The Long Noncoding RNA Metastasis-Associated Lung Adenocarcinoma Transcript-1 Regulates CCDC80 Expression by Targeting miR-141-3p/miR-200a-3p in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
OBJECTIVE: Our previous study showed that Coiled-Coil Domain Containing 80 (CCDC80) accelerates the development of atherosclerosis by decreasing lipoprotein lipase (LPL) expression and activity in apoE knockout mice. However, the regulatory mechanism for CCDC80 expression is unclear. This study was designed to evaluate whether noncoding RNAs involved the regulation of CCDC80 expression in vascular smooth muscle cells. METHODS AND RESULTS: Bioinformatics prediction and luciferase reporter gene results showed that miR-141-3p/200a-3p bound to the 3'UTR of CCDC80. Furthermore, miR-141-3p/200a-3p mimics decreased the expression of CCDC80 but increased LPL expression. Opposite results were observed with miR-141-3p/200a-3p inhibitors. We also found that lncRNA metastasis-associated lung adenocarcinoma transcript-1 (MALAT1) interacted with the sequences of miR-141-3p/200a-3p and decreased their expression. RT-qPCR and western blotting results showed that MALAT1 overexpression increased CCDC80 expression and decreased LPL expression, while MALAT1 knockdown displayed an opposite phenotype. The effects of both MALAT1 overexpression and knockdown were blocked by miR-141-3p/200a-3p mimics or inhibitors. CONCLUSIONS: Thus, we demonstrated that lncRNA MALAT1 regulates CCDC80 and LPL expression through miR-141-3p/200a-3p.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".