MicroRNA-196a is regulated by ER and is a prognostic biomarker in ER+ breast cancer
Bibliographic record
Abstract
BACKGROUND: MicroRNAs are potent post-transcriptional regulators involved in all hallmarks of cancer. Mir-196a is transcribed from two loci and has been implicated in a wide range of developmental and pathogenic processes, with targets including Hox, Fox, Cdk inhibitors and annexins. Genetic variants and altered expression of MIR196A are associated with risk and progression of multiple cancers including breast cancer, however little is known about the regulation of the genes encoding this miRNA, nor the impact of variants therein. METHODS: Genomic data and chromatin interaction analysis were used to discover functional promoter and enhancer elements for MIR196A. Expression data were used to associate MIR196A with mechanisms of resistance, breast cancer subtypes and prognosis. RESULTS: Here we demonstrate that MIR196A displays complex and dynamic expression patterns, in part controlled by long-range transcriptional regulation between promoter and enhancer elements bound by ERα. Expression of this miRNA is significantly increased in drug-resistant models of hormone-receptor positive disease. The expression of MIR196A also proves to be a robust prognostic factor for patients with advanced and post-menopausal ER+ disease. CONCLUSION: This work sheds light on the normal and abnormal regulation of MIR196A and provides a novel stratification method for therapeutically resistant breast cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".