ReviewMethods in molecular biology (Clifton, N.J.)2026
New Strategies to Uncover Genes Involved in the Development of Prostate Cancer in Families with Multiple Cases.
Review in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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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0 citing papers in PubMed.
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Authors and funding
5 authors.
Funding
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Abstract
Prostate cancer (PC) is the fourth most common cancer worldwide and is recognised as one of the major men's health challenges of the twenty-first century. There has been a steady increase in PC incidence, particularly for advanced-stage PC cases, which have increased by 5% since 2014. Despite advances in diagnostic and treatment techniques, understanding of the genetic mechanisms underlying PC remains limited, especially in families with multiple cases of the disease, where genetic predispositions significantly increase the risk of developing cancer. This chapter provides a comprehensive and up-to-date review of PC, covering key aspects such as its epidemiology, aetiology and associated risk factors. From the hereditary point of view, this chapter covers the main genes associated with the susceptibility to develop PC and related pathologies. Additionally, the chapter evaluates the current paradigm of detection and treatment protocols, while also exploring future research directions.
Indexed as
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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.