miR-623 Targets Metalloproteinase-1 and Attenuates Extravasation of Brain Metastatic Triple-Negative Breast Cancer Cells
Bibliographic record
Abstract
Background: Most breast cancer-related deaths result from metastasis. Understanding the molecular basis of metastasis is needed for the development of effective targeted and preventive strategies. Matrix metalloproteinase-1 (MMP1) plays an important role in brain metastasis (BM) of triple-negative breast cancer (TNBC) by promoting extravasation of cancer cells across the brain endothelium (BE). MMP1 expression is controlled by endogenous microRNAs. Preliminary bioinformatics analysis has revealed that miR-623, known to target the 3'UTR of MMP1, is significantly downregulated in brain metastatic tumors compared to primary BC tumors. However, the involvement of miR-623 in MMP1 upregulation in breast cancer brain metastatic cells (BCBMC) remains unexplored. Here, we investigated the role of miR-623 in MMP1 regulation and its impact on the extravasation of TNBC cells through the BE in vitro. Materials and Methods: A loss-and-gain of function method was employed to address the effect of miR-623 modulation on MMP1 expression. MMP1 regulation by miR-623 was investigated by real-time PCR, western blot, luciferase and transwell migration assays using an in vitro human BE model. Results: Our results confirmed that brain metastatic TNBC cells express lower levels of miR-623 compared with cells having low propensity to spread toward the brain. miR-623 binds to the 3'-untranslated region of MMP1 transcript and downregulates its expression. Restoring miR-623 expression significantly decreased MMP1 expression, preserved the endothelial barrier integrity, and attenuated transmigration of BCBMC through the BE. Conclusion: Our study elucidates, for the first time, the crucial role of miR-623 as MMP1 direct regulator in BCBMC and sheds light on miR-623 as a novel therapeutic target that can be exploited to predict and prevent brain metastasis in TNBC. Importantly, the presents study helps in unraveling a brain metastasis-specific microRNA signature in TNBC that can be used as a guide to personalized metastasis prediction and preventive approach with better therapeutic outcome.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".