| 1. |
- Sigurdardottir, Lara G., et al.
(författare)
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Circadian disruption, sleep loss, and prostate cancer risk : a systematic review of epidemiologic studies
- 2012
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - Philadelphia, USA : American Association for Cancer Research. - 1055-9965 .- 1538-7755. ; 21:7, s. 1002-1011
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Forskningsöversikt (refereegranskat)abstract
- Disruption of the circadian system has been hypothesized to increase cancer risk, either because of direct disruption of the molecular machinery generating circadian rhythms or because of disruption of parameters controlled by the clock such as melatonin levels or sleep duration. This hypothesis has been studied in hormone-dependent cancers among women, but data are sparse about potential effects of circadian disruption on the risk of prostate cancer. This review systematically examines available data evaluating the effects of light at night, sleep patterns, and night shift work on prostate cancer risk.
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| 2. |
- Pettersson, Andreas, et al.
(författare)
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The ABC model of prostate cancer : A conceptual framework for the design and interpretation of prognostic studies
- 2017
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Ingår i: Cancer. - Hoboken, USA : John Wiley & Sons. - 0008-543X .- 1097-0142. ; 123:9, s. 1490-1496
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Forskningsöversikt (refereegranskat)abstract
- There has been limited success in identifying prognostic biomarkers in prostate cancer. A partial explanation may be that insufficient emphasis has been put on clearly defining what type of marker or patient category a biomarker study aims to identify and how different cohort characteristics affect the ability to identify such a marker. In this article, the authors put forth the ABC model of prostate cancer, which defines 3 groups of patients with localized disease that an investigator may seek to identify: patients who, within a given time frame, will not develop metastases even if untreated (category A), will not develop metastases because of radical treatment (category B), or will develop metastases despite radical treatment (category C). The authors demonstrate that follow-up time and prostate-specific antigen screening intensity influence the prevalence of patients in categories A, B, and C in a study cohort, and that prognostic markers must be tested in both treated and untreated cohorts to accurately distinguish the 3 groups. The authors suggest that more emphasis should be put on considering these factors when planning, conducting, and interpreting the results from prostate cancer biomarker studies, and propose the ABC model as a framework to aid in that process.
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