The Non-coding 3′UTR of CD44 Induces Metastasis by Regulating Extracellular Matrix Functions
Bibliographic record
Abstract
The importance of non-coding RNA transcripts in regulating microRNA (miRNA) functions, especially the 3'-untranslated region (3' UTR), has been revealed in recent years. Genes encoding the extracellular matrix normally produce large mRNA transcripts including the 3' UTR. How these large transcripts affect miRNA functions and how miRNAs modulate extracellular matrix protein expression are largely unknown. Here, we demonstrate that the overexpression of the CD44 3' UTR results in enhanced cell motility, invasion and cell adhesion in human breast carcinoma cell line MDA-MB-231. Furthermore, we found that expression of the CD44 3' UTR enhances metastasis in vivo. We hypothesize that increased expression of the CD44 3' UTR affects miRNA binding and modulates synthesis of the extracellular matrix. Computational analysis indicated that miRNAs that interact with the CD44 3' UTR also have binding sites in other matrix-encoding mRNA 3' UTRs, including collagen type 1α1 (Col1α1) repressed by miR-328 and fibronectin type 1 (FN1) repressed by miR-512-3p, miR-491 and miR-671. Protein analysis demonstrated that expression of CD44, Col1α1 and FN1 were synergistically upregulated in vitro and in vivo upon transfection of the CD44 3' UTR. The non-coding 3' UTR of CD44 interacts with multiple miRNAs that target extracellular matrix properties and thus can be used to antagonize miRNA activities.
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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".