Fecal Microbial Dysbiosis Is Associated with Colorectal Cancer Risk in a Korean Population

Abstract

Purpose: The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking.

Materials and methods: In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2.

Results: There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples.

Conclusion: Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.

Keywords: 16S rRNA gene sequencing; Colorectal neoplasms; Fecal microbiome; Microbial dysbiosis; Microbial metabolic pathways.

Fig. 1.

(A) Comparison of alpha diversity indices based on anatomical sites. (B) Based on cancer stage. (C) Principal coordinate analysis plot of the weighted UniFrac distance. Red indicates colorectal cancer cases and blue indicates healthy controls.

Fig. 2.

(A) Statistical analysis of taxonomic and functional profiles (STAMP) results plot for taxonomic phylum STAMP results plots for colon cancer (B) and rectal cancer (C) at phylum level.

Fig. 3.

Enterotypes identified from the microbial composition of colorectal cancer cases.

Fig. 4.

Comparison of microbial dysbiosis index between each anatomical sites of colorectal cancer cases and controls. (A) Total population. (B) Each sex. (C) Across cancer stages. (D) Across cancer stages by sex.

Fig. 5.

Community detection based on greedy optimization of modularity of the network.