MicroRNA- 103 as a novel potential biomarker of poor prognosis and durg resistance in solid tumours
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUD: Multiple studies have reported that microRNA-103 is unregulated in a variety of tumours, involved in tumorigenesis, and associated with tumour prognosis, so a systematic review and meta-analysis were performed to determine the relationship between microRNA-103 and the prognosis of solid tumours. METHODS: The PubMed, Web of Science, and EMBASE databases were searched to retrieve articles to determine the relationship between microRNA-103 and tumour prognosis. Relevant articles were graded according to the Newcastle-Ottawa Scale (NOS). The 95% confidence interval (CI) was calculated by the fixed-effect/random-effect models and the risk ratio (RR) were summarised. RESULTS: Eight out of 162 retrieved articles were included in this review, with an average NOS score of 7.2 points. Four studies of tissue samples and four studies of serum samples suggested that the overexpression of microRNA-103 was associated with overall survival (RR = 2.65, 95% CI: 1.79-3.93, P = 0.000 and RR = 3.31, 95% CI: 2.04-5.36, P = 0.000, respectively). CONCLUSION: This meta-analysis, combining 9 studies, found that overexpression of miRNA-103 is associated with poor prognosis in solid tumours, particularly in serum samples. Sensitivity analysis confirmed that high tissue expression correlates with poor outcomes. miRNA-103's role in tumor progression suggests its potential as a prognostic biomarker for solid tumors, warranting further research for clinical applications.
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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