Understanding the TGF-beta language or getting lost n translation: The TGFbeta microRNAome
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed female cancer in the world. Consequently, researchers deployed tremendous efforts that led to a significant increase in life expectancy and quality of life. Despite those advances, breast cancer remains the second leading cause of female cancer-related deaths after lung cancer in Canada. Considering their cost-effective nature, microRNAs (miRNAs) have become an interesting therapeutic avenue in the treatment of diseases, including cancer. Micro-RNAs are small, non-coding RNAs whose main function is in regulating the output of RNA translation. By binding the 3’UTR of the messages they target, miRNAs add a level of regulation that ensures homeostasis is maintained. In the advent of cancer, the miRNA expression profile is dysregulated, this being the cause for, or the consequence of, expression of genes that favour tumour progression and the silencing of those that protect the cell from tumorigenesis.In this thesis, we examined the regulation of miRNAs in the context of TGFbeta-mediated mammary tumour suppression and progression. This cytokine behaves as a tumour suppressor, a role commonly observed in healthy epithelial cells and early carcinoma but lost to benefit a pathway that signals for tumour progression in more advanced cancers. Interestingly, TGF also regulates the expression of a number of miRNAs. Here, we show that TGFbeta negatively regulates the expression of the miR-30 family of miRNAs. We found them to antagonize TGFbeta-mediated tumour suppression by preventing the modulation of the expression of genes important for cell apoptosis, cytostasis, and inhibition of immortalization. We found the miR-30s are more highly expressed in basal breast cancer, where TGFbeta lost its tumour suppressive function, and that their inhibition impedes tumour development in vivo. Moreover, we found another down-regulated miRNA, miR-584, to prevent TGFbeta-mediated cell migration. This miRNA functions by preventing the expression of the actin-binding protein PHACTR1, leading to a reorganization of the cytoskeleton that prevents cell motility. Thus, we found a way to control both the tumour-suppressor and pro-metastatic roles of TGFbeta.With this research, we have elucidated the involvement of miRNAs downstream of TGFbeta in the development of breast cancer, and potentially opened the door for the development of a TGFbeta-targeting therapy
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".