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Cited by 551Gut microbiota and healthOral microbiology and periodontitis researchClostridium difficile and Clostridium perfringens research

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

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

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Dai, Zhenwei, Coker, Olabisi Oluwabukola, Nakatsu, Geicho, Wu, William K. K., Zhao, Liuyang, Chen, Zigui, Chan, Francis K. L., Kristiansen, Karsten, Sung, Joseph J. Y., Wong, Sunny Hei, Yu, Jun·2018·Microbiome
DOI 10.1186/s40168-018-0451-2PMC PMC5896039
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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.

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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. 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)CC BY · PubMed Central
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. 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 modelCC BY · PubMed Central
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. 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 pointsCC BY · PubMed Central
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
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 S9CC BY · PubMed Central

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Authors
Dai, Z., Coker, O. O., Nakatsu, G., Wu, W. K. K., Zhao, L., Chen, Z., Chan, F. K. L., Kristiansen, K., Sung, J. J. Y., Wong, S. H., & Yu, J.
Affiliations
  • Chinese University of Hong Kong (HK)
  • BGI Group (China) (CN)
Corresponding author
  • Wong, Sunny Hei
  • Yu, Jun
ORCID
Venue
Microbiome
Publisher
Springer Science and Business Media LLC
Published
2018-04-11
Volume
6
Issue
1
Pages
70-70
ISSN
2049-2618 (ISSN-L)
DOI
10.1186/s40168-018-0451-2
PMC
PMC5896039
OpenAlex
W2796803893
Cited by
545
References
54
Citations by year
2026: 43 · 2025: 70 · 2024: 99 · 2023: 97 · 2022: 67 · 2021: 78 · 2020: 53 · 2019: 29 · 2018: 8
FWCI
14.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Microbiome (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • RGC-GRF (766613, 14106145, 14111216)
  • 135 Program Project China (2016YFC1303200)
  • 973 Program China (2013CB531401)
  • National Key Technology R&D Program (2014BAI09B05)
  • Shenzhen Virtual University Park Support Scheme to CUHK Shenzhen Research Institute
  • Croucher Foundation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Health Sciences › Medicine › Infectious Diseases › Clostridium difficile and Clostridium perfringens research
Keywords
colorectal cancer, gut microbiota, metagenomics, bacterial markers, Bacteroides fragilis, Fusobacterium nucleatum, Porphyromonas asaccharolytica, Parvimonas micra, Prevotella intermedia, Alistipes finegoldii, dysbiosis, microbial co-occurrence networks, non-invasive cancer diagnosis
MeSH terms
Gastrointestinal Microbiome, Aged, Bacteria, Cell Transformation, Neoplastic, Female, Humans, Male, Middle Aged, Colorectal Neoplasms, Computational Biology, Metagenome, Metagenomics, Gene Ontology

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