Oncogenic Potential of the <i>miR-106-363</i> Cluster and Its Implication in Human T-Cell Leukemia
Bibliographic record
Abstract
We previously reported the identification of the Kis2 common retrovirus integration site, located on mouse chromosome X, in radiation leukemia virus-induced T-cell leukemias. Tumors with a provirus at the Kis2 locus overexpressed a novel noncoding RNA (ncRNA) with a complex splicing pattern and no polyA tail. Database upgrade revealed the presence of a microRNA (miRNA) cluster, miR-106-363, just downstream of the Kis2 ncRNAs. We found that Kis2 ncRNAs are the pri-miRNA of miR-106-363, and we present evidence that Kis2 ncRNA overexpression in mouse tumors results in miR-106a, miR-19b-2, miR-92-2, and miR-20b accumulation. We show the oncogenic potential of those miRNAs in anchorage independence assay and confirm pri-miR-106-363 overexpression in 46% of human T-cell leukemias tested. This overexpression contributes in rising miR-92 and miR-19 levels, as this is the case for miR-17-92 cluster overexpression. Furthermore, we identified myosin regulatory light chain-interacting protein, retinoblastoma-binding protein 1-like, and possibly homeodomain-interacting protein kinase 3 as target genes of this miRNA cluster, which establishes a link between these genes and T-cell leukemia for the first time.
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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 | 0.000 |
| 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) | 0.001 | 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".