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Record W4396934841 · doi:10.3389/fmicb.2024.1406526

Gut microbiome alterations during gastric cancer: evidence assessment of case–control studies

2024· review· en· W4396934841 on OpenAlexaboutno aff
Ruimin Zhang, Yingxin Wu, Wantao Ju, Senlin Wang, Yanjun Liu, Hongmei Zhu

Bibliographic record

VenueFrontiers in Microbiology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobiomeCancerGut microbiomeBiologyMedicinePhysiologyBioinformaticsComputational biologyGenetics

Abstract

fetched live from OpenAlex

Objectives The study aims to systematically identify the alterations in gut microbiota that observed in gastric cancer through comprehensive assessment of case–control studies. Methods The systematic literature search of PubMed, Embase, Cochrane Library, and Web of Science was conducted to identify case–control studies that compared the microbiomes of individuals with and without gastric cancer. Quality of included studies was evaluated with the Newcastle-Ottawa Quality Assessment Scale (NOS). Meta-analyses utilized a random-effects model, and subgroup and sensitivity analyses were performed to assess study heterogeneity. All data analyses were performed using the “metan” package in Stata 17.0, and the results were described using log odds ratios (log ORs) with 95% confidence intervals (CIs). Results A total of 33 studies involving 4,829 participants were eligible for analysis with 29 studies provided changes in α diversity and 18 studies reported β diversity. Meta-analysis showed that only the Shannon index demonstrated statistical significance for α-diversity [−5.078 (−9.470, −0.686)]. No significant differences were observed at the phylum level, while 11 bacteria at genus-level were identified significant changed, e.g., increasing in Lactobacillus [5.474, (0.949, 9.999)] and Streptococcus [5.095, (0.293, 9.897)] and decreasing in Porphyromonas and Rothia with the same [−8.602, (−11.396, −5.808)]. Sensitivity analysis indicated that the changes of 9 bacterial genus were robust. Subgroup analyses on countries revealed an increasing abundance of Helicobacter and Streptococcus in Koreans with gastric cancer, whereas those with gastric cancer from Portugal had a reduced Neisseria. Regarding the sample sources, the study observed an increase in Lactobacillus and Bacteroides in the gastric mucosa of people with gastric cancer, alongside Helicobacter and Streptococcus. However, the relative abundance of Bacteroides decreased compared to the non-gastric cancer group, which was indicated in fecal samples. Conclusion This study identified robust changes of 9 bacterial genus in people with gastric cancer, which were country-/sample source-specific. Large-scale studies are needed to explore the mechanisms underlying these changes. Systematic Review Unique Identifier: CRD42023437426 https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023437426 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.180
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.180
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.019
Bibliometrics0.0130.009
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.400
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainMethods
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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