Developmental Regulation of circRNAs in Normal and Diseased Mammary Gland: A Focus on circRNA-miRNA Networks
Bibliographic record
Abstract
Circular RNAs (circRNAs) have emerged as critical regulators in various biological processes including diseases. In the mammary gland (MG), which undergoes most of its development postnatally, circRNAs play pivotal roles in both physiological and pathological contexts. This review highlights the involvement of circRNAs during key developmental stages of the MG, with particular emphasis on lactation, where circRNA-miRNA networks significantly influence milk secretion and composition. CircRNAs exhibit stage-, breed- and species-specific expression patterns during lactation, which underscores their complexity. This intricate regulation also plays a significant role in pathological conditions of the MG, where dysregulated circRNA expression contributes to disease progression such as mastitis, early breast cancer (BC) stages, and epithelial-to-mesenchymal transition in BC (EMT). In mastitis, altered circRNA expression disrupts immune responses and compromises epithelial integrity. During early BC progression, circRNAs drive cell proliferation, while in EMT, they facilitate metastatic processes. By focusing on the circRNA-miRNA interactions underlying these processes, this review highlights their potential use as biomarkers for MG development, disease progression, and as therapeutic targets.
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 | 0.001 |
| 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 | 0.000 | 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".