Integrated metagenomic and metabolomic analysis reveals distinct gut-microbiome-derived phenotypes in early-onset colorectal cancer

• Published in: Gut Vol. 72; no. 6; pp. 1129 - 1142
• Main Authors: Kong, Cheng, Liang, Lei, Liu, Guang, Du, Lutao, Yang, Yongzhi, Liu, Jianqiang, Shi, Debing, Li, Xinxiang, Ma, Yanlei
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd and British Society of Gastroenterology 01.06.2023; BMJ Publishing Group LTD
• Subjects: Acetaldehyde; Acetic acid; Age; Biological analysis; Biomarkers; Choline; Chromatography; COLORECTAL CANCER; Colorectal carcinoma; Colorectal Neoplasms - diagnosis; Digestive system; Discriminant analysis; Gastrointestinal Microbiome - genetics; Genes; Gut microbiota; Humans; Intestinal microflora; Invasiveness; Mann-Whitney U test; Metabolism; Metabolites; Metabolomics; Metagenomics; Microbiomes; Microbiota; Orthology; Pathogenesis; Phenotype; Phenotypes; Prediction models; Quality control; Software; Tryptophan; Tumors; γ-Aminobutyric acid; United Kingdom > UK; United States > US; China; Germany; COLORECTAL CANCER
• ISSN: 0017-5749; 1468-3288; 1468-3288
• DOI: 10.1136/gutjnl-2022-327156

ObjectiveThe incidence of early-onset colorectal cancer (EO-CRC) is steadily increasing. Here, we aimed to characterise the interactions between gut microbiome, metabolites and microbial enzymes in EO-CRC patients and evaluate their potential as non-invasive biomarkers for EO-CRC.DesignWe performed metagenomic and metabolomic analyses, identified multiomics markers and constructed CRC classifiers for the discovery cohort with 130 late-onset CRC (LO-CRC), 114 EO-CRC subjects and age-matched healthy controls (97 LO-Control and 100 EO-Control). An independent cohort of 38 LO-CRC, 24 EO-CRC, 22 LO-Controls and 24 EO-Controls was analysed to validate the results.ResultsCompared with controls, reduced alpha-diversity was apparent in both, LO-CRC and EO-CRC subjects. Although common variations existed, integrative analyses identified distinct microbiome–metabolome associations in LO-CRC and EO-CRC. Fusobacterium nucleatum enrichment and short-chain fatty acid depletion, including reduced microbial GABA biosynthesis and a shift in acetate/acetaldehyde metabolism towards acetyl-CoA production characterises LO-CRC. In comparison, multiomics signatures of EO-CRC tended to be associated with enriched Flavonifractor plauti and increased tryptophan, bile acid and choline metabolism. Notably, elevated red meat intake-related species, choline metabolites and KEGG orthology (KO) pldB and cbh gene axis may be potential tumour stimulators in EO-CRC. The predictive model based on metagenomic, metabolomic and KO gene markers achieved a powerful classification performance for distinguishing EO-CRC from controls.ConclusionOur large-sample multiomics data suggest that altered microbiome–metabolome interplay helps explain the pathogenesis of EO-CRC and LO-CRC. The potential of microbiome-derived biomarkers as promising non-invasive tools could be used for the accurate detection and distinction of individuals with EO-CRC.