MiR-4458 inhibits breast cancer cell growth, migration, and invasiveness by targeting CPSF4
Bibliographic record
Abstract
Numerous studies have reported that CPSF4 is over-expressed in a large percentage of human lung cancers, and CPSF4 has been identified as a potential oncogene of human lung tumor. Downregulation of CPSF4 inhibits the proliferation and promotes the apoptosis of lung adenocarcinoma cells. A previous study by our group also found overexpression of CPSF4 in breast cancer (BC), and was closely associated with a poor prognosis for the patient. This study investigates microRNAs (miRNAs) that target CPSF4 to modulate BC cell proliferation. We found that miR-4458 was noticeably reduced in BC tissues and cells. Using a miR-4458 mimic, we found that cell proliferation, migration, and invasiveness were suppressed by miR-4458 overexpression, and were enhanced by reducing the expression of miR-4458. Moreover, the results from bioinformatics analyses suggest a putative target site in the CPSF4 3′-UTR. Furthermore, using luciferase reporter assays and Western blotting, we verified that miR-4458 directly targets the 3′-UTR of CPSF4 and downregulates COX-2 and h-TERT, which are downstream target genes of CPSF4. Additionally, PI3K/AKT and ERK were shown to be inhibited by miR-4458 overexpression in BC cells. Moreover, miR-4458 suppresses BC cell growth in vivo. Consequently, these results suggest that the miR-4458–CPSF4–COX-2–hTERT axis might serve as a potential target for the treatment of BC patients.
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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) | 0.001 | 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".