Role of tumor suppressor p53 and micro-RNA interplay in multiple myeloma pathogenesis
Bibliographic record
Abstract
The molecular mechanisms underlying dysregulated wild type (wt) p53 in multiple myeloma (MM) have been subjects of intense investigation for years. Indeed, correlation of rarely occurring TP53 gene mutations or deletions with adverse clinical outcomes in MM patients is strongly established, while in majority of cases wtp53 seems to be non-functional or dysregulated bearing a high clinical impact. Interestingly, findings from recent investigations show that micro-RNAs (miRNAs) may contribute to suppression of wtp53 in MM, as they are now known to function as key regulatory elements in the p53 network. This area is shedding new light on understanding the biologic effects of dysregulated p53 in MM pathogenesis especially drug resistance. miRNAs such as miR-125b (oncomiR) or miR-34a (tumor suppressor-miR) can be negative or positive regulators of wtp53 function, respectively, with specific effects on MM cell viability. On the other hand, our knowledge of miRNA interaction with mutant (mt) p53 in MM, which is rather related to disease progression and resistance to therapy, is limited which demands in-depth exploration. Here, we will put forward the current knowledge on miRNA-p53 interaction in MM and its role in MM pathogenesis including drug resistance. We will also highlight the pre-clinical approaches for therapeutic application of miRNAs targeting p53 pathway.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".