Long Non-Coding RNA TRG-AS1 Promoted Proliferation and Invasion of Lung Cancer Cells Through the miR-224-5p/SMAD4 Axis
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to investigate the role and mechanism of long non-coding RNA (lncRNA) TRG-AS1 in mediating the proliferation, invasion and migration of lung cancer cells as well lung tumor growth. METHODS: Firstly, the expression levels of TRG-AS1, miR-224-5p in lung cancer tissues or cells were quantified by quantitative real-time PCR. Western blot analysis was conducted to measure the expression levels of protein SMAD4. CCK-8 assay, wound healing assay and transwell assay were conducted to evaluate cell proliferation, migration and invasion, respectively. The interaction between TRG-AS1 and miR-224-5p was predicted by bioinformatics analysis. Dual-luciferase assay and RNA pull-down assay were performed to further confirm their interaction. In addition, the interaction between miR-224-5p and SMAD4 was detected by RIP assay. RESULTS: The results showed that TRG-AS1 was highly upregulated and miR-224-5p was downregulated in lung cancer. A negative correlation was found between TRG-AS1 and miR-224-5p. Furthermore, upregulation of TRG-AS1 promoted cell proliferation and invasion, while overexpression of miR-224-5p attenuated the effects of TRG-AS1. The downstream protein SMAD4 played an important role. In vivo study showed that knockdown of TRG-AS1 effectively retarded tumor growth. DISCUSSION: Our data suggested that the TRG-AS1/miR-224-5p/SMAD4 axis may be a potential therapeutic target in lung cancer.
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 | 0.001 |
| 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) | 0.002 | 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".