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

Boštjan Murovec (Author), Leon Deutsch (Author), Blaž Stres (Author)

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

črevesni mikrobi;strojno učenje;rak debelega črevesja;črevesni adenom;metagenomika;funkcionalni mikrobiom;gut microbiome;machine learning;colorectal cancer;colorectal adenoma;metagenomics;functional microbiome;

Data

Language:	English
Year of publishing:	2024
Typology:	1.01 - Original Scientific Article
Organization:	UL BF - Biotechnical Faculty
Publisher:	Frontiers Research Foundation
UDC:	579:004.85
COBISS:	207308547
ISSN:	1664-302X
Views:	548
Downloads:	78
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	črevesni mikrobi;strojno učenje;rak debelega črevesja;črevesni adenom;metagenomika;funkcionalni mikrobiom;
Type (COBISS):	Article
Pages:	10 str.
Issue:	ǂVol. ǂ15
Chronology:	2024
DOI:	10.3389/fmicb.2024.1426407
ID:	25021647