<i>OTOR</i> in breast carcinoma as a potent prognostic predictor correlates with cell proliferation, migration, and invasiveness
Bibliographic record
Abstract
Otoraplin (OTOR), recognized as an important cochlear gene, has a predicted secretory signal peptide sequence and harbors a high degree of cross-species conservation. However, its role in tumor progression is relatively unclear, especially in breast carcinoma (BC). This study investigated the clinicopathological significance of OTOR in breast infiltrating ductal carcinoma (IDC) with high metastasis to uncover its biological function in BC. OTOR was highly overexpressed in BC tissues and cells compared with normal samples. OTOR overexpression was associated with certain clinicopathological characteristics and poorer prognosis (overall survival; OS) of patients with breast IDC. As determined using CCK-8, colony formation, wound-healing, and Transwell assays, silencing OTOR using siRNA impeded BC-cell proliferation, migration, and invasiveness, which may have resulted from inactivating the mitogen-activated protein kinase – extracellular-signal-regulated kinase pathway. These results indicate that OTOR plays a crucial role in the progression of and prognosis for BC, which could help to identify future therapeutic targets for treating BC patients.
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".