Ablation of miR-10b Suppresses Oncogene-Induced Mammary Tumorigenesis and Metastasis and Reactivates Tumor-Suppressive Pathways
Bibliographic record
Abstract
The invasive and metastatic properties of many human tumors have been associated with upregulation of the miRNA miR-10b, but its functional contributions in this setting have not been fully unraveled. Here, we report the generation of miR-10b-deficient mice, in which miR-10b is shown to be largely dispensable for normal development but critical to tumorigenesis. Loss of miR-10b delays oncogene-induced mammary tumorigenesis and suppresses epithelial-mesenchymal transition, intravasation, and metastasis in a mouse model of metastatic breast cancer. Among the target genes of miR-10b, the tumor suppressor genes Tbx5 and Pten and the metastasis suppressor gene Hoxd10 are significantly upregulated by miR-10b deletion. Mechanistically, miR-10b promotes breast cancer cell proliferation, migration, and invasion through inhibition of the expression of the transcription factor TBX5, leading to repression of the tumor suppressor genes DYRK1A and PTEN In clinical specimens of breast cancer, the expression of TBX5, HOXD10, and DYRK1A correlates with relapse-free survival and overall survival outcomes in patients. Our results establish miR-10b as an oncomiR that drives metastasis, termed a metastamiR, and define the set of critical tumor suppressor mechanisms it overcomes to drive breast cancer progression. Cancer Res; 76(21); 6424-35. ©2016 AACR.
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 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 | 0.001 |
| 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".