Diet and the microbiome as mediators of prostate cancer risk, progression, and therapy response

被引:0
作者
Cruz-Lebron, Angelica
Faiez, Tasnim Syakirah [1 ]
Hess, Megan M. [1 ]
Sfanos, Karen S. [1 ,2 ,3 ]
机构
[1] Johns Hopkins Univ, Sch Med, Dept Pathol, Baltimore, MD 21218 USA
[2] Sidney Kimmel Comprehens Canc Ctr, Baltimore, MD 21231 USA
[3] Johns Hopkins Univ, Sch Med, Dept Urol, Baltimore, MD 21218 USA
关键词
Prostate cancer; diet; microbiome; inflammation; infection; metabolites; DOUBLE-STRAND BREAKS; PHIP-DNA ADDUCTS; INTESTINAL MICROBIOTA; METABOLIC-ACTIVATION; GUT MICROBIOTA; IN-VIVO; S-PHASE; GROWTH; EQUOL; RECEPTOR;
D O I
10.1016/j.urolonc.2024.12.001
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Complex relationships between the human microbiome and cancer are increasingly recognized for cancer sites that harbor commensal microbial communities such as the gut, genitourinary tract, and skin. For organ sites that likely do not contain commensal microbiota, there is still a substantial capacity for the human-associated microbiota to influence disease etiology across the cancer spectrum. We propose such a relationship for prostate cancer, the most commonly diagnosed cancer in males in the United States. This review explores the current evidence for a role for the urinary and gut microbiota in prostate cancer risk, via both direct interactions (prostate infections) and long-distance interactions such as via the metabolism of procarcinogenic or anticarcinogenic dietary metabolites. We further explore a newly recognized role of the gut microbiota in mediating cancer treatment response or resistance either via production of androgens and/or procarcinogenic metabolites or via direct metabolism of anticancer drugs that are used to treat advanced disease. Overall, we present the current state of knowledge relating to how the human microbiome mediates prostate cancer risk, progression, and therapy response, as well as suggest future research directions for the field. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:209 / 220
页数:12
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