Prognostic value of long non-coding RNA SNHG20 in cancer
Bibliographic record
Abstract
BACKGROUND: Small nucleolar RNA host gene 20 (SNHG20) is a newly identified long non-coding RNA (lncRNA). Accumulative evidence suggest that SNHG20 is highly related to tumorigenesis. However, whether the levels of SNHG20 can be used for prognosis of patients with different cancer types was unclear. The present study aims to explore the role of SNHG20 in tumor prognosis and its clinical significance. METHODS: Related articles published before March 14, 2019 were searched in PubMed, Excerpta Medica Database (EMBASE), ISI Web of Science, and China National Knowledge Infrastructure (CNKI). Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were obtained using Stata 11.0 software and used to for determination of the link between the levels of SNHG20 and overall survival (OS). Fixed or random model was chosen depending on the heterogeneity of the studies. A quality assessment of the included studies was performed according to the Newcastle-Ottawa scale. This study was approved by the Medical Ethics Committee of Xiangya Hospital of Central South University. RESULTS: After a strict filtering process, a total of 1149 patients from 15 studies were enrolled in this study. Pooled data showed that elevated level of SNHG20 was correlated not only with poor overall survival (HR = 2.49, 95% confidence interval (CI): 2.05-2.98), but also with tumor-node-metastasis stage (TNM) (odds ratio (OR) = 3.32, 95% CI: 2.27-4.86), high histological grade (OR = 2.11, 95% CI: 1.55-2.87), tumor size (OR = 2.92, 95% CI: 2.17-3.91), and lymph node metastasis (OR = 4.48, 95% CI: 2.90-6.92). Of note, there is no significant heterogeneity difference among the studies. CONCLUSION: Up-regulated SNHG20 predicts unfavorable prognosis for multiple kinds of cancers although further studies are in need to verify its clinical applications.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".