Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation
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Study information
-
Quality control
- Retracted paper
- Contamination issues suspected
- Batch effect issues suspected
- Uncontrolled confounding suspected
- Results are suspect (various reasons)
- Tags applied
study design
Citation
PMID PubMed identifier for scientific articles.
DOI Digital object identifier for electronic documents.
URI
Authors
Thomas AM, Manghi P, Asnicar F, Pasolli E, Armanini F, Zolfo M, Beghini F, Manara S, Karcher N, Pozzi C, Gandini S, Serrano D, Tarallo S, Francavilla A, Gallo G, Trompetto M, Ferrero G, Mizutani S, Shiroma H, Shiba S, Shibata T, Yachida S, Yamada T, Wirbel J, Schrotz-King P, Ulrich CM, Brenner H, Arumugam M, Bork P, Zeller G, Cordero F, Dias-Neto E, Setubal JC, Tett A, Pardini B, Rescigno M, Waldron L, Naccarati A, Segata N
Journal
Nature medicine
Year
2019
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.
Experiment 1
Needs review
Subjects
- Host species Species from which microbiome was sampled. Contact us to have more species added.
- Homo sapiens
- Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology
- Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces
- Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology
- Colorectal cancer cancer of colorectum,cancer of large bowel,cancer of large intestine,cancer of the large bowel,colon cancer,colorectal cancer,colorectum cancer,CRC,large intestine cancer,malignant colorectal neoplasm,malignant colorectal tumor,malignant colorectum neoplasm,malignant large bowel neoplasm,malignant large bowel tumor,malignant large intestine neoplasm,malignant large intestine tumor,malignant neoplasm of colorectum,malignant neoplasm of large bowel,malignant neoplasm of large intestine,malignant neoplasm of the large bowel,malignant neoplasm of the large intestine,malignant tumor of large bowel,malignant tumor of large intestine,malignant tumor of the large bowel,malignant tumor of the large intestine,Colorectal cancer
- Group 0 name Corresponds to the control (unexposed) group for case-control studies
- controls
- Group 1 name Corresponds to the case (exposed) group for case-control studies
- colorectal cancer cases
- Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group
- colorectal cancer cases
- Group 0 sample size Number of subjects in the control (unexposed) group
- 308
- Group 1 sample size Number of subjects in the case (exposed) group
- 313
Lab analysis
- Sequencing type
- WMS
- 16S variable region One or more hypervariable region(s) of the bacterial 16S gene
- Not specified
- Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance
- Illumina
Statistical Analysis
- Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).
- raw counts
- Statistical test
- Linear Regression
- Significance threshold p-value or FDR threshold used for differential abundance testing (if any)
- 0.05
- MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?
- Yes
Alpha Diversity
- Richness Number of species
- increased
Signature 1
Reviewed Marked as Reviewed by Lwaldron on 2021/06/26
Source: Extended Data Fig. 4
Description: taxonomic meta-analysis of CRC cases vs controls
Abundance in Group 1: increased abundance in colorectal cancer cases
Signature 2
Reviewed Marked as Reviewed by Lwaldron on 2021/06/26
Source: Extended Data Fig. 4
Description: taxonomic meta-analysis of CRC cases vs controls
Abundance in Group 1: decreased abundance in colorectal cancer cases
Signature 3
Reviewed Marked as Reviewed by Lwaldron on 2021/06/26
Source: Extended Data Fig. 5
Description: putative oral species in CRC cases vs controls
Abundance in Group 1: increased abundance in colorectal cancer cases
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