Identification of differentially expressed genes in uveal melanoma using suppressive subtractive hybridization.
Bibliographic record
Abstract
PURPOSE: Uveal melanoma (UM) is the most common primary cancer of the eye, resulting not only in vision loss, but also in metastatic death. This study attempts to identify changes in the patterns of gene expression that lead to malignant transformation and proliferation of normal uveal melanocytes (UVM) using the Suppressive Subtractive Hybridization (SSH) technique. METHODS: The SSH technique was used to isolate genes that are differentially expressed in the TP31 cell line derived from a primary UM compared to UVM. The expression level of selected genes was further validated by microarray, semi-quantitative RT-PCR and western blot analyses. RESULTS: Analysis of the subtracted libraries revealed that 37 and 36 genes were, respectively, up- and downregulated in TP31 cells compared to UVM. Differential expression of the majority of these genes was confirmed by comparing UM cells with UVM by microarray. The expression pattern of selected genes was analyzed by semi-quantitative RT-PCR and western blot, and was found to be consistent with the SSH findings. CONCLUSIONS: We demonstrated that the SSH technique is efficient to detect differentially expressed genes in UM. The genes identified in this study represent valuable candidates for further functional analysis in UM and should be informative in studying the biology of this tumor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".