Regulation of FGF‐2 by an endogenous antisense RNA: Effects on cell adhesion and cell‐cycle progression
Bibliographic record
Abstract
Fibroblast growth factor (FGF-2) and its endogenous antisense RNA FGF antisense (FGF-AS) have been implicated in cancer progression and correlated with clinical outcomes of cancer patients. We previously reported that elevated FGF-AS expression is associated with reduced tumor recurrence and improved survival rates in patients with FGF-2-dependent esophageal adenocarcinoma. In the present study we examined the effect of siRNA knockdown of each transcript on the expression of its complementary partner RNA, and consequent changes in cellular phenotype and behavior. FGF-AS and FGF-2 were inversely expressed in a cell-cycle-dependent manner and siRNA-mediated knockdown of either FGF-AS or FGF-2 resulted in upregulation of the complementary transcript and protein. siRNA-mediated knockdown of FGF-AS was associated with a dramatic increase in cell-substratum adhesion and marked changes in the expression of a number of genes encoding adhesion molecules. Microarray analysis and RT-PCR analysis also revealed antithetical effects of FGF-2 and FGF-AS siRNA knockdown on the expression of a number of cell-cycle-related genes, including SKP2, SESTRIN-3, EIF4BP2, CDC27, and P190RhoGAP (P190). Cell-cycle analysis following siRNA-mediated knockdown of FGF-AS or FGF-2 indicate that both factors are involved in control of transition through the G₁ and G₂ boundaries, affecting cell-cycle progression, proliferation, and apoptosis. Finally, siRNA knockdown of FGF-AS resulted in a significant increase in invasion activity. These data indicate that regulatory interactions between FGF-AS and FGF-2 are involved in control of cell adhesion, cell-cycle progression, and invasion, providing a possible explanation for the protective effects of FGF-AS expression observed in FGF-2-dependent cancers.
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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".