Multi-kingdom microbiota analyses identifybacterial–fungal interactions and biomarkers of colorectal cancer acrosscohorts

Ning‐Ning Liu(Shanghai Jiao Tong University), Na Jiao(Sun Yat-sen University), Jing-Cong Tan(Shanghai Jiao Tong University), Ziliang Wang(Shanghai University of Traditional Chinese Medicine), Dingfeng Wu(Tongji University), An-Jun Wang(Shanghai Jiao Tong University), Jie Chen(Shanghai Jiao Tong University), Liwen Tao(Tongji University), Chenfen Zhou(Chinese Academy of Sciences), Wenjie Fang(Second Military Medical University), Io Hong Cheong(Shanghai Jiao Tong University), Weihua Pan(Second Military Medical University), Wanqing Liao(Second Military Medical University), Zisis Kozlakidis(Centre international de recherche sur le cancer), Christopher Heeschen(Shanghai Jiao Tong University), Geromy G. Moore(Agricultural Research Service), Lixin Zhu(Sun Yat-sen University), Xingdong Chen(Fudan University), Guoqing Zhang(Chinese Academy of Sciences), Ruixin Zhu(Tongji University), Hui Wang(Shanghai Jiao Tong University)
Nature Microbiology
January 27, 2022
Cited by 303Open Access
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Abstract

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.


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