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标题：Inhibition of Tumor Growth by Dietary Indole-3-Carbinol in a Prostate Cancer Xenograft Model May Be Associated with Disrupted Gut Microbial Interactions
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作者：Yanbei Wu ; Robert W. Li ; Haiqiu Huang 等
期刊名称：Nutrients
电子版ISSN：2072-6643
出版年度：2019
卷号：11
期号：2
页码：467-480
DOI：10.3390/nu11020467
出版社：MDPI Publishing
摘要：Accumulated evidence suggests that the cruciferous vegetables-derived compound indole-3-carbinol (I3C) may protect against prostate cancer, but the precise mechanisms underlying its action remain unclear. This study aimed to verify the hypothesis that the beneficial effect of dietary I3C may be due to its modulatory effect on the gut microbiome of mice. Athymic nude mice (5–7 weeks old, male, Balb c/c nu/nu) with established tumor xenografts were fed a basal diet (AIN-93) with or without 1 µmoles I3C/g for 9 weeks. The effects of dietary I3C on gut microbial composition and microbial species interactions were then examined by 16s rRNA gene-based sequencing and co-occurrence network analysis. I3C supplementation significantly inhibited tumor growth (p < 0.0001) and altered the structure of gut microbiome. The abundance of the phylum Deferribacteres, more specifically, Mucispirillum schaedleri, was significantly increased by dietary I3C. Additionally, I3C consumption also changed gut microbial co-occurrence patterns. One of the network modules in the control group, consisting of seven bacteria in family S-27, was positively correlated with tumor size (p < 0.009). Moreover, dietary I3C disrupted microbial interactions and altered this association between specific microbial network and tumor development. Our results unraveled complex relationships among I3C ingestion, gut microbiota, and prostate tumor development and may provide a novel insight into the mechanism for the chemopreventive effect of dietary I3C on prostate cancer.
关键词：indole-3-carbinol; prostate cancer; gut microbiota; microbial interactions; co-occurrence network indole-3-carbinol ; prostate cancer ; gut microbiota ; microbial interactions ; co-occurrence network