State of the Art: The Microbiome in Bladder Cancer

被引:0
作者
Isali, Ilaha [1 ]
Almassi, Nima [2 ]
Nizam, Amanda [3 ]
Campbell, Rebecca [2 ]
Weight, Christopher [2 ]
Gupta, Shilpa [3 ]
Pooja, Ghatalia [4 ]
Fulmes, Antoniy [2 ]
Mishra, Kirtishri [5 ]
Abbosh, Philip [4 ]
Bukavina, Laura [2 ]
机构
[1] Weill Cornell Med, Dept Urol, New York, NY USA
[2] Cleveland Clin, Glickman Urol & Kidney Inst, Cleveland, OH 44195 USA
[3] Cleveland Clin, Taussig Canc Ctr, Cleveland, OH USA
[4] Fox Chase Canc Ctr, Philadelphia, PA USA
[5] Univ Hosp Cleveland Med Ctr, Dept Urol, Cleveland, OH USA
关键词
Microbiome; Bladder cancer; Urine microbiome; Gut microbiome; Intratumoral microbiome; TISSUE;
D O I
10.1016/j.urolonc.2024.11.008
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
This review assesses the current understanding of the relationship between the human microbiome and BCa. Recognizing how the micro- biome affects the tumor microenvironment provides valuable insights into cancer biology, potentially uncovering interactions that could be leveraged to develop innovative therapeutic approaches. By clarifying these intricate microbial-tumor dynamics, novel targets for micro- biome-based interventions can be identified, ultimately improving treatment effectiveness and patient outcomes. Current literature lacks comprehensive insights into the effects of BCa treatment on the microbiome and the prevalence of immunotherapy-related toxicities. Further research into the microbiome's role in BCa development could bridge the gap between fundamental research and therapeutic applications. Implementing microbiome surveillance, metagenomic sequencing, and metabolomics in clinical trials could deepen our understanding of BCa and its treatment. This review explores the existing understanding of the urine, tissue, and gut microbiomes and their connections to BCa. Enhanced knowledge of these relationships can pave the way for future research to identify reliable disease predictors, prognostic markers, and novel therapeutic targets. (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:199 / 208
页数:10
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