Targeting of CCBE1 by miR-330-3p in human breast cancer promotes metastasis
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRs) are involved in the regulation of many processes that contribute to malignancy, including cell proliferation, radiation resistance, invasion and metastasis. The role of miR-330-3p, an miR upregulated in breast cancer, remains unclear. METHODS: We examine the association of miR-330-3p with distant relapse-free survival in the Oxford cohort of breast cancer patients. We also study miR-330-3p function using in vitro invasion and ex ovo metastasis assays. Using in vitro luciferase assays, we validate a novel target gene for miR-330-3p, Collagen And Calcium Binding EGF Domains 1 (CCBE1). We assess functional consequences of CCBE1 loss by using siRNA-mediated knockdown followed by in vitro invasion assays. Lastly, we examine the expression profile of CCBE1 in breast carcinomas in the Curtis and TCGA Breast Cancer data sets using Oncomine Platform as well as distant relapse-free and overall survival of patients in the Helsinki University breast cancer data set according to CCBE1 expression status. RESULTS: miR-330-3p is enriched in breast cancer, and higher levels of miR-330-3p expression are associated with lower distant relapse-free survival in a cohort of breast cancer patients. Consistent with these observations, overexpression of miR-330-3p in breast cancer cell lines results in greater invasiveness in vitro, and miR-330-3p-overexpressing cells also metastasise more aggressively ex ovo. We identify CCBE1 as a direct target of miR-330-3p, and show that knockdown of CCBE1 results in a greater invasive capacity. Accordingly, in breast cancer patients CCBE1 is frequently downregulated, and its loss is associated with reduced distant relapse-free and overall survival. CONCLUSIONS: We show for the first time that miR-330-3p targets CCBE1 to promote invasion and metastasis. miR-330-3p and CCBE1 may represent promising biomarkers in breast cancer.
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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) | 0.000 | 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 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".