RETRACTED ARTICLE: The Breast Cancer Susceptibility Gene Product Fibroblast Growth Factor Receptor 2 Serves as a Scaffold for Regulation of NF-κB Signaling
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Fibroblast growth factor (FGF) receptor 2 (FGFR2) has been identified in genome-wide association studies to be associated with increased breast cancer risk; however, its mechanism of action remains unclear. Here we show that the two major FGFR2 alternatively spliced isoforms, FGFR2-IIIb and FGFR2-IIIc, interact with IκB kinase β and its downstream target, NF-κB. FGFR2 inhibits nuclear RelA/p65 NF-κB translocation and activity and reduces expression of dependent transcripts, including interleukin-6. These interactions result in diminished STAT3 phosphorylation and reduced breast cancer cell growth, motility, and invasiveness. FGFR2 also arrests the epithelial cell-to-mesenchymal cell transition (EMT), resulting in attenuated neoplastic growth in orthotopic xenografts of breast cancer cells. Our studies provide strong evidence for the protective effects of FGFR2 on tumor progression. We propose that FGFR2 serves as a scaffold for multiple components of the NF-κB signaling complex. Through these interactions, FGFR2 isoforms can respond to tissue-specific FGF signals to modulate epithelial cell-stromal cell communications in cancer progression.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".