MicroRNA-518-3p suppresses cell proliferation, invasiveness, and migration in colorectal cancer via targeting <i>TRIP4</i>
Bibliographic record
Abstract
MicroRNA (miR)-518-3p has been shown to function as a tumor suppressor. This study was conducted to investigate the effects of miR-518-3p in colorectal cancer (CRC). The miR-518-3p mimic, mimic negative control (NC), miR-518-3p inhibitor, inhibitor-NC, ShRNA-TRIP4, and ShRNA-NC vectors were transfected into SW480 cells using Lipofectamine 2000. Cell viability was detected using CCK-8. Colony formation, cell invasiveness, and cell migration were assessed by plate colony formation, Transwell assays, and wound healing assays, respectively. Relative mRNA and protein levels were detected using RT–qPCR and Western blot, respectively. The target gene thyroid hormone receptor interactor 4 (TRIP4) of miR-518-3p was identified and further verified using dual-luciferase reporter assay. Compared with normal tissues, levels of miR-518-3p were decreased and TRIP4 was significantly increased in the tissues from patients with CRC. Following transfection with a miR-518-3p mimic or ShRNA-TRIP4, cell viability decreased in a time-dependent manner, and colony formation rate, wound closure rate, and the number of invasive cells were much lower for the transfected cells than in the corresponding NC and control groups. miR-518-3p overexpression or silencing of TRIP4 significantly down-regulated the expression of MMP-2 and MMP-9. Knockdown of miR-518-3p had the opposite effects, and TRIP4 was identified as a target of miR-518-3p. The inhibitory effects of miR-518-3p on the progressions of CRC are associated with TRIP4.
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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 | 0.001 |
| 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 | 0.001 |
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".