Comparative Proteomic Analysis of Matched Primary and Metastatic Melanoma Cell Lines
Bibliographic record
Abstract
Identification of the biochemical pathways involved in the transformation from primary to metastatic melanoma is an area under intense investigation. A 2DE proteomics approach has been applied herein to the matched patient primary and metastatic melanoma cell lines WM-115 and WM-266-4, respectively, to better understand the processes that underlie tumor progression. Image analysis between samples aligned 470 common gel spots. Quantitative gel analysis indicated 115 gel spots of greater intensity in the metastatic line compared with the primary one, leading to the identification of 131 proteins via database searching of nano-LC-ESI-Q-TOF-MS/MS data. This more than tripled the number of proteins previously shown to be of higher abundance during melanoma progression. Also observed were 22 gel spots to be of lesser intensity in the metastatic line with respect to the primary one. Of these gel spots 15 proteins could be identified. Numerous proteins from both groups had not been reported previously to participate in melanoma progression. Further analysis of one protein, cyclophilin A, confirmed that this protein is expressed at higher levels in metastatic melanoma compared with primary melanoma and normal fibroblasts. Overall, this study expands our knowledge of protein modulation during melanoma stages, and suggests new targets for inhibitor development.
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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 | 0.000 |
| 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 | 0.001 |
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".