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

被引：111

作者：

Kong, Cheng ^{[1
,2
]}

Liang, Lei ^{[1
,2
]}

Liu, Guang ^{[3
]}

Du, Lutao ^{[4
]}

Yang, Yongzhi ^{[1
,2
]}

Liu, Jianqiang ^{[5
]}

Shi, Debing ^{[1
,2
]}

Li, Xinxiang ^{[1
,2
]}

Ma, Yanlei ^{[1
,2
]}

机构：

[1] Fudan Univ, Shanghai Canc Ctr, Dept Colorectal Surg, Shanghai 200032, Peoples R China

[2] Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China

[3] Guangdong Hongyuan Pukang Med Technol Co Ltd, Shenzhen, Guangdong, Peoples R China

[4] Shandong Univ, Hosp 2, Dept Clin Lab, Jinan, Shandong, Peoples R China

[5] Fudan Univ, Shanghai Canc Ctr, Dept Endoscopy, Shanghai, Peoples R China

来源：

GUT | 2023年 / 72卷 / 06期

基金：

中国国家自然科学基金;

关键词：

COLORECTAL CANCER; METABOLITES; MALIGNANCY; DIET;

D O I：

10.1136/gutjnl-2022-327156

中图分类号：

R57 [消化系及腹部疾病];

学科分类号：

摘要：

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.

引用

页码：1129 / 1142

页数：14