Evidence map›Paper›PMID 39833681›Full record

ArticleBMC microbiology2025

Fecal occult blood affects intestinal microbial community structure in colorectal cancer.

Wu Guodong, Wu Yinhang, Wu Xinyue, Shen Hong, Chu Jian, Qu Zhanbo, Han Shuwen

Abstract read
In one paragraph

Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Wu GuodongHuzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558 Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province, 313000, People's Republic of China.
Wu YinhangHuzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558 Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province, 313000, People's Republic of China.
Wu XinyueHuzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558 Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province, 313000, People's Republic of China.
Shen HongHuzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558 Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province, 313000, People's Republic of China.
Chu JianHuzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558 Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province, 313000, People's Republic of China.
Qu ZhanboHuzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558 Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province, 313000, People's Republic of China.
Han ShuwenHuzhou Central Hospital, Affiliated Central Hospital Huzhou University, No.1558 Sanhuan North Road, Wuxing District, Huzhou, Zhejiang Province, 313000, People's Republic of China. shuwenhan985@163.com.

Funding

Public Welfare Technology Application Research Program of Huzhou No.2021YZ22the Key research and development project of Science and Technology Department of Zhejiang Province 2022C03026Zhejiang Province Traditional Chinese Medicine Science and Technology Project No.2024ZL1018
6 · The paper itself

Abstract

backgroundGut microbes have been used to predict CRC risk. Fecal occult blood test (FOBT) has been recommended for population screening of CRC.

objectiveTo analyze the effects of fecal occult blood test (FOBT) on gut microbes.

methodsFecal samples from 107 healthy individuals (FOBT-negative) and 111 CRC patients (39 FOBT-negative and 72 FOBT-positive) were included for 16 S ribosomal RNA sequencing. Based on the results of different FOBT, the community structure and diversity of intestinal bacteria in healthy individuals and CRC patients were analyzed. Characteristic gut bacteria were screened, and various machine learning algorithms were applied to construct CRC risk prediction models.

resultsThe gut microbiota of healthy people and CRC patients with different fecal occult blood were mapped. There was no statistical difference in diversity between CRC patients with negative FOBT and positive FOBT. Bacteroides, Blautia and Escherichia-Shigella were more correlated to healthy individuals, while Streptococcus showed higher correlation with CRC patients with negative FOBT. The accuracy of CRC risk prediction model based on the support vector machines (SVM) algorithm was the highest (89.71%). Subsequently, FOBT was included as a characteristic element in the model construction, and the prediction accuracy of the model was all increased. Similarly, the CRC risk prediction model based on SVM algorithm had the highest accuracy (92%).

conclusionFOB affects the community composition of gut microbes. When predicting CRC risk based on gut microbiome, considering the influence of FOBT is expected to improve the accuracy of CRC risk prediction.

Indexed as

BacteriaColorectal NeoplasmsGastrointestinal MicrobiomeOccult BloodAdultAgedFecesFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16SSupport Vector MachineRNA, Ribosomal, 16S16S ribosomal RNA sequencingArtificial intelligenceColorectal cancerFecal occult blood testGut microbesRisk prediction

Identifiers

PMID39833681
PMCPMC11745023

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.