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

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

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.

• Jezik: Angleški jezik
• Ključne besede: gut microbiome, machine learning, colorectal cancer, colorectal adenoma, metagenomics, functional microbiome
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko; BF - Biotehniška fakulteta ; FGG - Fakulteta za gradbeništvo in geodezijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Založnik: Frontiers Research Foundation
• Leto izida: 2024
• Št. strani: 10 str.
• Številčenje: Vol. 15
• PID: 20.500.12556/RUL-161601
• UDK: 579:004.85
• ISSN pri članku: 1664-302X
• DOI: 10.3389/fmicb.2024.1426407
• COBISS.SI-ID: 207308547
• Datum objave v RUL: 12.09.2024

Gradivo je del revije

• Naslov: Frontiers in microbiology
• Skrajšan naslov: Front. microbiol.
• Založnik: Frontiers Research Foundation
• ISSN: 1664-302X
• COBISS.SI-ID: 4146296

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: črevesni mikrobi, strojno učenje, rak debelega črevesja, črevesni adenom, metagenomika, funkcionalni mikrobiom