MicroRNA- 103 as a novel potential biomarker of poor prognosis and durg resistance in solid tumours
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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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".