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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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1 Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Department of Big Data in Health Science School of Public Health, Hangzhou, China (GRID:grid.13402.34) (ISNI:0000 0004 1759 700X)
2 Center for Disease Control and Prevention of Hangzhou, Hangzhou, China (GRID:grid.13402.34)
3 Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University, Department of Colorectal Surgery and Oncology, Hangzhou, China (GRID:grid.13402.34) (ISNI:0000 0004 1759 700X)
4 West China School of Public Health and West China Fourth Hospital, Sichuan University, Department of Oncology, Sichuan, China (GRID:grid.13291.38) (ISNI:0000 0001 0807 1581)
5 Faculty of Medicine, University of Thessaly, Volos, Greece (GRID:grid.410558.d) (ISNI:0000 0001 0035 6670)
6 Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK (GRID:grid.470885.6)
7 Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK (GRID:grid.4305.2) (ISNI:0000 0004 1936 7988)
8 Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK (GRID:grid.4305.2) (ISNI:0000 0004 1936 7988); Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK (GRID:grid.470904.e) (ISNI:0000 0004 0496 2805)