Differentiation of mammary tumors and reduction in metastasis upon <i>Malat1</i> lncRNA loss
Bibliographic record
Abstract
Genome-wide analyses have identified thousands of long noncoding RNAs (lncRNAs). Malat1 (metastasis-associated lung adenocarcinoma transcript 1) is among the most abundant lncRNAs whose expression is altered in numerous cancers. Here we report that genetic loss or systemic knockdown of Malat1 using antisense oligonucleotides (ASOs) in the MMTV (mouse mammary tumor virus)-PyMT mouse mammary carcinoma model results in slower tumor growth accompanied by significant differentiation into cystic tumors and a reduction in metastasis. Furthermore, Malat1 loss results in a reduction of branching morphogenesis in MMTV-PyMT- and Her2/neu-amplified tumor organoids, increased cell adhesion, and loss of migration. At the molecular level, Malat1 knockdown results in alterations in gene expression and changes in splicing patterns of genes involved in differentiation and protumorigenic signaling pathways. Together, these data demonstrate for the first time a functional role of Malat1 in regulating critical processes in mammary cancer pathogenesis. Thus, Malat1 represents an exciting therapeutic target, and Malat1 ASOs represent a potential therapy for inhibiting breast cancer progression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".