MicroRNAs: A Link between Mammary Gland Development and Breast Cancer
Bibliographic record
Abstract
Breast cancer is among the most common cancers in women, second to skin cancer. Mammary gland development can influence breast cancer development in later life. Processes such as proliferation, invasion, and migration during mammary gland development can often mirror processes found in breast cancer. MicroRNAs (miRNAs), small, non-coding RNAs, can repress post-transcriptional RNA expression and can regulate up to 80% of all genes. Expression of miRNAs play a key role in mammary gland development, and aberrant expression can initiate or promote breast cancer. Here, we review the role of miRNAs in mammary development and breast cancer, and potential parallel roles. A total of 32 miRNAs were found to be expressed in both mammary gland development and breast cancer. These miRNAs are involved in proliferation, metastasis, invasion, and apoptosis in both processes. Some miRNAs were found to have contradictory roles, possibly due to their ability to target many genes at once. Investigation of miRNAs and their role in mammary gland development may inform about their role in breast cancer. In particular, by studying miRNA in development, mechanisms and potential targets for breast cancer treatment may be elucidated.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".