LncRNA NRAD1 regulates the triple-negative breast cancer transcriptome by miRNA biogenesis, localization, and predominately non-ceRNA interactions
Bibliographic record
Abstract
Breast cancer is a leading cause of cancer mortality in women with triple-negative breast cancer (TNBC) presenting greater treatment challenges due to its aggressive disease progression. Understanding TNBC's unique cell signaling and gene expression profiles will reveal novel therapeutic strategies. Non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have emerged as key regulators of gene expression and potential therapeutic targets. This study focuses on a TNBC-enriched lncRNA, non-coding RNA in the aldehyde dehydrogenase 1A pathway (NRAD1, previously LINC00284), which promotes progression in multiple cancers. Our analysis reveals that NRAD1 is central to miRNA-mRNA networks in TNBC cells, mediating cancer-promoting gene expression changes. Fractionation studies showed that NRAD1 is primarily located in the nucleus and mitochondria, with some cytoplasmic presence allowing for transcript-specific competitive endogenous RNA (ceRNA) interactions with miRNAs. However, NRAD1 primarily effects miRNAs independently of ceRNA activity, instead upregulating DICER (a miRNA biogenesis protein), altering sub-cellular distribution, and reducing biogenesis of mitochondria-localized miRNA (i.e., miR-4485-3p). These findings demonstrate novel regulatory interactions between the cancer-promoting lncRNA NRAD1 and miRNAs that alter gene expression in TNBC, expanding our understanding of regulatory lncRNA-miRNA effects, TNBC biology, and highlighting future therapeutic strategies for targeting non-coding RNAs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".