Expression of nm23-H1 in uveal melanoma
Bibliographic record
Abstract
Uveal melanoma (UM) is the most common malignant intraocular tumor in adults. Despite the high accuracy of clinical diagnosis and advances in local treatment, more than 50% of UM patients develop metastasis within 10 years of initial diagnosis. NM23 is one of the human metastasis suppressor genes. Reduced nm23-H1 expression is correlated with high metastatic potential in many different cancers. The purpose of this study is to investigate the expression of nm23-H1 in UM and its potential value as a prognostic marker. Immunostaining of nm23-H1 was verified in five human UM cell lines with different metastatic potentials. The expression level of nm23-H1 mRNA was evaluated with one-step quantitative real-time PCR. The invasion ability of the cell lines was assessed before and after silencing nm23-H1 with small interference RNA. Thirty-two cases of paraffin-embedded specimens of human UM were immunostained with nm23-H1 monoclonal antibody. The immunostaining was evaluated in a semiquantitative fashion based on extent and intensity. The real-time PCR results of five human UM cell lines showed that expression of nm23-H1 was higher in cell lines with low metastatic potential compared with those with high metastatic potential (P<0.05). The invasive ability of the UM cell lines increased after silencing nm23-H1 expression with small interference RNA (P<0.05). The immunostaining of nm23-H1 was cytoplasmic in all cell lines and UM patients samples. The increased immunostaining intensity of nm23-H1 in patients' samples was associated with better survival rate (Kaplan-Meier test P=0.0097). The expression of nm23-H1 was not correlated with other prognostic factors. It can be concluded that nm23-H1 may be a prognostic marker to predict the survival rate of UM patients and it has the potential to identify high-risk 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".