The relationship between Long Noncoding RNA (lncRNA) Small Nucleolar RNA Host Gene 12 (SNHG12) expression in solid malignant tumors and prognosis of tumor patients
Bibliographic record
Abstract
BACKGROUND: Small nucleolar RNA host gene 12 (SNHG12) has been demonstrated to be a long noncoding RNA (lncRNA) that facilitates the progression of several solid malignant tumors. However, whether the expression level of SNHG12 in solid malignant tumors is associated with patients prognosis have not been investigated. METHODS: We systematically searched PubMed, EMBASE and Cochrane Library from Jan 1, 1950 to Mar 24, 2020 for randomized controlled trials published in English on SNHG12 expression in solid malignant tumors. We used the Newcastle-Ottawa Scale to assess the quality of articles. The HRs and 95%CI that extracted from Kaplan-Meier curves were used to perform the forest plot using a fixed-effects model. The meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Thirteen articles containing 821 patients were included in this systematic review and meta-analysis. The result showed that high lncRNA SNHG12 expression is significantly associated with poor overall survival (OS) (HR = 1.94, 95% CI: 1.56-2.41, P < .001) and the studies are lack of statistically significant heterogeneity (P= .878, I = 0.0%). Beggs plot and Eggers test were applied to testify no publication bias existence in these studies. Subgroup analyses were performed and the result showed that TNM stage, lymph node metastasis and tumor type can influence the patients outcome, while there was no significantly correlation between SNHG12 expression and gender. CONCLUSIONS: The systematical review and meta-analysis synthetically analyzed 13 articles including 821 patients with ten types of solid malignant tumors, concluding that higher lncRNA SNHG12 expression is significantly associated with worse clinical prognosis.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".