, with 72 (56% of biopsied) diagnosed with clinically significant prostate cancer. A comparative analysis with national Swedish prostate cancer detection data indicated that the Capio PCC model (vs Sweden) revealed a distribution of 14% (vs 25%) low-risk, 59% (vs 42%) intermediate-risk, and 26% (vs 30%) high-risk and advanced cancers.
UNASSIGNED: This study underscores the effectiveness of the protocol-driven diagnostic process at Capio PCC, enabling earlier detection of intermediate-risk prostate cancer and reducing the need for MRI assessments compared with standard prostate cancer care in Sweden.
UNASSIGNED: At the Capio Prostate Cancer Center, a novel diagnostic approach incorporating prostate-specific antigen, Stockholm3, magnetic resonance imaging, and targeted biopsies has been implemented to enhance prostate cancer testing and diagnosis in Stockholm, Sweden.

It is unclear if the association between rheumatoid arthritis (RA) and a higher risk of prostate cancer (Pca) reflects a causal relationship. We conducted a meta-analysis and used the Mendelian randomization method (MR) to evaluate the association between RA and Pca risk. A meta-analysis and subgroup analysis of the incidence of Pca in patients with RA was conducted. To determine whether genetically elevated RA levels were causally linked to Pca, two MR samples were employed. To eliminate gender-related bias, we conducted a stratified analysis of the GWAS data for RA by gender, specifically including 140,254 males. Additional MR analysis was also performed to determine potential confounding factors influencing the association between genetically susceptible RA and Pca. In total, 409,950 participants were enrolled in 20 trials to investigate the Pca risk in patients with RA. The meta-analysis suggested that RA was unrelated to the Pca risk (SIR = 1.072, 95% CI, 0.883-1.261). However, a subgroup analysis showed that low smoking rates might increase the Pca risk in patients with RA by 24%. The MR analysis showed that increased genetic susceptibility to RA was related to a high Pca risk (OR = 36.20, 95%CI = 1.24-1053.12, P = 0.037). The causality estimation of MR-Egger, Weighted mode, Simple mode, and Weighted median method were similar in direction and magnitude. Although our meta-analysis found no correlation between RA and Pca risk, MR analyses supported a causal relationship between genetic susceptibility to RA and increased prostate risk. Early attention to Pca risk in patients with RA may be important for improving prognosis and mortality in such patients. Further research is needed to determine the etiology of RA attributed to Pca and its underlying mechanisms.

Genome-wide association studies along with expression quantitative trait locus (eQTL) mapping have identified hundreds of single-nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PCa), yet functional characterization of these risk loci remains challenging. To screen for potential regulatory SNPs, we designed a CRISPRi library containing 9,133 guide RNAs (gRNAs) to cover 2,166 candidate SNP loci implicated in PCa and identified 117 SNPs that could regulate 90 genes for PCa cell growth advantage. Among these, rs60464856 was covered by multiple gRNAs significantly depleted in screening (FDR < 0.05). Pooled SNP association analysis in the PRACTICAL and FinnGen cohorts showed significantly higher PCa risk for the rs60464856 G allele (p value = 1.2 × 10-16 and 3.2 × 10-7, respectively). Subsequent eQTL analysis revealed that the G allele is associated with increased RUVBL1 expression in multiple datasets. Further CRISPRi and xCas9 base editing confirmed that the rs60464856 G allele leads to elevated RUVBL1 expression. Furthermore, SILAC-based proteomic analysis demonstrated allelic binding of cohesin subunits at the rs60464856 region, where the HiC dataset showed consistent chromatin interactions in prostate cell lines. RUVBL1 depletion inhibited PCa cell proliferation and tumor growth in a xenograft mouse model. Gene-set enrichment analysis suggested an association of RUVBL1 expression with cell-cycle-related pathways. Increased expression of RUVBL1 and activation of cell-cycle pathways were correlated with poor PCa survival in TCGA datasets. Our CRISPRi screening prioritized about one hundred regulatory SNPs essential for prostate cell proliferation. In combination with proteomics and functional studies, we characterized the mechanistic role of rs60464856 and RUVBL1 in PCa progression.