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. 2019 Oct 3;51(10):1-15.
doi: 10.1038/s12276-019-0313-4.

Development of a colorectal cancer diagnostic model and dietary risk assessment through gut microbiome analysis

Jinho Yang #  1   2 , Andrea McDowell #  1 , Eun Kyoung Kim  1 , Hochan Seo  1 , Won Hee Lee  1 , Chang-Mo Moon  3 , Sung-Min Kym  4 , Dong Ho Lee  5 , Young Soo Park  5 , Young-Koo Jee  6 , Yoon-Keun Kim  7
Affiliations

Development of a colorectal cancer diagnostic model and dietary risk assessment through gut microbiome analysis

Jinho Yang et al. Exp Mol Med. .

"V体育官网入口" Abstract

Colorectal cancer (CRC) is the third most common form of cancer and poses a critical public health threat due to the global spread of westernized diets high in meat, cholesterol, and fat. Although the link between diet and colorectal cancer has been well established, the mediating role of the gut microbiota remains elusive. In this study, we sought to elucidate the connection between the gut microbiota, diet, and CRC through metagenomic analysis of bacteria isolated from the stool of CRC (n = 89) and healthy (n = 161) subjects. This analysis yielded a dozen genera that were significantly altered in CRC patients, including increased Bacteroides, Fusobacterium, Dorea, and Porphyromonas prevalence and diminished Pseudomonas, Prevotella, Acinetobacter, and Catenibacterium carriage VSports手机版. Based on these altered genera, we developed two novel CRC diagnostic models through stepwise selection and a simplified model using two increased and two decreased genera. As both models yielded strong AUC values above 0. 8, the simplified model was applied to assess diet-based CRC risk in mice. Mice fed a westernized high-fat diet (HFD) showed greater CRC risk than mice fed a regular chow diet. Furthermore, we found that nonglutinous rice, glutinous rice, and sorghum consumption reduced CRC risk in HFD-fed mice. Collectively, these findings support the critical mediating role of the gut microbiota in diet-induced CRC risk as well as the potential of dietary grain intake to reduce microbiota-associated CRC risk. Further study is required to validate the diagnostic prediction models developed in this study as well as the preventive potential of grain consumption to reduce CRC risk. .

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VSports在线直播 - Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1. Alpha diversity and phylum-level gut microbiota composition.
a Estimated species richness (Chao1 measure) and b alpha diversity defined by Shannon’s index. c Heatmap of the gut microbiota at the phylum level, with columns representing individual control and CRC stool samples and rows corresponding to the identified phyla. Color scale based on relative OTU abundance, and hierarchical clustering based on Euclidean distance. d Average relative abundance of individual phyla, with error bars representing the standard error (SE). Significance between groups assessed by a t test (* = Ad. p < 0.05, ** = Ad. p < 0.01)
Fig. 2
Fig. 2. Composition of the gut microbiota at the class, order and family levels.
a The left-side heatmap plots and hierarchical clustering dendrograms show the gut microbiota composition between individual control and CRC samples at the a class, b order, and c family levels. Relative abundances of individual taxa (rows) in each sample (columns) are indicated in the associated color scale. Right-side bar plots highlight the differing average relative abundance of individual key taxa between control and CRC subject stool microbiota at the a class, b order, and c family levels. Significant differences were calculated by a t test (* = Ad. p < 0.05, ** = Ad. p < 0.01)
Fig. 3
Fig. 3. Genus level gut microbiota composition and CRC diagnostic prediction model.
a Heatmap and clustering of individual control and CRC samples with a color scale indicating relative abundance at the genus level and hierarchical clustering measured by Euclidean distance. b Bar graph displaying the relative abundance of select genera and error bars showing the standard error (SE). Significance between control and CRC groups determined through Student’s t test (* = Ad. p < 0.05, ** = Ad. p < 0.01). c ROC curves of CRC diagnostic prediction models developed through stepwise selection of significantly altered genera (D1-model) and two increased and two decreased genera (D2-model). Models were validated by a 10-fold cross-validation method to assess the area under the curve (AUC), sensitivity, specificity, and accuracy of each model
Fig. 4
Fig. 4. Gut microbiota composition and CRC risk differed between RCD and HFD mice.
Heatmap and hierarchical clustering of gut microbiota relative abundance of individual control regular chow diet-fed (RCD) and high-fat diet-fed (HFD) mouse stool samples at the a phylum and c genus levels. The average relative abundances of individual taxa identified in RCD and HFD mouse stool at the b phylum and d genus levels. Standard errors (SEs) represented by error bars and significant differences between groups measured by the Mann–Whitney test (* = Ad. p < 0.05, ** = Ad. p < 0.01). e Predicted values of CRC risk in RCD and HFD mice are based on the D2 model
Fig. 5
Fig. 5. HFD mouse gut microbiota composition and associated CRC risk modulated by grain consumption.
Heatmap and hierarchical clustering depicts the differential microbiome relative abundance of HFD mouse stool after consumption of seven different grains at the a phylum and b genus levels. Rows represent taxa identified in each sample, and columns represent individual samples, grouped by diet type. c The predicted values of CRC risk in HFD mice and HFD mice fed seven different grains using the D2 model
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References (V体育安卓版)

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