Datasets exploring putative lncRNA-miRNA-mRNA axes in breast cancer cell lines
Bibliographic record
Abstract
Long non-coding RNA (lncRNA)/microRNA (miRNA)/messenger RNA (mRNA) interactions regulate oncogenesis and tumour suppression in breast cancer. Oncogenic lncRNA/miRNA/mRNA axes may offer novel therapeutic targets; therefore, identifying such axes is a clinically relevant undertaking. To explore miRNAs regulated by oncogenic lncRNAs, we queried the NCBI Gene Expression Omnibus (GEO) database to find datasets that profiled gene expression changes upon lncRNA knockdown in breast cancer. We identified four microarray datasets that permitted our interrogation of genes regulated by lncRNAs LincK, LincIN, SPRY4-IT1 and AC009283.1. We specifically analysed changes in miRNA transcripts within these datasets to study miRNAs regulated by each of the four lncRNAs. We subsequently identified the predicted mRNA targets for these miRNAs to uncover possible lncRNA/miRNA/mRNAs axes in breast cancer. These axes may be candidates for future investigation of gene regulation in 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".