Genital mycoplasma infections: a hidden factor in cervical cancer progression? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Exploring the role of the microbiome, specifically genital mycoplasmas, in cervical cancer (CC) offers insights into tumorigenesis, potential therapeutic targets and personalized treatment strategies. Although mycoplasmas are generally identified as commensals, their contributions to gynecological cancers, mainly CC, is increasingly recognized. This study investigates the association between CC and genital mycoplasma infections, highlighting the interactions with human papillomavirus (HPV) and their impact on cellular and immune mechanisms. METHODS: We conducted a systematic review and meta-analysis of databases through June 2024. Association strength was determined using pooled odds ratios (ORs) with 95% confidence intervals (CIs). Six case-control studies involving 319 cervical cancer patients and 447 controls were included. RESULTS: Pooled results showed that genital mycoplasmas were associated with a significantly increased risk of CC (OR = 1.64; 95% CI 1.25-2.14). The species-specific analysis demonstrated that Ureaplasma urealyticum was linked with a high risk of CC (OR = 1.81, 95% CI 1.31-2.51), while no significant association was seen for Ureaplasma parvum. HPV-positive subjects co-infected with genital mycoplasmas had a markedly increased risk of CC (OR = 3.13, 95% CI 2.04-4.79), highlighting potential synergistic effects in tumor progression. CONCLUSION: Mycoplasmas, particularly U. urealyticum, constitute co-factors in the development of CC, likely by influencing HPV persistence and immune evasion. Systemic screening coupled with targeted treatment of genital mycoplasmas in high-risk populations is thus warranted for CC prevention.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".