PT  - JOURNAL ARTICLE
AU  - Kong, Cheng
AU  - Liang, Lei
AU  - Liu, Guang
AU  - Du, Lutao
AU  - Yang, Yongzhi
AU  - Liu, Jianqiang
AU  - Shi, Debing
AU  - Li, Xinxiang
AU  - Ma, Yanlei
TI  - Integrated metagenomic and metabolomic analysis reveals distinct gut-microbiome-derived phenotypes in early-onset colorectal cancer
AID  - 10.1136/gutjnl-2022-327156
DP  - 2023 Jun 01
TA  - Gut
PG  - 1129--1142
VI  - 72
IP  - 6
4099  - http://gut.bmj.com/content/72/6/1129.short
4100  - http://gut.bmj.com/content/72/6/1129.full
SO  - Gut2023 Jun 01; 72
AB  - Objective The 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.Design We 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.Results Compared 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.Conclusion Our 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.All data relevant to the study are included in the article or uploaded as online supplemental information.