Evidence map›Paper›PMID 29871916›Full record

SynthesismBio2018

Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors.

Marc A Sze, Patrick D Schloss

Erratum issuedOpen access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in mBio, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 3 pooled it
3.7field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 56 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Association ofGut microbiome (Cambridge, England) · 2025
    Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Microbiome Analysis of More Than 2,000 NHS Bowel Cancer Screening Programme Samples Shows the Potential to Improve Screening Accuracy.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021
    Article
  15. Article
  16. Article
  17. Article
  18. Alteration of the abundance ofOncology letters · 2020
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Marc A SzeDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-3532-9653
Patrick D SchlossDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA pschloss@umich.edu.ORCID 0000-0002-6935-4275
University of Michigan · US

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
University of Michigan Center for Gastrointestinal ResearchP30DK034933 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI OMARY, BISHR · 1986 to 2021
$22.5M
Identification of Microbiome Based Markers to Improve Colorectal Cancer DetectionR01CA215574 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI RUFFIN, MACK T., SCHLOSS, PATRICK DAVID · 2018 to 2021
$1.4M
CIHRNCATS NIH HHS UL1 TR002240NCI NIH HHS R01 CA215574NIDDK NIH HHS P30 DK034933
6 · The paper itself

Abstract

An increasing body of literature suggests that both individual and collections of bacteria are associated with the progression of colorectal cancer. As the number of studies investigating these associations increases and the number of subjects in each study increases, a meta-analysis to identify the associations that are the most predictive of disease progression is warranted. We analyzed previously published 16S rRNA gene sequencing data collected from feces and colon tissue. We quantified the odds ratios (ORs) for individual bacterial taxa that were associated with an individual having tumors relative to a normal colon. Among the fecal samples, there were no taxa that had significant ORs associated with adenoma and there were 8 taxa with significant ORs associated with carcinoma. Similarly, among the tissue samples, there were no taxa that had a significant OR associated with adenoma and there were 3 taxa with significant ORs associated with carcinoma. Among the significant ORs, the association between individual taxa and tumor diagnosis was equal to or below 7.11. Because individual taxa had limited association with tumor diagnosis, we trained Random Forest classification models using only the taxa that had significant ORs, using the entire collection of taxa found in each study, and using operational taxonomic units defined based on a 97% similarity threshold. All training approaches yielded similar classification success as measured using the area under the curve. The ability to correctly classify individuals with adenomas was poor, and the ability to classify individuals with carcinomas was considerably better using sequences from feces or tissue.

Indexed as

Gastrointestinal MicrobiomeBacteriaBiomarkersColonColorectal NeoplasmsDNA, BacterialFecesHumansPhylogenyRNA, Ribosomal, 16SBiomarkersDNA, BacterialRNA, Ribosomal, 16S16S rRNAadenomabiomarkerscarcinomacolorectal cancerdiagnosticfecesmicrobiome

Identifiers

PMID29871916
PMCPMC5989068
OpenAlexW2791942296

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.