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| 005 | 20240229112552.0 | ||
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| 037 | _ | _ | |a DKFZ-2019-01015 |
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| 100 | 1 | _ | |a Thomas, Andrew Maltez |0 0000-0001-5789-3354 |b 0 |
| 245 | _ | _ | |a Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. |
| 260 | _ | _ | |a New York, NY |c 2019 |b Nature America Inc. |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1562136138_21534 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies. |
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| 700 | 1 | _ | |a Manghi, Paolo |b 1 |
| 700 | 1 | _ | |a Asnicar, Francesco |0 0000-0003-3732-1468 |b 2 |
| 700 | 1 | _ | |a Pasolli, Edoardo |b 3 |
| 700 | 1 | _ | |a Armanini, Federica |b 4 |
| 700 | 1 | _ | |a Zolfo, Moreno |0 0000-0001-6661-4046 |b 5 |
| 700 | 1 | _ | |a Beghini, Francesco |0 0000-0002-8105-9607 |b 6 |
| 700 | 1 | _ | |a Manara, Serena |b 7 |
| 700 | 1 | _ | |a Karcher, Nicolai |b 8 |
| 700 | 1 | _ | |a Pozzi, Chiara |b 9 |
| 700 | 1 | _ | |a Gandini, Sara |0 0000-0002-1348-4548 |b 10 |
| 700 | 1 | _ | |a Serrano, Davide |b 11 |
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| 700 | 1 | _ | |a Gallo, Gaetano |0 0000-0003-1066-4671 |b 14 |
| 700 | 1 | _ | |a Trompetto, Mario |b 15 |
| 700 | 1 | _ | |a Ferrero, Giulio |0 0000-0002-4580-0680 |b 16 |
| 700 | 1 | _ | |a Mizutani, Sayaka |b 17 |
| 700 | 1 | _ | |a Shiroma, Hirotsugu |b 18 |
| 700 | 1 | _ | |a Shiba, Satoshi |b 19 |
| 700 | 1 | _ | |a Shibata, Tatsuhiro |0 0000-0002-0477-210X |b 20 |
| 700 | 1 | _ | |a Yachida, Shinichi |b 21 |
| 700 | 1 | _ | |a Yamada, Takuji |b 22 |
| 700 | 1 | _ | |a Wirbel, Jakob |0 0000-0002-4073-3562 |b 23 |
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| 700 | 1 | _ | |a Zeller, Georg |0 0000-0003-1429-7485 |b 29 |
| 700 | 1 | _ | |a Cordero, Francesca |b 30 |
| 700 | 1 | _ | |a Dias-Neto, Emmanuel |0 0000-0001-5670-8559 |b 31 |
| 700 | 1 | _ | |a Setubal, João Carlos |b 32 |
| 700 | 1 | _ | |a Tett, Adrian |b 33 |
| 700 | 1 | _ | |a Pardini, Barbara |0 0000-0001-9571-4257 |b 34 |
| 700 | 1 | _ | |a Rescigno, Maria |b 35 |
| 700 | 1 | _ | |a Waldron, Levi |0 0000-0003-2725-0694 |b 36 |
| 700 | 1 | _ | |a Naccarati, Alessio |0 0000-0001-5774-0905 |b 37 |
| 700 | 1 | _ | |a Segata, Nicola |0 0000-0002-1583-5794 |b 38 |
| 773 | _ | _ | |a 10.1038/s41591-019-0405-7 |g Vol. 25, no. 4, p. 667 - 678 |0 PERI:(DE-600)1484517-9 |n 4 |p 667 - 678 |t Nature medicine |v 25 |y 2019 |x 1546-170X |
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