MicroRNA miR-24 Enhances Tumor Invasion and Metastasis by Targeting PTPN9 and PTPRF to Promote EGF Signaling
Why this work is in the frame
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Bibliographic record
Abstract
MicroRNAs are known to play regulatory roles in gene expression associated with cancer development. We analyzed levels of the microRNA miR-24 in patients with breast carcinoma and found that miR-24 was higher in breast carcinoma samples than in benign breast tissues. We generated constructs expressing miR-24 and studied its functions using both in vitro and in vivo techniques. We found that the ectopic expression of miR-24 promoted breast cancer cell invasion and migration. In vivo experiments in mice indicated that the expression of miR-24 enhanced tumor growth, invasion into local tissues, metastasis to lung tissues and decreased overall mouse survival. In the miR-24-expressing cells and tumors, EGFR was highly phosphorylated, whereas expression of the phosphatases tyrosine-protein phosphatase non-receptor type 9 (PTPN9) and receptor-type tyrosine-protein phosphatase F (PTPRF) were repressed. We confirmed that miR-24 could directly target both PTPN9 and PTPRF. Consistent with this, we found that the levels of phosphorylated epidermal growth factor receptor (pEGFR) were higher whereas the levels of PTPN9 and PTPRF were lower in the patients with metastatic breast carcinoma. Ectopic expression of PTPN9 and PTPRF decreased pEGFR levels, cell invasion, migration and tumor metastasis. Furthermore, we found that MMP2, MMP11, pErk, and ADAM15 were upregulated, whereas TIMP2 was downregulated; all of which supported the roles of miR-24 in tumor invasion and metastasis. Our results suggest that miR-24 plays a key role in breast cancer invasion and metastasis. miR-24 could potentially be a target for cancer intervention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it