LncRNA MSC-AS1 facilitates lung adenocarcinoma through sponging miR-33b-5p to up-regulate GPAM
Bibliographic record
Abstract
Many reports have indicated that long non-coding RNAs (lncRNAs) are closely associated with the occurrence and development of various cancers. Musculin antisense RNA 1 (MSC-AS1) is a an lncRNA known to act as an oncogene in several types of human cancers; however, its specific function in lung adenocarcinoma (LUAD) is still unclear. For this study, we designed and conducted experiments to clarify the function of the lncRNA MSC-AS1 in LUAD and its underlying mechanisms. We found that the expression of MSC-AS1 was significantly higher in LUAD tissues and cells than that in normal ones. Through loss-of function assays, we confirmed that the proliferation of LUAD cells was significantly restrained by down-regulation of MSC-AS1 and the rate of cell apoptosis was accelerated. The results from our mechanistic experiments showed that MSC-AS1 interacts with microRNA-33b-5p (miR-33b-5p). Moreover, glycerol-3-phosphate acyltransferase, mitochondrial (GPAM) was found to be a direct target gene of miR-33b-5p, and it has similar functions to MSC-AS1. Further, inhibition of miR-33b-5p or overexpression GPAM reversed the inhibitory effects of MSC-AS1 silencing on LUAD cell growth. In short, MSC-AS1 facilitates LUAD progression through sponging miR-33b-5p to up-regulate GPAM.
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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) | 0.002 | 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".