Identification and characterization of long non-coding RNAs as targets of mammary tumor cell proliferation and migration
Bibliographic record
Abstract
Abstract Recent genome-wide studies revealed that as much as 80% of the human genome can be transcribed whereas only 2% of this RNA is translated into proteins. Non-coding transcripts can be subdivided into several groups, with long non-coding RNAs (lncRNAs) representing the largest and most diverse class. With breast cancer being the most frequent malignancy in women worldwide, we set out to investigate the potential of lncRNAs as novel therapeutic targets. By performing RNA-Seq on tumor sections and mammary organoids from MMTV-PyMT and MMTV-Neu-NDL mice, modeling the luminal B and HER2/neu-amplified subtypes of human breast cancer respectively, we generated a comprehensive catalog of differentially expressed lncRNAs. We identified several hundred potentially oncogenic lncRNAs that were over-expressed in a subtype-specific manner as well as numerous lncRNAs up-regulated in both models. Among these lncRNA we defined a subset of 30 previously uncharacterized lncRNAs as Mammary Tumor Associated RNAs (MaTARs) and we identified human orthologs. We functionally validated the role of these MaTARs by antisense oligonucleotide (ASO) mediated knockdown in primary mammary tumor cells and 3D ex vivo organoids. Upon independent knockdown of 15 MaTARs, we observed significantly reduced cell proliferation, invasion and/or collective cell migration in a cancer-specific context. Thus, MaTARs are likely key drivers of mammary tumor progression and/or metastasis and represent promising new therapeutic targets.
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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 | 0.000 |
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".