Prognostic significance of microRNA-100 in solid tumors: an updated meta-analysis
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it