ReviewCurrent opinion in urology2015
A genetic-based approach to personalized prostate cancer screening and treatment.
Review in Current opinion in urology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Pooled it
- Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.Scientific reports · 2022Article
- Association between the omentin-1 gene rs2274907 A>T polymorphism and colorectal cancer in the Chinese Han population: a case-control study.The Journal of international medical research · 2021Article
- Post-GWAS in prostate cancer: from genetic association to biological contribution.Nature reviews. Cancer · 2019Review
- Association between polymorphisms in sex hormones synthesis and metabolism and prostate cancer aggressiveness.PloS one · 2017Article
- Association of HMGB1 Gene Polymorphisms with Risk of Colorectal Cancer in a Chinese Population.Medical science monitor : international medical journal of experimental and clinical research · 2016Article
- What's new in screening in 2015?Current opinion in urology · 2016Review
- Glycogen synthase kinase-3: a potential preventive target for prostate cancer management.Urologic oncology · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
purpose of reviewRecent advances in sequencing technologies have allowed for the identification of genetic variants within germline DNA that can explain a significant portion of the genetic underpinnings of prostate cancer. Despite evidence suggesting that these genetic variants can be used for improved risk stratification, they have not yet been routinely incorporated into routine clinical practice. This review highlights their potential utility in prostate cancer screening. RECENT
findingsThere are now almost 100 genetic variants, called single nucleotide polymorphisms (SNPs) that have been recently found to be associated with the risk of developing prostate cancer. In addition, some of these prostate cancer risk SNPs have also been found to influence prostate specific antigen (PSA) expression levels and potentially aggressive disease. SUMMARY: Incorporation of panels of prostate cancer risk SNPs into clinical practice offers potential to provide improvements in patient selection for prostate cancer screening; PSA interpretation (e.g. by correcting for the presence of SNPs that influence PSA expression levels; decision for biopsy (using prostate cancer risk SNPs); and possibly the decision for treatment. A proposed clinical algorithm incorporating these prostate cancer risk SNPs is discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.