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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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Subspecies of the human gut microbiota carry implicit information for in-depth microbiome research
Tričković, M., Kieser, S., Zdobnov, E. M., & Trajkovski, M. (2025) · Cell Host & Microbe · doi:10.1016/j.chom.2025.07.015 · Cited by 11
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Kim, M., Gim, D. H., Kim, S., Park, S., Eom, T. P., Seol, J., Yeo, J., Jo, C., Seo, G., Ku, H., et al. (2025) · Gastroenterology Insights · doi:10.3390/gastroent16030034 · Cited by 11
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Bisht, V., Acharjee, A., & Gkoutos, G. V. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104556 · Cited by 9
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Ruaud, A., Pfister, N., Ley, R. E., & Youngblut, N. D. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010714 · Cited by 9
Hu, M. Y., Zhu, J., Peng, G., Lu, W., Wang, H., & Xie, Z. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad394 · Cited by 10
Kato, I., Sun, J., Larson, J. C., Hastert, T. A., & Abrams, J. (2020) · Journal of Women s Health · doi:10.1089/jwh.2019.8162 · Cited by 9
Deutsch, L., Debevec, T., Millet, G. P., Osredkar, D., Opara, S., Šket, R., Murovec, B., Mramor, M., Plavec, J., & Stres, B. (2022) · Metabolites · doi:10.3390/metabo12060536 · Cited by 9
MetaFunc: taxonomic and functional analyses of high throughput sequencing for microbiomes
Sulit, A. K., Kolisnik, T., Frizelle, F., Purcell, R. V., & Schmeier, S. (2023) · Gut Microbiome · doi:10.1017/gmb.2022.12 · Cited by 9
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Chen, Y., Wu, G., & Zhao, Y. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-981-15-2385-4_2 · Cited by 10
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Wu, G. G., Xu, T., Zhao, N., Lam, Y. Y., Ding, X., Wei, D., Fan, J., Shi, Y., Li, X., Li, M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.02.490290 · Cited by 9
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Goc, J., & Sonnenberg, G. F. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-22-0164 · Cited by 8
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Liu, W., Lau, H. C.-H., Ding, X. L., Yin, X., Wu, W. K. K., Wong, S. H., Sung, J. J. �., Zhang, T., & Yu, J. Q. (2025) · iMeta · doi:10.1002/imt2.70008 · Cited by 9
Sammallahti, H., Rezasoltani, S., Pekkala, S., Kokkola, A., Aghdaei, H. A., Looha, M. A., Ghanbari, R., Zamani, F., Sadeghi, A., Sarhadi, V. K., et al. (2025) · Gut Pathogens · doi:10.1186/s13099-025-00698-0 · Cited by 8
Zheng, Q., Zhong, Y., Lian, H., Zhuang, J., Wang, L., Chen, J., Wang, H., Wang, H., Ye, X., Huang, Z., et al. (2025) · United European Gastroenterology Journal · doi:10.1002/ueg2.70064 · Cited by 8
Han, W., Zhou, Y., Wang, Y., Liu, X., Sun, T., & Xu, J. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1591076 · Cited by 9
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Wang, Y., Chen, C., Xiao, X. H., Kang, Y., He, J., Beloqui, A., & Xu, Y. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115741 · Cited by 9
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Nielsen, F. D., Jespersen, M. G., Møller, J. E., Coia, J. E., Dessau, R. B., Sydenham, T. V., Strube, M. L., Møller‐Jensen, J., & Justesen, U. S. (2026) · Communications Medicine · doi:10.1038/s43856-026-01403-1 · Cited by 8
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Self-citationDeciphering associations between gut microbiota and clinical factors using microbial modules
Wang, R., Zheng, X., Song, F., Wong, M. H., Leung, K., & Cheng, L. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad213 · Cited by 9
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Chun, J., Kim, J., Youn, Y. H., & Park, H. (2023) · Journal of Digestive Cancer Research · doi:10.52927/jdcr.2023.11.2.85 · Cited by 8
Gao, Y., & Sun, F. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010608 · Cited by 8
Ma, C., Zhang, Y., Jiang, S., Teng, F., Huang, S. W., & Zhang, J. (2023) · mSystems · doi:10.1128/msystems.00828-23 · Cited by 11
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Hull, R., Lolas, G., Makrogkikas, S., Jensen, L. D., Syrigos, K. N., Evangelou, G., Padayachy, L. C., Egbor, C., Mehrotra, R., Makhafola, T. J., et al. (2021) · Microorganisms · doi:10.3390/microorganisms9102129 · Cited by 8
Mezerová, K., Starý, L., Zbořil, P., Klementa, I., Stašek, M., Špička, P., Skalický, P., & Raclavský, V. (2021) · Life · doi:10.3390/life11111165 · Cited by 9
