The prognostic value of miR-126 expression in non-small-cell lung cancer: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related death. Growing evidence from recent studies have shown indicated that microRNA-126 (miR-126) played an important role in the progression of NSCLC. However, the potential value of miR-126 expression in prognosis of NSCLC remains to be fully elucidated. Here, we carried out a meta-analysis to assess the potential prognostic value of miR-126 for NSCLC. METHODS: PubMed, Embase, the Cochrane library, Web of Science, CNKI and WanFang database, as well as the reference of included studies, were searched to recognize pertinent studies until April 30, 2017. New castle-Ottawa scale was used to evaluate the quality of studies. Pooled hazard ratio (HR) with 95% confidence interval (CI) for overall survival (OS) was extracted by using a fixed-effects or a random-effects model on the basis of heterogeneity. Publication bias was evaluated by using Begg's tests. RESULTS: We identified four eligible trials involving 666 non-small-cell lung cancer patients in this meta-analysis. The results indicated that a high level of miR-126 played a favorable role in the overall survival (HR 0.73, 95% CI 0.61-0.86, fixed-effects model). There was no bias existed in this study. CONCLUSIONS: Our study showed that high expression level of miR-126 was a promising positive factor for OS for non-small cell lung cancer patients, and miR-126 might be a potential target for non-small-cell lung cancer therapy in the future.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.067 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".