High SNHG expression may contribute to poor cervical cancer prognosis, based on systematic reviews and meta-analyses
Bibliographic record
Abstract
BACKGROUND: More and more long non-coding RNA small nucleotide host RNA (SNHG) gene family has been confirmed to be unregulated in cervical cancer (CC) tissues, and it is significantly related to the prognosis of CC. The purpose of this study was to conduct a meta-analysis to explore the correlation between the expression level of SNHGs and the prognosis of CC. METHODS: Six relevant electronic databases were searched, relevant original documents were screened, and the research quality of each document was assessed based on the Newcastle–Ottawa Scale (NOS) scale. Relevant data were extracted including SNHG expression levels, survival outcomes and follow-up time. Hazard ratio (HR) and Odds ratio (OR) with 95% confidence interval (CI) were combined to assess the association between SNHG expression and overall survival (OS) TNM stage, tumor size, depth of invasion. The sensitivity analyzes and Begg’s test was conducted to explore potential publication bias. RESULTS: The results of pooling HR with 95%CI indicating the marked positive association between increasing SNHG expression and poor OS (HR: 2.046, 95%CI: 1.402–2.691). In addition, high SNHG expressions contribute to advanced TNM stage (OR: 1.476, 95%CI: 1.178–1.849), easier to lymph node metastasis (OR: 1.614, 95%CI: 1.021–2.553), bigger tumor size (OR: 1.299, 95%CI: 1.031–1.638). Meanwhile, an insignificant relationship was also found between high SNHGs expression and histological grade (OR: 1.053, 95%CI: 0.814–1.361), DM (OR: 1.659, 95%CI: 0.969–2.838), depth of invasion (OR: 1.126, 95%CI: 0.466–2.726) and age (OR: 1.115, 95%CI: 0.899–1.382). Sensitivity analysis suggests the reliability and robustness of OS, the results of Begg’s test indicated that there is no significant publication bias in the original literature. CONCLUSION: Most SNHGs are highly expressed in CC tissues, elevated SNHG expression predicts poor prognosis of CC, SNHG may serve as a potential target for tumor therapy and a promising prognostic marker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".