Dai, Z.-H., Tang, M., Chen, Y., Zhang, T., Li, J., Lv, G., Yan, Y., Ouyang, Z.-H., Huang, W., & Zou, M. (2022) · Cancers · doi:10.3390/cancers14092053 · Cited by 8
Ahmad, S., Ashktorab, H., Brim, H., & Housseau, F. (2022) · World Journal of Gastroenterology · doi:10.3748/wjg.v28.i25.2782 · Cited by 9
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Wang, C., Ma, A., Li, Y., McNutt, M. E., Zhang, S., Zhu, J., Hoyd, R., Wheeler, C. E., Robinson, L. A., Chan, C. H. F., et al. (2024) · Cancer Research Communications · doi:10.1158/2767-9764.crc-23-0213 · Cited by 7
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Wang, X., Zhang, Q., Xu, R., Li, X., & Hong, Z. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1416806 · Cited by 7
Yu, Y., Xia, L., Wang, Z., Zhu, T., Zhao, L., & Fan, S. (2024) · iScience · doi:10.1016/j.isci.2024.111453 · Cited by 7
Taba, N., Fischer, K., Estonian Biobank Research Team, Org, E., & Aasmets, O. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2453616 · Cited by 7
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Navarro-Sánchez, A., Nieto-Vitoria, M. Á., López-López, J. A., Martínez-Crespo, J. J., & Navarro‐Mateu, F. (2025) · BMC Cancer · doi:10.1186/s12885-025-13770-4 · Cited by 8
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Eshkiki, H., Tanhaei, F., Caraffini, F., & Mora, B. (2025) · Applied Sciences · doi:10.3390/app152412934 · Cited by 9
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Yang, Z., Zhu, J., Lu, W., Tian, F., Zhang, H., & Chen, W. (2024) · Food Bioscience · doi:10.1016/j.fbio.2024.104541 · Cited by 8
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Shi, K., Liu, Q., Ji, Q., He, Q., & Zhao, X.-M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae530 · Cited by 7
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Flory, M., Bravo, P., & Alam, A. M. (2024) · Immunometabolism · doi:10.1097/in9.0000000000000050 · Cited by 9
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Wirbel, J., Essex, M., Forslund, S. K., & Zeller, G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.09.491139 · Cited by 7
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Ilozumba, M. N., Lin, T., Hardikar, S. S., Byrd, D. A., Round, J. L., Stephens, W. Z., Holowatyj, A. N., Warby, C. A., Damerell, V., Li, C. I., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.70431 · Cited by 6
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Guan, Z., Qi, H., & Feng, Q. (2025) · Open Research (University of Surrey) · doi:10.1002/viw.20240153 · Cited by 6
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Qian, L.-M., Wang, S., Zhou, W., Qin, Z., Wang, Y., Zhao, Q., & Xu, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf208 · Cited by 6
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Wei, Z., Chen, G., & Tang, Z.-Z. (2025) · Genome biology · doi:10.1186/s13059-025-03721-4 · Cited by 5
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Mansilla, M. J. G., Rodríguez‐Sojo, M. J., Lista, A. R., Mosqueda, C. V. A., García-García, J., Gálvez, J., Rodríguez‐Nogales, A., Ruiz‐Malagón, A. J., & Rodríguez, M. J. (2025) · Nutrients · doi:10.3390/nu17223565 · Cited by 8
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Chen, J., Li, Y., Tang, S., Jin, W., & Yan, R. (2025) · Nature Communications · doi:10.1038/s41467-025-65679-y · Cited by 7
Sakamoto, M., Kuroda, S., Katayama, T., Mikane, Y., Hanzawa, S., Kadowaki, D., Yoshida, Y., Hamada, Y., Sugimoto, R., Yagi, C., et al. (2025) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-025-04252-4 · Cited by 7
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Yu, W., Qu, H., Wang, S., Shī, M., Lu, Y., Xia, S., Stanton, C., Zhang, C., Wang, Y., Zhu, T., et al. (2026) · The Lancet Microbe · doi:10.1016/j.lanmic.2026.101428 · Cited by 6
Dutta, G., Mishra, P., Mishra, S. P., & Badhai, J. (2026) · Onco · doi:10.3390/onco6030031 · Cited by 7
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Wu, M., Yu, H., Gao, Y., Li, H., Wang, C., Li, H., Ma, X., Dong, M., Li, B., Bai, J., et al. (2023) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2023.1024723 · Cited by 6
Liu, H., Song, C., Wang, J., Chen, Z., Zhang, X., Zhou, H., Yao, L., Chen, D., Gu, W., Huang, R.-K., et al. (2023) · The International Journal of Biological Markers · doi:10.1177/03936155231210881 · Cited by 6
gülhan, Ü. G., Nıkerel, E., Çakır, T., Sevilgen, F. E., & Durmuş, S. (2024) · Molecular Omics · doi:10.1039/d4mo00016a · Cited by 6
Ota, G., Inoüe, R., Saito, A., Kono, Y., Kitayama, J., Sata, N., & Horie, H. (2024) · Journal of the Anus Rectum and Colon · doi:10.23922/jarc.2024-014 · Cited by 6
