Cited by 1,375Gut microbiota and healthColorectal Cancer Screening and DetectionGenetic factors in colorectal cancer

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Potential of fecal microbiota for early-stage detection of colorectal cancer

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Zeller, Georg, Tap, Julien, Voigt, Anita Y, Sunagawa, Shinichi, Kultima, Jens Roat, Costea, Paul I, Amiot, Aurélien, Böhm, Jürgen, Brunetti, Francesco, Habermann, Nina, Hercog, Rajna, Koch, Moritz, Luciani, Alain, Mende, Daniel R, Schneider, Martin A, Schrotz‐King, Petra, Tournigand, Christophe, Tran Van Nhieu, Jeanne, Yamada, Takuji, Zimmermann, Jürgen, Benes, Vladimir, Kloor, Matthias, Ulrich, Cornelia M, von Knebel Doeberitz, Magnus, Sobhani, Iradj, Bork, Peer·2014·Molecular Systems Biology

DOI 10.15252/msb.20145645PMC PMC4299606

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Abstract

Several bacterial species have been implicated in the development of colorectal carcinoma (CRC), but CRC-associated changes of fecal microbiota and their potential for cancer screening remain to be explored. Here, we used metagenomic sequencing of fecal samples to identify taxonomic markers that distinguished CRC patients from tumor-free controls in a study population of 156 participants. Accuracy of metagenomic CRC detection was similar to the standard fecal occult blood test (FOBT) and when both approaches were combined, sensitivity improved > 45% relative to the FOBT, while maintaining its specificity. Accuracy of metagenomic CRC detection did not differ significantly between early- and late-stage cancer and could be validated in independent patient and control populations (N = 335) from different countries. CRC-associated changes in the fecal microbiome at least partially reflected microbial community composition at the tumor itself, indicating that observed gene pool differences may reveal tumor-related host-microbe interactions. Indeed, we deduced a metabolic shift from fiber degradation in controls to utilization of host carbohydrates and amino acids in CRC patients, accompanied by an increase of lipopolysaccharide metabolism.

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Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Global cancer statistics
Jemal, A., Bray, F., Ferlay, J., Ward, E., & Forman, D. (2011) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.20107 · Cited by 55,044
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
Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 21,481
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 21,098
Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00062-07 · Cited by 20,145
Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega
Sievers, F., Wilm, A., Dineen, D., Gibson, T., Karplus, K., Li, W.-Z., Lopez, R., McWilliam, H., Remmert, M., Söding, J., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.75 · Cited by 15,576
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
An obesity-associated gut microbiome with increased capacity for energy harvest
Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., & Gordon, J. I. (2006) · Nature · doi:10.1038/nature05414 · Cited by 12,444
Structure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726

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Authors: Zeller, G., Tap, J., Voigt, A. Y., Sunagawa, S., Kultima, J. R., Costea, P. I., Amiot, A., Böhm, J., Brunetti, F., Habermann, N., Hercog, R., Koch, M., Luciani, A., Mende, D. R., Schneider, M. A., Schrotz‐King, P., Tournigand, C., Tran Van Nhieu, J., Yamada, T., Zimmermann, J., Benes, V., Kloor, M., Ulrich, C. M., von Knebel Doeberitz, M., Sobhani, I., & Bork, P.
Affiliations: European Molecular Biology Laboratory (DE)
Université Paris-Est Créteil (FR)
German Cancer Research Center (DE)
University Hospital Heidelberg (DE)
Tokyo Institute of Technology (JP)
Corresponding author: Sobhani, Iradj
ORCID: Zeller, Georg
Tap, Julien
Voigt, Anita Y
Sunagawa, Shinichi
Kultima, Jens Roat
Costea, Paul I
Amiot, Aurélien
Brunetti, Francesco
Hercog, Rajna
Koch, Moritz
Luciani, Alain
Mende, Daniel R
Schneider, Martin A
Schrotz‐King, Petra
Tournigand, Christophe
Yamada, Takuji
Kloor, Matthias
Ulrich, Cornelia M
Sobhani, Iradj
Venue: Molecular Systems Biology
Publisher: Springer Science and Business Media LLC
Published: 2014-11-01
Volume: 10
Issue: 11
Pages: 766-766
ISSN: 1744-4292 (ISSN-L)
DOI: 10.15252/msb.20145645
PMC: PMC4299606
OpenAlex: W2130725058
Cited by: 1,375
References: 98
Citations by year: 2026: 131 · 2025: 168 · 2024: 162 · 2023: 134 · 2022: 160 · 2021: 140 · 2020: 134 · 2019: 97 · 2018: 82 · 2017: 85 · 2016: 59 · 2015: 20 · 2014: 1
FWCI: 22.57
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 27 d)
Locations: Molecular Systems Biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
HAL (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: European Research Council (268985)
METACARDIS (FP7‐HEALTH‐2012‐INNOVATION‐I‐305312)
International Human Microbiome Standards (HEALTH‐F4‐2010‐261376)
French LNCC 2004 grant
ACD grants
PHRC 2009 Vatnimad grant
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
Health Sciences › Medicine › Pathology and Forensic Medicine › Genetic factors in colorectal cancer
Keywords: colorectal cancer, fecal microbiota, early cancer detection, cancer screening, metagenomics, fecal occult blood test, microbial biomarkers, host-microbe interactions, fiber degradation
MeSH terms: Feces, Humans, Occult Blood, Sensitivity and Specificity, Colorectal Neoplasms, Case-Control Studies, Early Detection of Cancer, Metagenomics, Molecular Typing, Microbiota

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Potential of fecal microbiota for early-stage detection of colorectal cancer

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