Evidence map›Paper›PMID 35292756›Full record

SynthesisBritish journal of cancer2022

A systematic review of microbial markers for risk prediction of colorectal neoplasia.

Lili Yu, Gang Zhao, Lijuan Wang, Xuan Zhou, Jing Sun, Xinxuan Li, Yingshuang Zhu, Yazhou He, Kleovoulos Kofonikolas, Debby Bogaert and 4 more

Open access · hybridAbstract readSystematic Review
In one paragraph

Synthesis in British journal of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 3 pooled it
4.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 48 citations in OpenAlex.

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  3. Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 7 institutions in 3 countries.

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 WangDepartment 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 ZhouDepartment 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 SunDepartment 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 LiDepartment 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 ZhuDepartment of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China.
Yazhou HeDepartment of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Sichuan, China.
Kleovoulos KofonikolasFaculty of Medicine, University of Thessaly, Volos, Greece.
Debby BogaertCentre for Inflammation Research, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0003-2651-7000
Malcolm DunlopColon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Yimin ZhuDepartment 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 TheodoratouCentre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0001-5887-9132
Xue LiDepartment 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. xueli157@zju.edu.cn.ORCID http://orcid.org/0000-0001-6880-2577
Second Affiliated Hospital of Zhejiang University · CNCentre for Inflammation Research · GBEdinburgh Cancer Research · GBHangzhou Center for Disease Control and Prevention · CNSichuan University · CNUniversity of Edinburgh · GBUniversity of Thessaly · GR

Funding

Cancer Research UK 12076Cancer Research UK 18927Cancer Research UK 22804Chief Scientist Office SCAF/16/03Medical Research Council MC_PC_U127527198Medical Research Council MC_U127527198Medical Research Council MC_UU_00007/1
6 · The paper itself

Abstract

backgroundSubstantial 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.

methodsA 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.

resultsForty-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%).

conclusionsThis 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.

Indexed as

AdenomaColorectal NeoplasmsBiomarkersDysbiosisHumansProspective StudiesBiomarkers

Identifiers

PMID35292756
PMCPMC9042911
OpenAlexW4220680754

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.