A Systematic Review of the Gastrointestinal Microbiome: A Game Changer in Colorectal Cancer
Eastmond, A. K., Shetty, C., Rizvi, S. M. H. A., Sharaf, J., Williams, K.-A. D., Tariq, M., Acharekar, M. V., Saldivia, S. E. G., Unnikrishnan, S., Chavarria, Y. Y., et al. (2022) · Cureus · doi:10.7759/cureus.28545 · Cited by 6
Vaccination and Microbiota Manipulation Approaches for Colon Cancer Prevention in Rodent Models
Tosti, E., Srivastava, N., & Edelmann, W. (2023) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-23-0015 · Cited by 6
Human gene and microbial analyses in rectal cancer complete responses to radiotherapy
Sulit, A. K., Wilson, K., Pearson, J. F., Silander, O., Sampurno, S., Michael, M., Ramsay, R. G., Heriot, A. G., Frizelle, F., & Purcell, R. V. (2023) · BJS Open · doi:10.1093/bjsopen/zrad035 · Cited by 6
Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer
Cai, Z., Li, P., Zhu, W., Wei, J., Lü, J., Song, X., Li, K., Li, S., & Li, M. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1130446 · Cited by 7
Human gut microbiota and endocrinology: paradigm shift from genome to its regulation
Lamaudière, M. T. F., Turner, M. C., Arasaradnam, R. P., & Morozov, I. Y. (2023) · Journal of Endocrinology · doi:10.1530/joe-23-0348 · Cited by 7
Microbial Biomarkers for Colorectal Cancer Identified with Random Forest Model
Sun, W., Wang, L., Zhang, Q., & Dong, Q. (2020) · Exploratory Research and Hypothesis in Medicine · doi:10.14218/erhm.2019.00026 · Cited by 6
Hajeebu, S., Ngembus, N. J., Bandi, P. S., Panigrahy, P. K., & Heindl, S. E. (2021) · Cureus · doi:10.7759/cureus.17415 · Cited by 6
Development of colorectal cancer detection and prediction based on gut microbiome big-data
Sun, Y., Fan, X., & Zhao, J. (2022) · Medicine in Microecology · doi:10.1016/j.medmic.2022.100053 · Cited by 6
Romanov, V. A., Карасев, И. А., Klimenko, N. S., Игоревич, К. С., Tyakht, A., & Malikhova, O. A. (2022) · Cancers · doi:10.3390/cancers14215207 · Cited by 6
Popa, O., Oldenburg, E., & Ebenhöh, O. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0448 · Cited by 6
HIV infection among women and children and antidiscrimination laws: an overview.
Gittler, J., & Rennert, S. (1992) · PubMed · Cited by 5
Jiang, Y., Wang, Y., Che, L., Zhou, Q., & Li, S. C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.26.509484 · Cited by 6
Wheeler, C. E., Coleman, S. S., Hoyd, R., Denko, L., Chan, C. H. F., Churchman, M. L., Denko, N. C., Dodd, R. D., Eljilany, I., Hardikar, S. S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.24.542123 · Cited by 6
Self-citationQuantifying Metagenomic Strain Associations from Microbiomes with AnpanPreprint
Ghazi, A. R., Thompson, K. N., Bhosle, A., Mei, Z., Yan, Y., Wang, F., Wang, K., Franzosa, E. A., & Huttenhower, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.06.631550 · Cited by 7
Role of Crosstalk Between Gut Microbiota and Immune Cells in Colorectal Cancer: A Narrative Review
Ye, M., Yuan, G., Jin, Y., & Zeng, X. (2025) · Infectious Microbes & Diseases · doi:10.1097/im9.0000000000000172 · Cited by 5
Role of Microbiota‐Derived Metabolites in Prostate Cancer Inflammation and Progression
Rathinavelu, P. K., Kumar, A., & Kumar, V. (2025) · Cell Biochemistry and Function · doi:10.1002/cbf.70050 · Cited by 5
Oral microbiota in colorectal cancer: Unraveling mechanisms and application potential
Zhang, X., Chen, Y., Xia, Y., Lin, S., Zhou, X., Pang, X., Yu, J., & Sun, L. (2025) · Life Sciences · doi:10.1016/j.lfs.2025.123462 · Cited by 5
Metatranscriptomic analysis of the microbiota of tumor tissue in colon cancer
Buetas, E., Conde‐Pérez, K., Concha, Á., Celeiro‐Muñoz, C., Noguera, J. F., Bou, G., López, S. P., Vallejo, J. A., Poza, M., Carda‐Diéguez, M., et al. (2026) · Microbiome · doi:10.1186/s40168-026-02372-2 · Cited by 5
Luévano, J. M., Liu, J. C., & Stappenbeck, T. S. (2025) · Clinical and Translational Gastroenterology · doi:10.14309/ctg.0000000000000864 · Cited by 6
Mubaraki, F. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328194 · Cited by 5
Morsy, Y., Walberg, Ã., Wawrzyniak, P., Hubeli, B., Truscello, L., Mamie, C., Niechcial, A., Gueguen, E., Manzini, R., Gottier, C., et al. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2530157 · Cited by 4
Janak, A., Jain, A. K., García-Milian, R., Aladelokun, O. B., Ma, X., Paty, P. B., Khan, S. A., & Johnson, C. H. (2025) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2025.08.052 · Cited by 5
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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