Expression Profiling Reveals Novel Pathways in the Transformation of Melanocytes to Melanomas
Bibliographic record
Abstract
Affymetrix and spotted oligonucleotide microarrays were used to assess global differential gene expression comparing normal human melanocytes with six independent melanoma cell strains from advanced lesions. The data, validated at the protein level for selected genes, confirmed the overexpression in melanoma cells relative to normal melanocytes of several genes in the growth factor/receptor family that confer growth advantage and metastasis. In addition, novel pathways and patterns of associated expression in melanoma cells not reported before emerged, including the following: (a) activation of the NOTCH pathway; (b) increased Twist expression and altered expression of additional transcriptional regulators implicated in embryonic development and epidermal/mesenchymal transition; (c) coordinated activation of cancer/testis antigens; (d) coordinated down-regulation of several immune modulation genes, in particular in the IFN pathways; (e) down-regulation of several genes implicated in membrane trafficking events; and (f) down-regulation of growth suppressors, such as the Prader-Willi gene NECDIN, whose function was confirmed by overexpression of ectopic Flag-necdin. Validation of differential expression using melanoma tissue microarrays showed that reduced ubiquitin COOH-terminal esterase L1 in primary melanoma is associated with worse outcome and that increased expression of the basic helix-loop-helix protein Twist is associated with worse outcome. Some differentially expressed genes reside on chromosomal regions displaying common loss or gain in melanomas or are known to be regulated by CpG promoter methylation. These results provide a comprehensive view of changes in advanced melanoma relative to normal melanocytes and reveal new targets that can be used in assessing prognosis, staging, and therapy of melanoma 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".