High Expression of<i>KIF14</i>in Retinoblastoma: Association with Older Age at Diagnosis
Bibliographic record
Abstract
PURPOSE: KIF14 a mitotic kinesin gene plays an important role in cytokinesis. Deregulation of KIF14 may be a pathway of tumor progression and results in decreased patient survival as seen in breast tumors. Recently, KIF14, a possible gene that drives gain of chromosome arm 1q (the most commonly gained chromosomal region in retinoblastoma), has been shown to be a strong oncogene candidate overexpressed by more than two orders of magnitude in retinoblastoma. This study was conducted to quantify the expression of KIF14 in human retinoblastoma tumors and correlate it with disease phenotype. METHODS: KIF14 expression was examined by using real-time RT-PCR in 30 retinoblastoma tumors with age at diagnosis between 3 and 68 months. Two 18-month-old, three adult (55-62 years), and three fetal (one 18 weeks' and another pooled retina of 18 and 20 weeks' gestation) retinas were used as the control. KIF14 expression was normalized to the housekeeping control gene TBP and compared with that in an 18-month-old control retina. The protein expression was confirmed in tumor cells by immunohistochemistry and phenotypic correlation was performed. RESULTS: KIF14 was expressed between 3- and 207-fold greater than 18-month-old retina in 30 retinoblastoma tumors (P < 0.0001). Immunohistochemistry revealed KIF14 localization to both nucleus and cytoplasm of tumor cells. KIF14 mRNA overexpression correlated significantly with older age at diagnosis (P = 0.006). There was no association with differentiation, invasion, or duration of the disease with KIF14 overexpression. CONCLUSIONS: Overexpression of KIF14 was confirmed in primary human retinoblastoma and showed that patients with an older age at diagnosis express significantly higher levels of KIF14.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".