Evidence map›Paper›PMID 40528214›Full record

SynthesisJournal of translational medicine2025

Cross-cohort analysis identifies shared gut microbial signatures and validates microbial risk scores for colorectal cancer.

Yuhan Zhang, Jiahui Luo, Kuangyu Chen, Na Li, Chenyu Luo, Shuang Di, Junjie Qin, Feng Zhang, Hongda Chen, Min Dai

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yuhan Zhang *Center for Clinical and Epidemiologic Research, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, 100029, China.ORCID 0000-0002-2768-3900
Jiahui Luo *Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Kuangyu ChenKey Laboratory of Data Engineering and Knowledge Engineering (MOE), and School of Information, Renmin University of China, Beijing, 100872, China.
Na LiDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Chenyu LuoDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Shuang DiCentre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, L8P 0A1, Canada.
Junjie QinSchool of Life Science and Technology, School of Food Science and Engineering, Nutrition and Health Research Institute, Wuhan Polytechnic University, Wuhan, 430024, China.
Feng ZhangKey Laboratory of Data Engineering and Knowledge Engineering (MOE), and School of Information, Renmin University of China, Beijing, 100872, China.
Hongda ChenCenter for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. chenhongda@pumch.cn.
Min DaiDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. daimin2002@hotmail.com.

Funding

Beijing Nova Program of Science and Technology 20230484397Institute of Chinese Materia Medica, China Academy of Chinese Medical Science 2022-I2M-1-0031National Natural Science Foundation of China 82173606National Natural Science Foundation of China 82273726
6 · The paper itself

Abstract

backgroundMicrobiome-wide association studies showed links between colorectal cancer (CRC) and gut microbiota. However, the clinical application of gut microbiota in CRC prevention has been hindered by the diversity of study populations and technical variations. We aimed to determine CRC-related gut microbial signatures based on cross-regional, cross-population, and cross-cohort metagenomic datasets, and elucidate its application value in CRC risk assessment.

methodsWe used the MMUPHin tool to perform a meta-analysis of our own cohort and seven publicly available metagenomics datasets to identify gut microbial species associated with CRC across different cohorts, comprising of 570 CRC cases and 557 controls. Based on differential species sets, we constructed the microbial risk score (MRS) using α-diversity of the sub-community (MRS

resultsWe found that MRS

conclusionWe identified six CRC-related species across regions, populations, and cohorts. The constructed MRS

Indexed as

Colorectal NeoplasmsGastrointestinal MicrobiomeCase-Control StudiesCohort StudiesFemaleHumansMaleMetagenomicsMiddle AgedReproducibility of ResultsRisk AssessmentRisk FactorsColorectal cancerCross-cohortDisease predictionGut microbiotaMicrobial risk score

Identifiers

PMID40528214
PMCPMC12175378

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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.