MiRNAs with prognostic significance in multiple myeloma
Bibliographic record
Abstract
BACKGROUND: Multiple myeloma (MM) is a clonal plasma cell malignancy associated with hypercalcemia, bone lesions, and renal failure. The prognostic significance of the mutation of miRNAs, one kind of small noncoding RNA molecules that can modulate gene expression, should be confirmed in non-Hodgkin lymphomas (NHL). This study aimed to identify the prognostic value of miRNAs in patients with MM. METHODS: A meta-analysis was performed to estimate the pooled hazard ratios and their corresponding 95% confidence intervals for the associations between levels of miRNA expression (predictive factors) and outcomes in patients with MM. We systematically searched the PubMed, Web of Science, and China National Knowledge Infrastructure databases (final search conducted January 1, 2018) to identify eligible studies. Eligible studies were included by certain inclusion and exclusion criteria, whose quality was assessed by Newcastle-Ottawa Scale. RESULTS: After performing the literature search and review, 10 relevant studies, including 1214 cases, were identified. The results of our meta-analysis revealed that upregulated miR-92a level and downregulated miR-16, miR-25, miR-744, miR-15a, let-7e, and miR-19b expression were associated with poor prognosis in MM. CONCLUSIONS: This study identified miRNAs could serve as potential prognostic biomarkers in MM. Given the limited research available, the clinical application of these findings has yet to be verified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".