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. 2024 Aug 26:15:1426407.
doi: 10.3389/fmicb.2024.1426407. eCollection 2024.

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

Boštjan Murovec  1 , Leon Deutsch  2   3 , Blaž Stres  2   4   5   6
Affiliations

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

Boštjan Murovec et al. Front Microbiol. .

Abstract

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n = 380) and colorectal adenomas (CRA, n = 110) against generally healthy participants (n = 2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (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: colorectal adenoma; colorectal cancer; functional microbiome; gut microbiome; machine learning; metagenomics.

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Conflict of interest statement

LD was employed by the NU B.V. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Boxplots representing Shannon diversity metrics for healthy individuals and patients with colorectal cancer or colorectal adenoma.
Figure 2
Figure 2
LDA scores plots of components one and two for healthy (red), patients with CRC (green) and CRA (blue): (A) taxonomy, (B) gene families, (C) enzymatic reactions, and (D) metabolic pathways.
Figure 3
Figure 3
ROC plots for classification between healthy individuals (green), CRC (orange) and CRA (blue) patients based on taxonomy (A), functional genes (B), enzymatic reactions, (C) and metabolic pathways (D).
See this image and copyright information in PMC

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