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. 2018 Apr 11;6(1):70.
doi: 10.1186/s40168-018-0451-2.

Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers

Zhenwei Dai  1   2 , Olabisi Oluwabukola Coker  1   2 , Geicho Nakatsu  1   2 , William K K Wu  1   2 , Liuyang Zhao  1   2 , Zigui Chen  3 , Francis K L Chan  1   2 , Karsten Kristiansen  4   5 , Joseph J Y Sung  1   2 , Sunny Hei Wong  6   7 , Jun Yu  8   9
Affiliations

Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers

Zhenwei Dai et al. Microbiome. .

Abstract

Background: Alterations of gut microbiota are associated with colorectal cancer (CRC) in different populations and several bacterial species were found to contribute to the tumorigenesis. The potential use of gut microbes as markers for early diagnosis has also been reported. However, cohort specific noises may distort the structure of microbial dysbiosis in CRC and lead to inconsistent results among studies. In this regard, our study targeted at exploring changes in gut microbiota that are universal across populations at species level.

Results: Based on the combined analysis of 526 metagenomic samples from Chinese, Austrian, American, and German and French cohorts, seven CRC-enriched bacteria (Bacteroides fragilis, Fusobacterium nucleatum, Porphyromonas asaccharolytica, Parvimonas micra, Prevotella intermedia, Alistipes finegoldii, and Thermanaerovibrio acidaminovorans) have been identified across populations. The seven enriched bacterial markers classified cases from controls with an area under the receiver-operating characteristics curve (AUC) of 0.80 across the different populations. Abundance correlation analysis demonstrated that CRC-enriched and CRC-depleted bacteria respectively formed their own mutualistic networks, in which the latter was disjointed in CRC. The CRC-enriched bacteria have been found to be correlated with lipopolysaccharide and energy biosynthetic pathways.

Conclusions: Our study identified potential diagnostic bacterial markers that are robust across populations, indicating their potential universal use for non-invasive CRC diagnosis. We also elucidated the ecological networks and functional capacities of CRC-associated microbiota.

Keywords: Colorectal cancer; Diagnostic marker; Ecology; Microbiota.

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Conflict of interest statement

Ethics approval and consent to participate

The study conformed to the ethical principles outlined by the Declaration of Helsinki and was approved by the Institutional Review Boards of the Chinese University of Hong Kong.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Microbial composition and statistical power difference across cohorts. a–c Principal coordinate analysis for control samples, CRC samples, and all samples, respectively. (CA, CRC; NC, negative control) The correlations between phenotypes and PCoAs are labeled with their corresponding coordinates. d Statistical power to detect differentially abundant bacteria of various fold change (fold change = 10, 20, and 40%) versus cohort sample size (number of control samples × number of case samples)
Fig. 2
Fig. 2
Differentially abundant bacteria in CRC across cohorts. a Left panel; abundance of 7 CRC-enriched species and 20 CRC-depleted species with largest fold change. The bacteria abundance was normalized to natural log fold change relative to abundance median of control samples. Right panel; confidence interval for individual pair fold change. The confidence intervals were calculated based on Wilcoxon signed-rank test. b Violin graph for the abundance of the 7 CRC-enriched bacteria in different cohorts. Abundance change significance within individual cohort is labeled with * (P < 0.05, *P < 0.01, **P < 0.001, ***). c Prediction power of 7 CRC-enriched bacteria with SVM model
Fig. 3
Fig. 3
Meta-analysis of correlations among CRC-associated bacteria. a Correlation between the 69 CRC differentially abundant bacteria in CRC samples. Nodes having correlations between circles were labeled with dark blue, and the four CRC-enriched oral species were labeled with dark red. Five commensal bacterial species were denoted with triangle shape nodes. The size of the nodes is proportional to their corresponding centrality. Node attributes are included in Additional file 28: Table S3. b Comparison of the correlation network between CRC-depleted bacteria in control and CRC showing the mid-points of histogram bars. Cubic spline was used to connect the points
Fig. 4
Fig. 4
Correlation network between CRC-enriched bacteria and KO categories. Nodes with the same color share the same CRC-enriched pathway. Seven KEGG pathways that were enriched in CRC and involve CRC-enriched bacteria correlated KO categories are listed. The corresponding adjusted P values of the abundance change from control to CRC are also provided. Correlation details are attached in Additional file 29: Table S8 and Additional file 27: Table S9
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