ArticleThe Prostate2021
Genetic polymorphisms at 19q13.33 are associated with [-2]proPSA (p2PSA) levels and provide additional predictive value to prostate health index for prostate cancer.
Article in The Prostate, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Systematic review of instruments for measuring sex and gender attributes: assessment of measurement properties and utility in research on clinical and functional outcomes.Biomedical engineering online · 2026Pooled it
- The Combined Effect of Polygenic Risk Score and Prostate Health Index in Chinese Men Undergoing Prostate Biopsy.Journal of clinical medicine · 2023Article
- Extensive germline-somatic interplay contributes to prostate cancer progression through HNF1B co-option of TMPRSS2-ERG.Nature communications · 2022Article
- Genetic polymorphisms at 19q13.33 are associated with [-2]proPSA (p2PSA) levels and provide additional predictive value to prostate health index for prostate cancer.The Prostate · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate health index (phi), a derivative of [-2]proPSA (p2PSA), has shown better accuracy than prostate-specific antigen (PSA) in prostate cancer (PCa) detection. The present study was to investigate whether previously identified PSA-associated single nucleotide polymorphisms (SNPs) influence p2PSA or phi levels and lead to potential clinical utility.
methodsWe conducted an observational prospective study with 2268 consecutive patients who underwent prostate biopsy in three tertiary medical centers from August 2013 to March 2019. Genotyping data of the 46 candidate genes with a ± 100 kb window were tested for association with p2PSA and phi levels using linear regression. Multivariable logistic regression models were performed and internally validated using repeated tenfold cross-validation. We further calculated personalized phi cutoff values based on the significant genotypes. Discriminative performance was assessed using decision curve analysis and net reclassification improvement (NRI) index.
resultsWe detected 11 significant variants at 19q13.33 which were p2PSA-associated independent of PCa. The most significant SNP, rs198978 in KLK2 (P
conclusionRs198978, is independently associated with p2PSA values, and can improve the diagnostic ability of phi for PCa using personalized cutoff values.
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