Basic Fibroblast Growth Factor (FGF-2) Overexpression Is a Risk Factor for Esophageal Cancer Recurrence and Reduced Survival, which Is Ameliorated by Coexpression of the FGF-2 Antisense Gene
Bibliographic record
Abstract
PURPOSE: The basic fibroblast growth factor (FGF-2) gene is bidirectionally transcribed to generate overlapping sense and antisense (FGF-AS) mRNAs. FGF-AS has been implicated in the post-transcriptional regulation of FGF-2 expression. The aim of this study was to characterize FGF-2 and FGF-AS in esophageal cancer and to correlate their expression with clinicopathologic findings and outcome. EXPERIMENTAL DESIGN: Reverse transcription-PCR was used to study FGF-2 and FGF-AS mRNA expression (normalized to glyceraldehyde-3-phosphate dehydrogenase) in 48 esophageal cancers relative to matched histologically normal esophageal epithelia (internal control). We used Cox proportional hazards analysis to calculate hazard ratios for recurrence and survival of patients with underexpression relative to the overexpression of FGF-2 and/or FGF-AS. RESULTS: Overexpression of FGF-2 mRNA, by comparison with tumors underexpressing FGF-2, was associated with significantly increased risk for tumor recurrence (hazard ratio, 3.80; 95% confidence interval, 1.64-8.76) and reduced overall survival (hazard ratio, 2.11; 95% confidence interval, 1.0-4.58). When the effects of FGF-2 and FGF-AS were considered simultaneously, the association of FGF-2 mRNA overexpression with recurrence and mortality was even more pronounced, whereas FGF-AS mRNA overexpression was associated with reduced risk for recurrence and improved survival. CONCLUSIONS: Overexpression of FGF-2 mRNA is associated with tumor recurrence and reduced survival after surgical resection of esophageal cancer and that these risks are reduced in tumors coexpressing the FGF-AS mRNA. These data support the hypothesis that FGF-AS is a novel tumor suppressor that modulates the effect of FGF-2 expression and may have potential clinical application to the development of novel therapeutic strategies.
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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 | 0.000 | 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".