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ORIGINAL RESEARCH article

Front. Microbiol.
Sec. Systems Microbiology
Volume 15 - 2024 | doi: 10.3389/fmicb.2024.1426407
This article is part of the Research Topic Microbiome and Machine Learning, Volume II View all 15 articles

Predictive modeling of colorectal cancer using exhaustive analysis of microbiome information layers available from public metagenomic data

Provisionally accepted
Boštjan Murovec Boštjan Murovec 1Leon Deutsch Leon Deutsch 2Blaz Stres Blaz Stres 3*
  • 1 Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
  • 2 The NU B.V., Leiden, Netherlands
  • 3 D13 Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Ljubljana, Slovenia

The final, formatted version of the article will be published soon.

    This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n=380) and colorectal adenomas (CRA, n=110) with those of generally healthy participants (n=2461) from diverse studies. The overarching objective was to conduct a real-life experiment and construct a robust machine learning model applicable to the general population. A total of 2951 stool samples underwent a comprehensive analysis utilizing the in-house MetaBakery pipeline, encompassing diverse data matrices, including microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study revealed no statistically significant difference in microbial diversity among individuals, with distinct clusters identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, showcasing the potential of microbial taxa, functional genes, enzymatic reactions, and metabolic pathways as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions and metabolic pathways) associated with CRC, were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies.

    Keywords: gut microbiome, machine learning, colorectal cancer, Colorectal adenoma, Metagenomics, functional microbiome

    Received: 01 May 2024; Accepted: 09 Aug 2024.

    Copyright: © 2024 Murovec, Deutsch and Stres. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Blaz Stres, D13 Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Ljubljana, 1231, Slovenia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.