Retracted: Up‐regulation of microRNA‐145 promotes differentiation by repressing OCT4 in human endometrial adenocarcinoma cells
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
BACKGROUND: MicroRNA-145 (miR-145) has been reported to be a tumor-suppressing agent in several studies. It can repress pluripotency and control human embryonic stem cell differentiation by regulating the core pluripotency factor OCT4. However, it is not known whether miR-145 can play a role in inducing tumor cell differentiation and repressing growth of tumors. METHODS: Ishikawa cells, the established human endometrial cancer cells, were treated with miR-145 mimics, inhibitor, or small interfering RNA OCT4. miR-145 levels were assayed using TaqMan microRNA assays, and the messenger RNA levels of OCT4 and the differentiation marker glycodelin were measured using real-time polymerase chain reaction. The protein levels of OCT4 and glycodelin were characterized via flow cytometry, western blotting, and immunohistochemistry. In vivo activity was measured in a xenograft mouse model. RESULTS: Up-regulating miR-145 reduced the expression of OCT4 and induced the differentiation of Ishikawa cells to closely resemble normal endometrial epithelium both in vitro and in vivo. miR-145 successfully inhibited tumor growth. We also found that in patients with endometrial carcinoma, miR-145 and OCT4 were expressed in tissues, and there was a relationship between miR-145, OCT4, and the degree of tumor cell differentiation. CONCLUSIONS: Our results strongly suggested that miR-145 is a tumor cell differentiation-inducing agent in endometrial carcinoma, and that miR-145 or OCT4 may be useful markers for grading endometrial carcinoma.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".