Prognostic significance of microRNA-100 in solid tumors: an updated meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify prognostic significance of microRNA-100 (miR-100) in solid tumor. METHODS: Literature search was conducted in databases such as PubMed, Embase, and Web of Science, using the following words "(microRNA-100 OR miR-100 OR mir100) AND (tumor OR neoplasm OR cancer OR carcinoma OR malignancy)." The search was updated up until July 10, 2016. Newcastle-Ottawa scale was used to evaluate the quality of studies. Pooled hazard ratio (HR) with 95% confidence interval (CI) for patients' survival was calculated by using a fixed-effects or a random-effects model on the basis of heterogeneity. Subgroup analysis, sensitive analysis, and meta-regression were used to investigate the sources of heterogeneity. Publication bias was evaluated by using Begg's and Egger's tests. RESULTS: A total of 16 articles with 1,501 patients were included in the present meta-analysis. It was demonstrated that a lower expression of miR-100 plays a negative role in the overall survival (OS) of patients with solid tumor (HR =1.92; 95% CI =1.25-2.94). In addition, the association between miR-100 and prognosis was also revealed in the following subgroups: non-small-cell lung cancer (NSCLC; HR =2.46; 95% CI =1.98-3.06), epithelial ovarian cancer (EOC; HR =2.29, 95% CI =1.72-3.04), and bladder cancer (BC; HR =4.14, 95% CI =1.85-9.27). CONCLUSION: This meta-analysis indicates that lower expression of miR-100 is related to poorer OS in patients with solid tumor, especially in those with NSCLC, EOC, and BC. MiR-100 is a promising prognosis predictor and may be a potential target for 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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".