<b> <i>Skp2</i> </b> Gene Copy Number Aberrations Are Common in Non-Small Cell Lung Carcinoma, and Its Overexpression in Tumors with <b> <i>ras</i> </b> Mutation Is a Poor Prognostic Marker
Bibliographic record
Abstract
PURPOSE: Skp2 plays a critical role in cell cycle progression, especially at the G(1)-S transition, putatively through its control of several cell cycle regulator proteins. The Skp2 gene is located on a region of chromosome 5p that is commonly overrepresented in lung cancer. The present study aimed to evaluate Skp2 abnormalities and their prognostic value in non-small cell lung cancer (NSCLC). EXPERIMENTAL DESIGN: In total 16 NSCLC cell lines and 163 primary tumors were included in studies to measure Skp2 relative gene copy number, mRNA abundance, and protein level. The tumors were also evaluated for p27 protein expression level and ras mutation. These values were correlated with the clinical and pathological features of the patients. RESULTS: Skp2 relative gene copy number aberrations were found in 88 and 65% of NSCLC cell lines and primary tumors, respectively. Overrepresentation was especially common among squamous cell carcinoma (74%). Both gene copy overrepresentation (13%) and loss (35%) were found in adenocarcinoma. Skp2 relative gene copy number was significantly correlated with mRNA and protein levels, but none of these were correlated with p27 protein levels. Neither high Skp2 protein expression nor ras mutation was prognostically significant. In NSCLCs with ras mutation, however, high Skp2 protein expression was a significant independent poor prognostic marker. CONCLUSION: There appears to be a synergistic interaction between high Skp2 protein expression and ras mutation with negative impact on the survival of NSCLC patients.
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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 | 0.000 |
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".