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Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer
Open accessAbstract
Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10−5). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics. Cross-study analysis defines fecal microbial species associated with colorectal cancer.
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“The lack of solid cross-study reproducible microbial biomarkers of ICI response is not attributable to analytical choices, as applying the same meta-analysis methods in the context of colorectal cancer confirmed the strong and consistent biomarkers across cohorts described elsewhere13,14.”
From Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma (2022) · cites this paper as [13]
In the citing paper
“This signature, identified using both supervised machine learning and a meta-analysis, included the bacterial species Solobacterium moorei, Fusobacterium nucleatum, Parvimonas micra, Peptostreptococcus stomatis, Peptostreptococcus anaerobius and Gemella morbillorum”
From Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy (2023) · cites this paper as [49]
In the citing paper
“Several taxonomic features associated with sequence variants of cutC were also found to be enriched in patients with CRC, including Hungatella hathewayi, Clostridium asparagiforme, Klebsiella oxytoca, and E coli.”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [146]
In the citing paper
“A meta-analysis of metagenomes of fecal samples146 found patients with CRC to have higher levels of 2 bacterial genes that regulate TMA synthesis: choline TMA-lyase (cutC) and choline TMA-lyase-activating enzyme (cutD).”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [146]
In the citing paper
“Moreover, the bai operon, encoding 7α-dehydroxylation in Clostridium spp. required for synthesis of secondary bile acids, was found to be enriched in metagenomes of fecal samples from patients with CRC.146”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [146]
In the citing paper
“A recent analysis of 8 geographically and technically diverse fecal shotgun metagenomic studies found evidence for increased production of secondary bile acids in patients with CRC, indicating a metabolic link between cancer-associated microbes and a fat- and meat-rich diet. 146”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [146]
In the citing paper
“This pattern has been found previously both with MetaPhlAn 2 (Thomas et al., 2019) and with mOTUs2 (Wirbel et al., 2019), in large part due to low-level detection of typically oral microbes in addition to the baseline gut microbiome during CRC.”
From Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2021) · cites this paper as Wirbel et al., 2019
In the citing paper
“Nearly all microbial-based cancer diagnostics are sequencing-based and have focused on tumors within the aerodigestive tract (31), such as colorectal (118–121), pancreatic (122, 123), and lung cancer (124–126).”
From The microbiome and human cancer (2021) · cites this paper as [118]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
MUSCLE: multiple sequence alignment with high accuracy and high throughput
Edgar, R. C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh340 · Cited by 44,662
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
Self-citationPeng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,818
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C., Turnbaugh, P. J., Fierer, N., & Knight, R. D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000080107 · Cited by 9,944
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Metadata
- Authors
- Wirbel, J., Pyl, P. T., Kartal, E., Zych, K., Kashani, A., Milanese, A., Fleck, J. S., Voigt, A. Y., Palleja, A., Ponnudurai, R., Sunagawa, S., Coelho, L. P., Schrotz-King, P., Vogtmann, E., Habermann, N., Niméus, E., Thomas, A. M., Manghi, P., Gandini, S., Serrano, D., Mizutani, S., Shiroma, H., Shiba, S., Shibata, T., Yachida, S., Yamada, T., Waldron, L., Naccarati, A., Segata, N., Sinha, R., Ulrich, C. M., Brenner, H., Arumugam, M., Bork, P., & Zeller, G.
- Affiliations
- European Molecular Biology Laboratory (DE)
- University of Copenhagen (DK)
- ETH Zurich (CH)
- Shanghai Medical College of Fudan University (CN)
- National Cancer Institute (US)
- Lund University (SE)
- Universidade de São Paulo (BR)
- University of Trento (IT)
- European Institute of Oncology (IT)
- Tokyo Institute of Technology (JP)
- National Cancer Center Research Institute (JP)
- Institute of Medical Science, The University of Tokyo (JP)
- City University of New York (US)
- Italian institute for Genomic Medicine (IT)
- University of Utah (US)
- German Cancer Research Center (DE)
- Corresponding author
- Arumugam, Manimozhiyan
- Zeller, Georg
- ORCID
- Wirbel, Jakob
- Pyl, Paul Theodor
- Kartal, Ece
- Zych, Konrad
- Milanese, Alessio
- Fleck, Jonas S.
- Voigt, Anita Y.
- Palleja, Albert
- Sunagawa, Shinichi
- Coelho, Luis Pedro
- Schrotz-King, Petra
- Vogtmann, Emily
- Niméus, Emma
- Thomas, Andrew M.
- Manghi, Paolo
- Gandini, Sara
- Serrano, Davide
- Mizutani, Sayaka
- Shiroma, Hirotsugu
- Shiba, Satoshi
- Shibata, Tatsuhiro
- Yachida, Shinichi
- Yamada, Takuji
- Waldron, Levi
- Naccarati, Alessio
- Segata, Nicola
- Sinha, Rashmi
- Ulrich, Cornelia M.
- Brenner, Hermann
- Arumugam, Manimozhiyan
- Bork, Peer
- Zeller, Georg
- Venue
- Nature Medicine
- Published
- 2019-04
- Volume
- 25
- Issue
- 4
- Pages
- 679-689
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC7984229
- OpenAlex
- W2927453907
- Semantic Scholar
- 62b9cf8262232a54b36b08e6822fc0e815ce909a
- Cited by
- 1,373
- References
- 85
- Citations by year
- 2026: 214 · 2025: 215 · 2024: 224 · 2023: 189 · 2022: 176 · 2021: 169 · 2020: 143 · 2019: 41
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Iris (University of Trento) (submittedVersion, other-oa)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (submittedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- Funders
- Deutsches Krebsforschungszentrum
- Huntsman Cancer Foundation
- Eidgenössische Technische Hochschule Zürich
- Novo Nordisk
- Helmut Horten Stiftung
- National Institutes of Health
- Japan Society for the Promotion of Science (16H06279, 18K15800)
- National Cancer Institute (R01CA189184, R01CA207371, ZIACP010198, P30CA042014, U01CA206110)
- H2020 European Research Council (669830, 716575)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Infectious Diseases › Clostridium difficile and Clostridium perfringens research
- Health Sciences › Medicine › Oncology › Colorectal Cancer Treatments and Studies
- Keywords
- colorectal cancer, gut microbiota, fecal metagenome, microbial signatures, cross-study comparison, protein degradation, carbohydrate degradation, secondary bile acids, cancer diagnosis
- MeSH terms
- Gastrointestinal Microbiome, Adenoma, Aged, Feces, Female, Humans, Male, Middle Aged, Models, Biological, Species Specificity, Biomarkers, Tumor, Colorectal Neoplasms, Reproducibility of Results, Cohort Studies, Databases, Genetic, Metagenome
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