Microbiota and metabolite-based prediction tool for colonic polyposis with and without a known genetic driver
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View abstract on PubMed
Summary
This summary is machine-generated.Microbiome and metabolome analysis reveals distinct microbial and metabolite profiles in colonic polyposis, differentiating genetic-positive and serrated polyposis syndrome patients from others, aiding diagnostic tools.
Area Of Science
- Gastroenterology and Microbiome Research
- Molecular Diagnostics and Biomarkers
Background
- Colorectal cancer (CRC) and polyp research extensively studies microbiome and metabolome, yet profiles in colonic polyposis, especially with genetic drivers, are underexplored.
- Understanding these profiles is crucial for developing targeted diagnostic and preventative strategies for polyposis patients and families.
Purpose Of The Study
- To investigate and compare the microbiome and metabolome profiles in individuals with colonic polyposis, including gene-positive (Gene-pos) and gene-negative (Gene-neg) adenomatous polyposis, and serrated polyposis syndrome (SPS).
- To identify microbial and metabolic signatures that can differentiate between these polyposis subtypes and potentially serve as diagnostic or risk-stratification tools.
Main Methods
- 16S rRNA sequencing was used on colon biopsies, polyps, and stool samples to analyze mucosa-associated microbiota.
- Linear discriminant analysis (LDA) was employed to differentiate between Gene-neg, Gene-pos, and SPS cohorts based on microbial taxa.
- <sup>1</sup>H NMR and Partial Least Squares Discriminant Analysis (PLS-DA) were used to quantify and analyze stool metabolites, assessing their predictive value for SPS.
Main Results
- The mucosa-associated microbiota in colonic polyposis mirrors that of small polyps.
- Gene-pos and SPS cohorts showed distinct microbiota populations compared to Gene-neg polyposis cohorts, with LDA achieving 89% and 93% accuracy in differentiation, respectively.
- SPS subjects exhibited increased fecal alanine compared to non-polyposis individuals, and the leucine to tyrosine ratio in stool may predict SPS.
Conclusions
- Microbial and metabolomic signatures can effectively differentiate between subtypes of colonic polyposis.
- These findings suggest potential for developing advanced diagnostic and risk-stratification tools for colonic polyposis patients.
- The identified signatures may pave the way for microbiome-targeted interventions aimed at polyp prevention.
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