Angioregulatory microRNAs in breast cancer: Molecular mechanistic basis and implications for therapeutic strategies
Bibliographic record
Abstract
Background: Cancer-associated angiogenesis is a fundamental process in tumor growth and metastasis. A variety of signaling regulators and pathways contribute to establish neovascularization, among them as small endogenous non-coding RNAs, microRNAs (miRNAs) play prominent dual regulatory function in breast cancer (BC) angiogenesis. Aim of Review: This review aims at describing the current state-of-the-art in BC angiogenesis-mediated by angioregulatory miRNAs, and an overview of miRNAs dysregulation association with the anti-angiogenic response in addition to potential clinical application of miRNAs-based therapeutics. Key Scientific Concepts of Review: Angioregulatory miRNA-target gene interaction is not only involved in sprouting vessels of breast tumors but also, trans-differentiation of BC cells to endothelial cells (ECs) in a process termed vasculogenic mimicry. Using canonical and non-canonical angiogenesis pathways, the tumor cell employs the oncogenic characteristics such as miRNAs dysregulation to increase survival, proliferation, oxygen and nutrient supply, and treatment resistance. Angioregulatory miRNAs in BC cells and their microenvironment have therapeutic potential in cancer treatment. Although, miRNAs dysregulation can serve as tumor biomarker nevertheless, due to the association of miRNAs dysregulation with anti-angiogenic resistant phenotype, clinical benefits of anti-angiogenic therapy might be challenging in BC. Hence, unveiling the molecular mechanism underlying angioregulatory miRNAs sparked a booming interest in finding new treatment strategies such as miRNA-based therapies in BC.
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 | 0.001 | 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".