<i>Retracted</i>: miR‐942 promotes tumor migration, invasion, and angiogenesis by regulating EMT via BARX2 in non‐small‐cell lung cancer
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
Epithelial-mesenchymal transition (EMT) has an important function in cancer. Recently, microRNAs have been reported to be involved in EMT by regulating target genes. miR-942 is considered a novel oncogene in esophageal squamous cell carcinoma. However, its role in non-small-cell lung cancer (NSCLC) has not been investigated. In this study, the expression of miR-942 in NSCLC patients tumor and paired adjacent tissues were assessed by quantitative real-time polymerase chain reaction and in situ hybridization. Transwell, wound healing, tube formation, and tail vein xenograft assays were conducted to assess miR-942's function in NSCLC. Potential miR-942 targets were confirmed using dual-luciferase reporter assays, immunohistochemistry, immunoblot, and rescue experiments. The results showed miR-942 is relatively highly expressed in human NSCLC tissues and cells. In vitro assays demonstrated that overexpression of miR-942 promoted cell migration, invasion, and angiogenesis. Tail vein xenograft assays suggested that miR-942 contributed to NSCLC metastasis in vivo. Three bioinformatics software was searched, and BARX2 was predicted as a downstream target of miR-942. Direct interaction between them was validated by dual-luciferase assays. Rescue experiments further confirmed that BARX2 overexpression could reverse functional changes caused by miR-942. Moreover, miR-942 increased EMT-associated proteins N-cadherin and vimentin by inhibiting BARX2, while E-cadherin expression is reduced. In summary, this study reveals that miR-942 induces EMT-related metastasis by directly targeting BARX2, which may provide a potential therapeutic strategy for NSCLC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".