An analysis of three different prostate cancer risk calculators applied prior to prostate biopsy: A Turkish cohort validation study

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

ZNF692 promotes cell proliferation, invasion and migration of human prostate cancer cells by targeting the EMT signaling pathway

BACKGROUND

Prostate cancer poses a considerable threat to human health. At present, the mechanism of tumor progression remains unclear. ZNF692 is overexpressed in many tumors, and the high expression of ZNF692 is correlated with tumor aggressiveness and tumor phenotype of prostate cancer, suggesting that ZNF692 may play an important role in tumor biology of prostate cancer. This paper aims to elucidate the relationship between them.

METHODS

The expression level of ZNF692 was verified in normal prostate cells (RWPE-1) and prostate cancer cells (LNCaP, PC3, DU145). PC3 cells were selected to construct the ZNF692 knockout prostate cancer cell line. The changes of cell proliferation, apoptosis, invasion and metastasis were detected by CCK8, Edu staining, Transwell assay and scratch assay. The expression levels of related proteins were detected by Western blot.

RESULTS

At the cellular level, ZNF692 was overexpressed to varying degrees in prostate cancer cell lines, with the highest expression in PC3 cell lines. CCK8 and Edu results showed that the proliferation of prostate cancer PC3 cells that knocked down ZNF692 was slowed. Transwell assay and scratch assay showed reduced invasion and migration of prostate cancer PC3 cells that knocked out ZNF692. Flow cytometry showed that the apoptosis rate of prostate cancer PC3 cells after ZNF692 knockout was increased. In addition, after ZNF692 silencing, the expression level of epithelial phenotype E-cadherin increased in PC3 cells, while the expression level of interstitial phenotype N-cadherin, Vimentin, c-Myc, and CyclinA1 decreased. The state of prostate cancer PC3 cells that overexpressed ZNF692 was reversed from the state after ZNF692 was knocked down.

CONCLUSION

ZNF692 can be used as a new prognostic marker and a potential biologic therapeutic target for PCa. By inhibiting the expression of c-myc and cyclinA1, the EMT signaling pathway is regulated to provide evidence for its potential molecular mechanism.

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

BACKGROUND

We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.

METHODS

Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.

RESULTS

Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.

CONCLUSION

Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.