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Yu L, Zhao G, Wang L, Zhou X, Sun J, Li X, Zhu Y, He Y, Kofonikolas K, Bogaert D, Dunlop M, Zhu Y, Theodoratou E, Li X. A systematic review of microbial markers for risk prediction of colorectal neoplasia. Br J Cancer 2022; 126:1318-1328. [PMID: 35292756 PMCID: PMC9042911 DOI: 10.1038/s41416-022-01740-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/23/2021] [Accepted: 02/03/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Substantial evidence indicates that dysbiosis of the gut microbial community is associated with colorectal neoplasia. This review aims to systematically summarise the microbial markers associated with colorectal neoplasia and to assess their predictive performance. METHODS A comprehensive literature search of MEDLINE and EMBASE databases was performed to identify eligible studies. Observational studies exploring the associations between microbial biomarkers and colorectal neoplasia were included. We also included prediction studies that constructed models using microbial markers to predict CRC and adenomas. Risk of bias for included observational and prediction studies was assessed. RESULTS Forty-five studies were included to assess the associations between microbial markers and colorectal neoplasia. Nine faecal microbiotas (i.e., Fusobacterium, Enterococcus, Porphyromonas, Salmonella, Pseudomonas, Peptostreptococcus, Actinomyces, Bifidobacterium and Roseburia), two oral pathogens (i.e., Treponema denticola and Prevotella intermedia) and serum antibody levels response to Streptococcus gallolyticus subspecies gallolyticus were found to be consistently associated with colorectal neoplasia. Thirty studies reported prediction models using microbial markers, and 83.3% of these models had acceptable-to-good discrimination (AUROC > 0.75). The results of predictive performance were promising, but most of the studies were limited to small number of cases (range: 9-485 cases) and lack of independent external validation (76.7%). CONCLUSIONS This review provides insight into the evidence supporting the association between different types of microbial species and their predictive value for colorectal neoplasia. Prediction models developed from case-control studies require further external validation in high-quality prospective studies. Further studies should assess the feasibility and impact of incorporating microbial biomarkers in CRC screening programme.
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Affiliation(s)
- Lili Yu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Gang Zhao
- Center for Disease Control and Prevention of Hangzhou, Hangzhou, China
| | - Lijuan Wang
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xuan Zhou
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Sun
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xinxuan Li
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingshuang Zhu
- Department of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yazhou He
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Sichuan, China
| | | | - Debby Bogaert
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | - Malcolm Dunlop
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Yimin Zhu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Xue Li
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Saa P, Urrutia A, Silva-Andrade C, Martín AJ, Garrido D. Modeling approaches for probing cross-feeding interactions in the human gut microbiome. Comput Struct Biotechnol J 2021; 20:79-89. [PMID: 34976313 PMCID: PMC8685919 DOI: 10.1016/j.csbj.2021.12.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/03/2021] [Accepted: 12/04/2021] [Indexed: 12/16/2022] Open
Abstract
Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.
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Affiliation(s)
- Pedro Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, 4860 Santiago, Chile
| | - Arles Urrutia
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Silva-Andrade
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Alberto J. Martín
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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Zhou Z, Ge S, Li Y, Ma W, Liu Y, Hu S, Zhang R, Ma Y, Du K, Syed A, Chen P. Human Gut Microbiome-Based Knowledgebase as a Biomarker Screening Tool to Improve the Predicted Probability for Colorectal Cancer. Front Microbiol 2020; 11:596027. [PMID: 33329482 PMCID: PMC7717945 DOI: 10.3389/fmicb.2020.596027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/29/2020] [Indexed: 12/19/2022] Open
Abstract
Colorectal cancer (CRC) is a common clinical malignancy globally ranked as the fourth leading cause of cancer mortality. Some microbes are known to contribute to adenoma-carcinoma transition and possess diagnostic potential. Advances in high-throughput sequencing technology and functional studies have provided significant insights into the landscape of the gut microbiome and the fundamental roles of its components in carcinogenesis. Integration of scattered knowledge is highly beneficial for future progress. In this study, literature review and information extraction were performed, with the aim of integrating the available data resources and facilitating comparative research. A knowledgebase of the human CRC microbiome was compiled to facilitate understanding of diagnosis, and the global signatures of CRC microbes, sample types, algorithms, differential microorganisms and various panels of markers plus their diagnostic performance were evaluated based on statistical and phylogenetic analyses. Additionally, prospects about current changelings and solution strategies were outlined for identifying future research directions. This type of data integration strategy presents an effective platform for inquiry and comparison of relevant information, providing a tool for further study about CRC-related microbes and exploration of factors promoting clinical transformation (available at: http://gsbios.com/index/experimental/dts_ mben?id=1).
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Affiliation(s)
- Zhongkun Zhou
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Shiqiang Ge
- Department of Electronic Information Engineering, Lanzhou Vocational Technical College, Lanzhou, China
| | - Yang Li
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Wantong Ma
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Yuheng Liu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Shujian Hu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Rentao Zhang
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Yunhao Ma
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Kangjia Du
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | | | - Peng Chen
- School of Pharmacy, Lanzhou University, Lanzhou, China
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Iezzi G, Cremonesi E, Majno PE. Pro-tumoral role of gut bacteria: sabotaging immune cell recruitment. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:59. [PMID: 30906763 DOI: 10.21037/atm.2018.12.56] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Giandomenica Iezzi
- Department of Biomedicine, University of Basel, Basel, Switzerland.,Department of Surgery, Ente Ospedaliero Cantonale and Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | | | - Pietro E Majno
- Department of Surgery, Ente Ospedaliero Cantonale and Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
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