Jong Y. Park

This paper develops a germline polygenic score (PRSagg) to predict prostate cancer aggressiveness and validates it across multiple datasets.

The paper identifies 59 differentially methylated genes with concordant epigenetic and transcriptomic changes and 111 DMGs associated with aggressive prostate cancer in Puerto Rican Hispanic/Latino men.

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

This study identifies 5hmC-based gene set activity scores in cfDNA as biomarkers for early detection of ADT resistance in prostate cancer.

The paper identifies a regulatory SNP rs60464856 that increases RUVBL1 expression and contributes to prostate cancer progression through cohesin-CTCF binding and cell-cycle activation.

The paper improves prostate cancer risk stratification across multiple ancestries using a new polygenic hazard score with additional SNPs, showing higher hazard ratios and positive predictive value for clinically significant prostate cancer.

The paper assesses how the performance of an African-ancestry-specific polygenic hazard score varies with local ancestry in 8q24 using genetic data from 6,253 men with African ancestry.

This study evaluates a polygenic hazard score in multi-ethnic populations and finds it is associated with age at prostate cancer diagnosis, aggressive diagnosis, and death across European, Asian, and African ancestries.

This study improves a polygenic hazard score for prostate cancer by adding 120 SNPs, enhancing hazard ratios but not positive predictive value of PSA testing.

The paper improves a polygenic hazard score for prostate cancer age at diagnosis by adding SNPs specific to African ancestry, enhancing performance in this population.

This paper evaluates how sample size affects polygenic hazard score models for prostate cancer, finding that 20-30k samples are needed for discovery models, but 10k suffice if SNPs are pre-established.

The paper evaluates a polygenic hazard score for prostate cancer screening, showing it improves positive predictive value by adjusting screening age based on genetic risk.

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.

This paper develops a genetic risk score to personalize prostate cancer screening age based on polygenic hazard scores and validates its predictive power in a separate dataset.