Evidence map›Paper›PMID 36510191›Full record

ArticleBMC gastroenterology2022

Evaluating the predictive performance of gut microbiota for the early-stage colorectal cancer.

Maedeh Amini, Sama Rezasoltani, Mohamad Amin Pourhoseingholi, Hamid Asadzadeh Aghdaei, Mohammad Reza Zali

Abstract read
In one paragraph

Article in BMC gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Cancers · 2023
    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

5 authors.

Maedeh AminiBasic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran. maedeh.amini92@yahoo.com.
Sama RezasoltaniSection Mass Spectrometry and Proteomics, Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
Mohamad Amin PourhoseingholiGastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran. amin_phg@yahoo.com.
Hamid Asadzadeh AghdaeiBasic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad Reza ZaliGastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) has been regarded as one of the most frequently diagnosed malignancies among the leading causes of cancer-related morbidity and mortality globally. Diagnosis of CRC at the early-stages of tumour might improve the survival rate of patients. The current study sought to determine the performance of fecal Fusobacterium nucleatum (F. nucleatum) and Streptococcus bovis (S. bovis) for timely predicting CRC.

methodsThrough a case-control study, the fecal sample information of 83 individuals (38 females, 45 males) referring to a hospital in Tehran, Iran was used. All patients underwent a complete colonoscopy, regarded as a gold standard test. Bacterial species including S. bovis and F. nucleatum were measured by absolute quantitative real-time PCR. The Bayesian univariate and bivariate latent class models (LCMs) were applied to estimate the ability of the candidate bacterial markers in order to early detection of patients with CRC.

resultsBayesian univariate LCMs demonstrated that the sensitivities of S. bovis and F. nucleatum were estimated to be 86% [95% credible interval (CrI) 0.82-0.91] and 82% (95% CrI 0.75-0.88); while specificities were 84% (95% CrI 0.78-0.89) and 80% (95% CrI 0.73-0.87), respectively. Moreover, the area under the receiver operating characteristic curves (AUCs) were 0.88 (95% CrI 0.83-0.94) and 0.80 (95% CrI 0.73-0.85) respectively for S. bovis and F. nucleatum. Based on the Bayesian bivariate LCMs, the sensitivities of S. bovis and F. nucleatum were calculated as 93% (95% CrI 0.84-0.98) and 90% (95% CrI 0.85-0.97), the specificities were 88% (95% CrI 0.78-0.93) and 87% (95% CrI 0.79-0.94); and the AUCs were 0.91 (95% CrI 0.83-0.99) and 0.88(95% CrI 0.81-0.96), respectively.

conclusionsOur data has identified that according to the Bayesian bivariate LCM, S. bovis and F. nucleatum had a more significant predictive accuracy compared with the univariate model. In summary, these intestinal bacteria have been highlighted as novel tools for early-stage CRC diagnosis.

Indexed as

Colorectal NeoplasmsBacteriaBayes TheoremCase-Control StudiesFemaleFusobacterium nucleatumHumansIranMaleBayesian approachColorectal cancerF. nucleatumIntestinal microbiotaLatent class analysisReceiver operator characteristic curveS. bovis

Identifiers

PMID36510191
PMCPMC9743636

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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.