Associations of the gut, cervical, and vaginal microbiota with cervical cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
An increasing number of studies indicate that the gut, cervical, and vaginal microbiota may play crucial roles in the development of cervical cancer (CC). However, the interactions between the microbiota and the host are yet unknown. To address this gap, a systematic review and meta-analysis were conducted to assess the microbiota alterations in a variety of body locations, including the gut and genital tract. Electronic searches of PubMed, Embase, Web of Science, and the Cochrane Library were conducted to retrieve eligible papers published from January 1, 2014, to January 1, 2024 (PROSPERO: CRD42024554433). This study was restricted to English-language studies reporting on alpha diversity, beta diversity, and relative abundance, as well as on patients with CC whose microbiota had been analyzed via next-generation sequencing technologies. To assess the risk of bias (RoB), we utilized the Newcastle‒Ottawa Quality Assessment Scale (NOS) and the ROBINS-I tool. For the meta-analysis, we employed Review Manager 5.4. Thirty-six eligible studies were included in this review. The Chao1 index (SMD = 0.96, [95% CI: 0.71, 1.21], I 2 = 0%) and the Shannon index (SMD = 1.02, [95% CI: 0.53, 1.50], I 2 = 85%) values from vaginal samples were significantly greater in patients than in the controls. In the cervical samples, the Shannon index value (SMD = 1.29, [95% CI: 0.61, 1.97], I 2 = 93%) significantly increased, whereas the Chao1 index value did not significantly differ (SMD = 0.50, [95% CI: -0.46, 1.46], I 2 = 89%). The Shannon index value (SMD = 0.25, [95% CI: -0.22, 0.72], I 2 = 38%) did not significantly differ across the gut samples. The majority of studies (19/25) indicated that the patients and noncancer controls differed significantly in terms of beta diversity. Cancer-associated changes were observed, with a dramatic decrease in the Lactobacillus genus and significant increases in pathogenic bacteria, including the Anaerococcus , Peptostreptococcus , Porphyromonas , Prevotella , and Sneathia genera. Additionally, the impact of antineoplastic therapies on microbial diversity was inconsistently reported across several studies. This systematic review elucidates the microbiota alterations associated with the prevalence of CC and its response to anti-tumor therapies, aiming to provide insights for future research directions and precision medicine strategies to enhance women’s quality of life. CRD42024554433.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".